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  • Efficient algorithm for creating an ideal distribution of groups into containers?

    - by Inshim
    I have groups of students that need to be allocated into classrooms of a fixed capacity (say, 100 chairs in each). Each group must only be allocated to a single classroom, even if it is larger than the capacity (ie there can be an overflow, with students standing up) I need an algorithm to make the allocations with minimum overflows and under-capacity classrooms. A naive algorithm to do this allocation is horrendously slow when having ~200 groups, with a distribution of about half of them being under 20% of the classroom size. Any ideas where I can find at least some good starting point for making this algorithm lightning fast? Thanks!

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  • integrity Constraints on a table.

    - by Dinesh
    See this sample schema Passenger(id PK, Name) Plane(id PK, capacity, type); Flight(id PK, planeId FK(Plane), flightDate, StartLocation, destination) CREATE TABLE Reservation(PassengerId, flightId, PRIMARY KEY (passengerId, flightId), FOREIGN KEY (passengerId) REFERENCES Passenger, FOREIGN KEY (flightId) REFERENCES Flight); I need to define an integrity constraint that enforces the restriction that the number of passengers on a plane cannot exceed the plane’s capacity. I have tried and achieved so far is this. CREATE TABLE Reservation( passengerId INTEGER, flightId INTEGER, PRIMARY KEY (passengerId, flightId), FOREIGN KEY (passengerId) REFERENCES Passenger, FOREIGN KEY (flightId) REFERENCES Flight, Constraint check1 check(Not Exists(select * from Flight s, (select count(*) as totalRes from Reservation group by flightId) t where t.totalRes > s.capacity ) ) ); I am not sure i am doing in right way or not. Any suggestions?

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  • Best Practices - updated: which domain types should be used to run applications

    - by jsavit
    This post is one of a series of "best practices" notes for Oracle VM Server for SPARC (formerly named Logical Domains). This is an updated and enlarged version of the post on this topic originally posted October 2012. One frequent question "what type of domain should I use to run applications?" There used to be a simple answer: "run applications in guest domains in almost all cases", but now there are more things to consider. Enhancements to Oracle VM Server for SPARC and introduction of systems like the current SPARC servers including the T4 and T5 systems, the Oracle SuperCluster T5-8 and Oracle SuperCluster M6-32 provide scale and performance much higher than the original servers that ran domains. Single-CPU performance, I/O capacity, memory sizes, are much larger now, and far more demanding applications are now being hosted in logical domains. The general advice continues to be "use guest domains in almost all cases", meaning, "use virtual I/O rather than physical I/O", unless there is a specific reason to use the other domain types. The sections below will discuss the criteria for choosing between domain types. Review: division of labor and types of domain Oracle VM Server for SPARC offloads management and I/O functionality from the hypervisor to domains (also called virtual machines), providing a modern alternative to older VM architectures that use a "thick", monolithic hypervisor. This permits a simpler hypervisor design, which enhances reliability, and security. It also reduces single points of failure by assigning responsibilities to multiple system components, further improving reliability and security. Oracle VM Server for SPARC defines the following types of domain, each with their own roles: Control domain - management control point for the server, runs the logical domain daemon and constraints engine, and is used to configure domains and manage resources. The control domain is the first domain to boot on a power-up, is always an I/O domain, and is usually a service domain as well. It doesn't have to be, but there's no reason to not leverage it for virtual I/O services. There is one control domain per T-series system, and one per Physical Domain (PDom) on an M5-32 or M6-32 system. M5 and M6 systems can be physically domained, with logical domains within the physical ones. I/O domain - a domain that has been assigned physical I/O devices. The devices may be one more more PCIe root complexes (in which case the domain is also called a root complex domain). The domain has native access to all the devices on the assigned PCIe buses. The devices can be any device type supported by Solaris on the hardware platform. a SR-IOV (Single-Root I/O Virtualization) function. SR-IOV lets a physical device (also called a physical function) or PF) be subdivided into multiple virtual functions (VFs) which can be individually assigned directly to domains. SR-IOV devices currently can be Ethernet or InfiniBand devices. direct I/O ownership of one or more PCI devices residing in a PCIe bus slot. The domain has direct access to the individual devices An I/O domain has native performance and functionality for the devices it owns, unmediated by any virtualization layer. It may also have virtual devices. Service domain - a domain that provides virtual network and disk devices to guest domains. The services are defined by commands that are run in the control domain. It usually is an I/O domain as well, in order for it to have devices to virtualize and serve out. Guest domain - a domain whose devices are all virtual rather than physical: virtual network and disk devices provided by one or more service domains. In common practice, this is where applications are run. Device considerations Consider the following when choosing between virtual devices and physical devices: Virtual devices provide the best flexibility - they can be dynamically added to and removed from a running domain, and you can have a large number of them up to a per-domain device limit. Virtual devices are compatible with live migration - domains that exclusively have virtual devices can be live migrated between servers supporting domains. On the other hand: Physical devices provide the best performance - in fact, native "bare metal" performance. Virtual devices approach physical device throughput and latency, especially with virtual network devices that can now saturate 10GbE links, but physical devices are still faster. Physical I/O devices do not add load to service domains - all the I/O goes directly from the I/O domain to the device, while virtual I/O goes through service domains, which must be provided sufficient CPU and memory capacity. Physical I/O devices can be other than network and disk - we virtualize network, disk, and serial console, but physical devices can be the wide range of attachable certified devices, including things like tape and CDROM/DVD devices. In some cases the lines are now blurred: virtual devices have better performance than previously: starting with Oracle VM Server for SPARC 3.1 there is near-native virtual network performance. There is more flexibility with physical devices than before: SR-IOV devices can now be dynamically reconfigured on domains. Tradeoffs one used to have to make are now relaxed: you can often have the flexibility of virtual I/O with performance that previously required physical I/O. You can have the performance and isolation of SR-IOV with the ability to dynamically reconfigure it, just like with virtual devices. Typical deployment A service domain is generally also an I/O domain: otherwise it wouldn't have access to physical device "backends" to offer to its clients. Similarly, an I/O domain is also typically a service domain in order to leverage the available PCI buses. Control domains must be I/O domains, because they boot up first on the server and require physical I/O. It's typical for the control domain to also be a service domain too so it doesn't "waste" the I/O resources it uses. A simple configuration consists of a control domain that is also the one I/O and service domain, and some number of guest domains using virtual I/O. In production, customers typically use multiple domains with I/O and service roles to eliminate single points of failure, as described in Availability Best Practices - Avoiding Single Points of Failure . Guest domains have virtual disk and virtual devices provisioned from more than one service domain, so failure of a service domain or I/O path or device does not result in an application outage. This also permits "rolling upgrades" in which service domains are upgraded one at a time while their guests continue to operate without disruption. (It should be noted that resiliency to I/O device failures can also be provided by the single control domain, using multi-path I/O) In this type of deployment, control, I/O, and service domains are used for virtualization infrastructure, while applications run in guest domains. Changing application deployment patterns The above model has been widely and successfully used, but more configuration options are available now. Servers got bigger than the original T2000 class machines with 2 I/O buses, so there is more I/O capacity that can be used for applications. Increased server capacity made it attractive to run more vertically-scaled applications, such as databases, with higher resource requirements than the "light" applications originally seen. This made it attractive to run applications in I/O domains so they could get bare-metal native I/O performance. This is leveraged by the Oracle SuperCluster engineered systems mentioned previously. In those engineered systems, I/O domains are used for high performance applications with native I/O performance for disk and network and optimized access to the Infiniband fabric. Another technical enhancement is Single Root I/O Virtualization (SR-IOV), which make it possible to give domains direct connections and native I/O performance for selected I/O devices. Not all I/O domains own PCI complexes, and there are increasingly more I/O domains that are not service domains. They use their I/O connectivity for performance for their own applications. However, there are some limitations and considerations: at this time, a domain using physical I/O cannot be live-migrated to another server. There is also a need to plan for security and introducing unneeded dependencies: if an I/O domain is also a service domain providing virtual I/O to guests, it has the ability to affect the correct operation of its client guest domains. This is even more relevant for the control domain. where the ldm command must be protected from unauthorized (or even mistaken) use that would affect other domains. As a general rule, running applications in the service domain or the control domain should be avoided. For reference, an excellent guide to secure deployment of domains by Stefan Hinker is at Secure Deployment of Oracle VM Server for SPARC. To recap: Guest domains with virtual I/O still provide the greatest operational flexibility, including features like live migration. They should be considered the default domain type to use unless there is a specific requirement that mandates an I/O domain. I/O domains can be used for applications with the highest performance requirements. Single Root I/O Virtualization (SR-IOV) makes this more attractive by giving direct I/O access to more domains, and by permitting dynamic reconfiguration of SR-IOV devices. Today's larger systems provide multiple PCIe buses - for example, 16 buses on the T5-8 - making it possible to configure multiple I/O domains each owning their own bus. Service domains should in general not be used for applications, because compromised security in the domain, or an outage, can affect domains that depend on it. This concern can be mitigated by providing guests' their virtual I/O from more than one service domain, so interruption of service in one service domain does not cause an application outage. The control domain should in general not be used to run applications, for the same reason. Oracle SuperCluster uses the control domain for applications, but it is an exception. It's not a general purpose environment; it's an engineered system with specifically configured applications and optimization for optimal performance. These are recommended "best practices" based on conversations with a number of Oracle architects. Keep in mind that "one size does not fit all", so you should evaluate these practices in the context of your own requirements. Summary Higher capacity servers that run Oracle VM Server for SPARC are attractive for applications with the most demanding resource requirements. New deployment models permit native I/O performance for demanding applications by running them in I/O domains with direct access to their devices. This is leveraged in SPARC SuperCluster, and can be leveraged in T-series servers to provision high-performance applications running in domains. Carefully planned, this can be used to provide peak performance for critical applications. That said, the improved virtual device performance in Oracle VM Server means that the default choice should still be guest domains with virtual I/O.

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  • Understanding G1 GC Logs

    - by poonam
    The purpose of this post is to explain the meaning of GC logs generated with some tracing and diagnostic options for G1 GC. We will take a look at the output generated with PrintGCDetails which is a product flag and provides the most detailed level of information. Along with that, we will also look at the output of two diagnostic flags that get enabled with -XX:+UnlockDiagnosticVMOptions option - G1PrintRegionLivenessInfo that prints the occupancy and the amount of space used by live objects in each region at the end of the marking cycle and G1PrintHeapRegions that provides detailed information on the heap regions being allocated and reclaimed. We will be looking at the logs generated with JDK 1.7.0_04 using these options. Option -XX:+PrintGCDetails Here's a sample log of G1 collection generated with PrintGCDetails. 0.522: [GC pause (young), 0.15877971 secs] [Parallel Time: 157.1 ms] [GC Worker Start (ms): 522.1 522.2 522.2 522.2 Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] [Processed Buffers : 2 2 3 2 Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] [GC Worker Other (ms): 0.3 0.3 0.3 0.3 Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] [Clear CT: 0.1 ms] [Other: 1.5 ms] [Choose CSet: 0.0 ms] [Ref Proc: 0.3 ms] [Ref Enq: 0.0 ms] [Free CSet: 0.3 ms] [Eden: 12M(12M)->0B(10M) Survivors: 0B->2048K Heap: 13M(64M)->9739K(64M)] [Times: user=0.59 sys=0.02, real=0.16 secs] This is the typical log of an Evacuation Pause (G1 collection) in which live objects are copied from one set of regions (young OR young+old) to another set. It is a stop-the-world activity and all the application threads are stopped at a safepoint during this time. This pause is made up of several sub-tasks indicated by the indentation in the log entries. Here's is the top most line that gets printed for the Evacuation Pause. 0.522: [GC pause (young), 0.15877971 secs] This is the highest level information telling us that it is an Evacuation Pause that started at 0.522 secs from the start of the process, in which all the regions being evacuated are Young i.e. Eden and Survivor regions. This collection took 0.15877971 secs to finish. Evacuation Pauses can be mixed as well. In which case the set of regions selected include all of the young regions as well as some old regions. 1.730: [GC pause (mixed), 0.32714353 secs] Let's take a look at all the sub-tasks performed in this Evacuation Pause. [Parallel Time: 157.1 ms] Parallel Time is the total elapsed time spent by all the parallel GC worker threads. The following lines correspond to the parallel tasks performed by these worker threads in this total parallel time, which in this case is 157.1 ms. [GC Worker Start (ms): 522.1 522.2 522.2 522.2Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] The first line tells us the start time of each of the worker thread in milliseconds. The start times are ordered with respect to the worker thread ids – thread 0 started at 522.1ms and thread 1 started at 522.2ms from the start of the process. The second line tells the Avg, Min, Max and Diff of the start times of all of the worker threads. [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] This gives us the time spent by each worker thread scanning the roots (globals, registers, thread stacks and VM data structures). Here, thread 0 took 1.6ms to perform the root scanning task and thread 1 took 1.5 ms. The second line clearly shows the Avg, Min, Max and Diff of the times spent by all the worker threads. [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] Update RS gives us the time each thread spent in updating the Remembered Sets. Remembered Sets are the data structures that keep track of the references that point into a heap region. Mutator threads keep changing the object graph and thus the references that point into a particular region. We keep track of these changes in buffers called Update Buffers. The Update RS sub-task processes the update buffers that were not able to be processed concurrently, and updates the corresponding remembered sets of all regions. [Processed Buffers : 2 2 3 2Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] This tells us the number of Update Buffers (mentioned above) processed by each worker thread. [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] These are the times each worker thread had spent in scanning the Remembered Sets. Remembered Set of a region contains cards that correspond to the references pointing into that region. This phase scans those cards looking for the references pointing into all the regions of the collection set. [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] These are the times spent by each worker thread copying live objects from the regions in the Collection Set to the other regions. [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] Termination time is the time spent by the worker thread offering to terminate. But before terminating, it checks the work queues of other threads and if there are still object references in other work queues, it tries to steal object references, and if it succeeds in stealing a reference, it processes that and offers to terminate again. [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] This gives the number of times each thread has offered to terminate. [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] These are the times in milliseconds at which each worker thread stopped. [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] These are the total lifetimes of each worker thread. [GC Worker Other (ms): 0.3 0.3 0.3 0.3Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] These are the times that each worker thread spent in performing some other tasks that we have not accounted above for the total Parallel Time. [Clear CT: 0.1 ms] This is the time spent in clearing the Card Table. This task is performed in serial mode. [Other: 1.5 ms] Time spent in the some other tasks listed below. The following sub-tasks (which individually may be parallelized) are performed serially. [Choose CSet: 0.0 ms] Time spent in selecting the regions for the Collection Set. [Ref Proc: 0.3 ms] Total time spent in processing Reference objects. [Ref Enq: 0.0 ms] Time spent in enqueuing references to the ReferenceQueues. [Free CSet: 0.3 ms] Time spent in freeing the collection set data structure. [Eden: 12M(12M)->0B(13M) Survivors: 0B->2048K Heap: 14M(64M)->9739K(64M)] This line gives the details on the heap size changes with the Evacuation Pause. This shows that Eden had the occupancy of 12M and its capacity was also 12M before the collection. After the collection, its occupancy got reduced to 0 since everything is evacuated/promoted from Eden during a collection, and its target size grew to 13M. The new Eden capacity of 13M is not reserved at this point. This value is the target size of the Eden. Regions are added to Eden as the demand is made and when the added regions reach to the target size, we start the next collection. Similarly, Survivors had the occupancy of 0 bytes and it grew to 2048K after the collection. The total heap occupancy and capacity was 14M and 64M receptively before the collection and it became 9739K and 64M after the collection. Apart from the evacuation pauses, G1 also performs concurrent-marking to build the live data information of regions. 1.416: [GC pause (young) (initial-mark), 0.62417980 secs] ….... 2.042: [GC concurrent-root-region-scan-start] 2.067: [GC concurrent-root-region-scan-end, 0.0251507] 2.068: [GC concurrent-mark-start] 3.198: [GC concurrent-mark-reset-for-overflow] 4.053: [GC concurrent-mark-end, 1.9849672 sec] 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.090: [GC concurrent-cleanup-start] 4.091: [GC concurrent-cleanup-end, 0.0002721] The first phase of a marking cycle is Initial Marking where all the objects directly reachable from the roots are marked and this phase is piggy-backed on a fully young Evacuation Pause. 2.042: [GC concurrent-root-region-scan-start] This marks the start of a concurrent phase that scans the set of root-regions which are directly reachable from the survivors of the initial marking phase. 2.067: [GC concurrent-root-region-scan-end, 0.0251507] End of the concurrent root region scan phase and it lasted for 0.0251507 seconds. 2.068: [GC concurrent-mark-start] Start of the concurrent marking at 2.068 secs from the start of the process. 3.198: [GC concurrent-mark-reset-for-overflow] This indicates that the global marking stack had became full and there was an overflow of the stack. Concurrent marking detected this overflow and had to reset the data structures to start the marking again. 4.053: [GC concurrent-mark-end, 1.9849672 sec] End of the concurrent marking phase and it lasted for 1.9849672 seconds. 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] This corresponds to the remark phase which is a stop-the-world phase. It completes the left over marking work (SATB buffers processing) from the previous phase. In this case, this phase took 0.0030184 secs and out of which 0.0000254 secs were spent on Reference processing. 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] Cleanup phase which is again a stop-the-world phase. It goes through the marking information of all the regions, computes the live data information of each region, resets the marking data structures and sorts the regions according to their gc-efficiency. In this example, the total heap size is 138M and after the live data counting it was found that the total live data size dropped down from 117M to 106M. 4.090: [GC concurrent-cleanup-start] This concurrent cleanup phase frees up the regions that were found to be empty (didn't contain any live data) during the previous stop-the-world phase. 4.091: [GC concurrent-cleanup-end, 0.0002721] Concurrent cleanup phase took 0.0002721 secs to free up the empty regions. Option -XX:G1PrintRegionLivenessInfo Now, let's look at the output generated with the flag G1PrintRegionLivenessInfo. This is a diagnostic option and gets enabled with -XX:+UnlockDiagnosticVMOptions. G1PrintRegionLivenessInfo prints the live data information of each region during the Cleanup phase of the concurrent-marking cycle. 26.896: [GC cleanup ### PHASE Post-Marking @ 26.896### HEAP committed: 0x02e00000-0x0fe00000 reserved: 0x02e00000-0x12e00000 region-size: 1048576 Cleanup phase of the concurrent-marking cycle started at 26.896 secs from the start of the process and this live data information is being printed after the marking phase. Committed G1 heap ranges from 0x02e00000 to 0x0fe00000 and the total G1 heap reserved by JVM is from 0x02e00000 to 0x12e00000. Each region in the G1 heap is of size 1048576 bytes. ### type address-range used prev-live next-live gc-eff### (bytes) (bytes) (bytes) (bytes/ms) This is the header of the output that tells us about the type of the region, address-range of the region, used space in the region, live bytes in the region with respect to the previous marking cycle, live bytes in the region with respect to the current marking cycle and the GC efficiency of that region. ### FREE 0x02e00000-0x02f00000 0 0 0 0.0 This is a Free region. ### OLD 0x02f00000-0x03000000 1048576 1038592 1038592 0.0 Old region with address-range from 0x02f00000 to 0x03000000. Total used space in the region is 1048576 bytes, live bytes as per the previous marking cycle are 1038592 and live bytes with respect to the current marking cycle are also 1038592. The GC efficiency has been computed as 0. ### EDEN 0x03400000-0x03500000 20992 20992 20992 0.0 This is an Eden region. ### HUMS 0x0ae00000-0x0af00000 1048576 1048576 1048576 0.0### HUMC 0x0af00000-0x0b000000 1048576 1048576 1048576 0.0### HUMC 0x0b000000-0x0b100000 1048576 1048576 1048576 0.0### HUMC 0x0b100000-0x0b200000 1048576 1048576 1048576 0.0### HUMC 0x0b200000-0x0b300000 1048576 1048576 1048576 0.0### HUMC 0x0b300000-0x0b400000 1048576 1048576 1048576 0.0### HUMC 0x0b400000-0x0b500000 1001480 1001480 1001480 0.0 These are the continuous set of regions called Humongous regions for storing a large object. HUMS (Humongous starts) marks the start of the set of humongous regions and HUMC (Humongous continues) tags the subsequent regions of the humongous regions set. ### SURV 0x09300000-0x09400000 16384 16384 16384 0.0 This is a Survivor region. ### SUMMARY capacity: 208.00 MB used: 150.16 MB / 72.19 % prev-live: 149.78 MB / 72.01 % next-live: 142.82 MB / 68.66 % At the end, a summary is printed listing the capacity, the used space and the change in the liveness after the completion of concurrent marking. In this case, G1 heap capacity is 208MB, total used space is 150.16MB which is 72.19% of the total heap size, live data in the previous marking was 149.78MB which was 72.01% of the total heap size and the live data as per the current marking is 142.82MB which is 68.66% of the total heap size. Option -XX:+G1PrintHeapRegions G1PrintHeapRegions option logs the regions related events when regions are committed, allocated into or are reclaimed. COMMIT/UNCOMMIT events G1HR COMMIT [0x6e900000,0x6ea00000]G1HR COMMIT [0x6ea00000,0x6eb00000] Here, the heap is being initialized or expanded and the region (with bottom: 0x6eb00000 and end: 0x6ec00000) is being freshly committed. COMMIT events are always generated in order i.e. the next COMMIT event will always be for the uncommitted region with the lowest address. G1HR UNCOMMIT [0x72700000,0x72800000]G1HR UNCOMMIT [0x72600000,0x72700000] Opposite to COMMIT. The heap got shrunk at the end of a Full GC and the regions are being uncommitted. Like COMMIT, UNCOMMIT events are also generated in order i.e. the next UNCOMMIT event will always be for the committed region with the highest address. GC Cycle events G1HR #StartGC 7G1HR CSET 0x6e900000G1HR REUSE 0x70500000G1HR ALLOC(Old) 0x6f800000G1HR RETIRE 0x6f800000 0x6f821b20G1HR #EndGC 7 This shows start and end of an Evacuation pause. This event is followed by a GC counter tracking both evacuation pauses and Full GCs. Here, this is the 7th GC since the start of the process. G1HR #StartFullGC 17G1HR UNCOMMIT [0x6ed00000,0x6ee00000]G1HR POST-COMPACTION(Old) 0x6e800000 0x6e854f58G1HR #EndFullGC 17 Shows start and end of a Full GC. This event is also followed by the same GC counter as above. This is the 17th GC since the start of the process. ALLOC events G1HR ALLOC(Eden) 0x6e800000 The region with bottom 0x6e800000 just started being used for allocation. In this case it is an Eden region and allocated into by a mutator thread. G1HR ALLOC(StartsH) 0x6ec00000 0x6ed00000G1HR ALLOC(ContinuesH) 0x6ed00000 0x6e000000 Regions being used for the allocation of Humongous object. The object spans over two regions. G1HR ALLOC(SingleH) 0x6f900000 0x6f9eb010 Single region being used for the allocation of Humongous object. G1HR COMMIT [0x6ee00000,0x6ef00000]G1HR COMMIT [0x6ef00000,0x6f000000]G1HR COMMIT [0x6f000000,0x6f100000]G1HR COMMIT [0x6f100000,0x6f200000]G1HR ALLOC(StartsH) 0x6ee00000 0x6ef00000G1HR ALLOC(ContinuesH) 0x6ef00000 0x6f000000G1HR ALLOC(ContinuesH) 0x6f000000 0x6f100000G1HR ALLOC(ContinuesH) 0x6f100000 0x6f102010 Here, Humongous object allocation request could not be satisfied by the free committed regions that existed in the heap, so the heap needed to be expanded. Thus new regions are committed and then allocated into for the Humongous object. G1HR ALLOC(Old) 0x6f800000 Old region started being used for allocation during GC. G1HR ALLOC(Survivor) 0x6fa00000 Region being used for copying old objects into during a GC. Note that Eden and Humongous ALLOC events are generated outside the GC boundaries and Old and Survivor ALLOC events are generated inside the GC boundaries. Other Events G1HR RETIRE 0x6e800000 0x6e87bd98 Retire and stop using the region having bottom 0x6e800000 and top 0x6e87bd98 for allocation. Note that most regions are full when they are retired and we omit those events to reduce the output volume. A region is retired when another region of the same type is allocated or we reach the start or end of a GC(depending on the region). So for Eden regions: For example: 1. ALLOC(Eden) Foo2. ALLOC(Eden) Bar3. StartGC At point 2, Foo has just been retired and it was full. At point 3, Bar was retired and it was full. If they were not full when they were retired, we will have a RETIRE event: 1. ALLOC(Eden) Foo2. RETIRE Foo top3. ALLOC(Eden) Bar4. StartGC G1HR CSET 0x6e900000 Region (bottom: 0x6e900000) is selected for the Collection Set. The region might have been selected for the collection set earlier (i.e. when it was allocated). However, we generate the CSET events for all regions in the CSet at the start of a GC to make sure there's no confusion about which regions are part of the CSet. G1HR POST-COMPACTION(Old) 0x6e800000 0x6e839858 POST-COMPACTION event is generated for each non-empty region in the heap after a full compaction. A full compaction moves objects around, so we don't know what the resulting shape of the heap is (which regions were written to, which were emptied, etc.). To deal with this, we generate a POST-COMPACTION event for each non-empty region with its type (old/humongous) and the heap boundaries. At this point we should only have Old and Humongous regions, as we have collapsed the young generation, so we should not have eden and survivors. POST-COMPACTION events are generated within the Full GC boundary. G1HR CLEANUP 0x6f400000G1HR CLEANUP 0x6f300000G1HR CLEANUP 0x6f200000 These regions were found empty after remark phase of Concurrent Marking and are reclaimed shortly afterwards. G1HR #StartGC 5G1HR CSET 0x6f400000G1HR CSET 0x6e900000G1HR REUSE 0x6f800000 At the end of a GC we retire the old region we are allocating into. Given that its not full, we will carry on allocating into it during the next GC. This is what REUSE means. In the above case 0x6f800000 should have been the last region with an ALLOC(Old) event during the previous GC and should have been retired before the end of the previous GC. G1HR ALLOC-FORCE(Eden) 0x6f800000 A specialization of ALLOC which indicates that we have reached the max desired number of the particular region type (in this case: Eden), but we decided to allocate one more. Currently it's only used for Eden regions when we extend the young generation because we cannot do a GC as the GC-Locker is active. G1HR EVAC-FAILURE 0x6f800000 During a GC, we have failed to evacuate an object from the given region as the heap is full and there is no space left to copy the object. This event is generated within GC boundaries and exactly once for each region from which we failed to evacuate objects. When Heap Regions are reclaimed ? It is also worth mentioning when the heap regions in the G1 heap are reclaimed. All regions that are in the CSet (the ones that appear in CSET events) are reclaimed at the end of a GC. The exception to that are regions with EVAC-FAILURE events. All regions with CLEANUP events are reclaimed. After a Full GC some regions get reclaimed (the ones from which we moved the objects out). But that is not shown explicitly, instead the non-empty regions that are left in the heap are printed out with the POST-COMPACTION events.

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  • Mysql performance problem & Failed DIMM

    - by murdoch
    Hi I have a dedicated mysql database server which has been having some performance problems recently, under normal load the server will be running fine, then suddenly out of the blue the performance will fall off a cliff. The server isn't using the swap file and there is 12GB of RAM in the server, more than enough for its needs. After contacting my hosting comapnies support they have discovered that there is a failed 2GB DIMM in the server and have scheduled to replace it tomorow morning. My question is could a failed DIMM result in the performance problems I am seeing or is this just coincidence? My worry is that they will replace the ram tomorrow but the problems will persist and I will still be lost of explanations so I am just trying to think ahead. The reason I ask is that there is plenty of RAM in the server, more than required and simply missing 2GB should be a problem, so if this failed DIMM is causing these performance problems then the OS must be trying to access the failed DIMM and slowing down as a result. Does that sound like a credible explanation? This is what DELLs omreport program says about the RAM, notice one dimm is "Critical" Memory Information Health : Critical Memory Operating Mode Fail Over State : Inactive Memory Operating Mode Configuration : Optimizer Attributes of Memory Array(s) Attributes : Location Memory Array 1 : System Board or Motherboard Attributes : Use Memory Array 1 : System Memory Attributes : Installed Capacity Memory Array 1 : 12288 MB Attributes : Maximum Capacity Memory Array 1 : 196608 MB Attributes : Slots Available Memory Array 1 : 18 Attributes : Slots Used Memory Array 1 : 6 Attributes : ECC Type Memory Array 1 : Multibit ECC Total of Memory Array(s) Attributes : Total Installed Capacity Value : 12288 MB Attributes : Total Installed Capacity Available to the OS Value : 12004 MB Attributes : Total Maximum Capacity Value : 196608 MB Details of Memory Array 1 Index : 0 Status : Ok Connector Name : DIMM_A1 Type : DDR3-Registered Size : 2048 MB Index : 1 Status : Ok Connector Name : DIMM_A2 Type : DDR3-Registered Size : 2048 MB Index : 2 Status : Ok Connector Name : DIMM_A3 Type : DDR3-Registered Size : 2048 MB Index : 3 Status : Critical Connector Name : DIMM_B1 Type : DDR3-Registered Size : 2048 MB Index : 4 Status : Ok Connector Name : DIMM_B2 Type : DDR3-Registered Size : 2048 MB Index : 5 Status : Ok Connector Name : DIMM_B3 Type : DDR3-Registered Size : 2048 MB the command free -m shows this, the server seems to be using more than 10GB of ram which would suggest it is trying to use the DIMM total used free shared buffers cached Mem: 12004 10766 1238 0 384 4809 -/+ buffers/cache: 5572 6432 Swap: 2047 0 2047 iostat output while problem is occuring avg-cpu: %user %nice %system %iowait %steal %idle 52.82 0.00 11.01 0.00 0.00 36.17 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 47.00 0.00 576.00 0 576 sda1 0.00 0.00 0.00 0 0 sda2 1.00 0.00 32.00 0 32 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 46.00 0.00 544.00 0 544 avg-cpu: %user %nice %system %iowait %steal %idle 53.12 0.00 7.81 0.00 0.00 39.06 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 49.00 0.00 592.00 0 592 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 49.00 0.00 592.00 0 592 avg-cpu: %user %nice %system %iowait %steal %idle 56.09 0.00 7.43 0.37 0.00 36.10 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 232.00 0.00 64520.00 0 64520 sda1 0.00 0.00 0.00 0 0 sda2 159.00 0.00 63728.00 0 63728 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 73.00 0.00 792.00 0 792 avg-cpu: %user %nice %system %iowait %steal %idle 52.18 0.00 9.24 0.06 0.00 38.51 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 49.00 0.00 600.00 0 600 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 49.00 0.00 600.00 0 600 avg-cpu: %user %nice %system %iowait %steal %idle 54.82 0.00 8.64 0.00 0.00 36.55 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 100.00 0.00 2168.00 0 2168 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 100.00 0.00 2168.00 0 2168 avg-cpu: %user %nice %system %iowait %steal %idle 54.78 0.00 6.75 0.00 0.00 38.48 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 84.00 0.00 896.00 0 896 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 84.00 0.00 896.00 0 896 avg-cpu: %user %nice %system %iowait %steal %idle 54.34 0.00 7.31 0.00 0.00 38.35 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 81.00 0.00 840.00 0 840 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 81.00 0.00 840.00 0 840 avg-cpu: %user %nice %system %iowait %steal %idle 55.18 0.00 5.81 0.44 0.00 38.58 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 317.00 0.00 105632.00 0 105632 sda1 0.00 0.00 0.00 0 0 sda2 224.00 0.00 104672.00 0 104672 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 93.00 0.00 960.00 0 960 avg-cpu: %user %nice %system %iowait %steal %idle 55.38 0.00 7.63 0.00 0.00 36.98 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 74.00 0.00 800.00 0 800 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 74.00 0.00 800.00 0 800 avg-cpu: %user %nice %system %iowait %steal %idle 56.43 0.00 7.80 0.00 0.00 35.77 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 72.00 0.00 784.00 0 784 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 72.00 0.00 784.00 0 784 avg-cpu: %user %nice %system %iowait %steal %idle 54.87 0.00 6.49 0.00 0.00 38.64 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 80.20 0.00 855.45 0 864 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 80.20 0.00 855.45 0 864 avg-cpu: %user %nice %system %iowait %steal %idle 57.22 0.00 5.69 0.00 0.00 37.09 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 33.00 0.00 432.00 0 432 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 33.00 0.00 432.00 0 432 avg-cpu: %user %nice %system %iowait %steal %idle 56.03 0.00 7.93 0.00 0.00 36.04 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 41.00 0.00 560.00 0 560 sda1 0.00 0.00 0.00 0 0 sda2 2.00 0.00 88.00 0 88 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 39.00 0.00 472.00 0 472 avg-cpu: %user %nice %system %iowait %steal %idle 55.78 0.00 5.13 0.00 0.00 39.09 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 29.00 0.00 392.00 0 392 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 29.00 0.00 392.00 0 392 avg-cpu: %user %nice %system %iowait %steal %idle 53.68 0.00 8.30 0.06 0.00 37.95 Device: tps Blk_read/s Blk_wrtn/s Blk_read Blk_wrtn sda 78.00 0.00 4280.00 0 4280 sda1 0.00 0.00 0.00 0 0 sda2 0.00 0.00 0.00 0 0 sda3 0.00 0.00 0.00 0 0 sda4 0.00 0.00 0.00 0 0 sda5 78.00 0.00 4280.00 0 4280

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  • Announcing: Oracle's Sun Flash Accelerator F80 PCIe Card

    - by uwes
    Ramp Up Your Server Performance with Oracle's Sun Flash Accelerator F80 PCIe Card! Oracle’s Sun Flash Accelerator F80 PCIe Card accelerates IO-starved applications and server performance by reducing storage latencies and increasing I/O throughput for greater productivity and business response! Sun Flash Accelerator F80 PCIe Card offers the following: Helps servers and their applications run faster and more efficient, while reducing power and space With 800GB capacity, delivers 2x the capacity of the previous F40 Flash Card for less than half the $/GB Accelerates I/O constrained databases with increased IOPS and consistent low-latency response timers Current and planned server support includes: The F80 is currently supported in Oracle’s SPARC T4-1, T4-2 and X4-2L servers.  SPARC T5, M5, M6 and Fujitsu M10 server support is planned for December 2013 (Preliminary only) Please read the Sales Bulletin on Oracle HW TRC for more details. (If you are not registered on Oracle HW TRC, click here ... and follow the instructions..) For More Information Go To: Oracle.com Flash Page Oracle Technology Network Flash Page

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  • Projected trajectory of a vehicle?

    - by mac
    In the game I am developing, I have to calculate if my vehicle (1) which in the example is travelling north with a speed V, can reach its target (2). The example depict the problem from atop: There are actually two possible scenarios: V is constant (resulting in trajectory 4, an arc of a circle) or the vehicle has the capacity to accelerate/decelerate (trajectory 3, an arc of a spiral). I would like to know if there is a straightforward way to verify if the vehicle is able to reach its target (as opposed to overshooting it). I'm particularly interested in trajectory #3, as I the only thing I could think of is integrating the position of the vehicle over time. EDIT: of course the vehicle has always the capacity to steer, but the steer radius vary with its speed (think to a maximum lateral g-force). EDIT2: also notice that (as most of the vehicles in real life) there is a minimum steering radius for the in-game ones too).

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  • Leveraging the Cloud to drive down costs and increase IT Agility

    The age of capital intensive IT is a thing of the past as scalability and pay-for-use will dominate in the new normal and as such, IT transformation is a necessity to make scalable what has traditionally been a largely fixed cost operation. IT functions can increase their agile capability most effectively by employing on-demand strategies that drive cost and capacity variability into their services rather than purely their technology. As companies move to the cloud they will also see an increase in their ability to accelerate time to market and capacity for innovation. Join us for this short, but informative interview with Tony Chauhan, Sr. Advisor with The Hackett Group as he provides his insights into effective cloud strategies.

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  • Is it feasible and useful to auto-generate some code of unit tests?

    - by skiwi
    Earlier today I have come up with an idea, based upon a particular real use case, which I would want to have checked for feasability and usefulness. This question will feature a fair chunk of Java code, but can be applied to all languages running inside a VM, and maybe even outside. While there is real code, it uses nothing language-specific, so please read it mostly as pseudo code. The idea Make unit testing less cumbersome by adding in some ways to autogenerate code based on human interaction with the codebase. I understand this goes against the principle of TDD, but I don't think anyone ever proved that doing TDD is better over first creating code and then immediatly therafter the tests. This may even be adapted to be fit into TDD, but that is not my current goal. To show how it is intended to be used, I'll copy one of my classes here, for which I need to make unit tests. public class PutMonsterOnFieldAction implements PlayerAction { private final int handCardIndex; private final int fieldMonsterIndex; public PutMonsterOnFieldAction(final int handCardIndex, final int fieldMonsterIndex) { this.handCardIndex = Arguments.requirePositiveOrZero(handCardIndex, "handCardIndex"); this.fieldMonsterIndex = Arguments.requirePositiveOrZero(fieldMonsterIndex, "fieldCardIndex"); } @Override public boolean isActionAllowed(final Player player) { Objects.requireNonNull(player, "player"); Hand hand = player.getHand(); Field field = player.getField(); if (handCardIndex >= hand.getCapacity()) { return false; } if (fieldMonsterIndex >= field.getMonsterCapacity()) { return false; } if (field.hasMonster(fieldMonsterIndex)) { return false; } if (!(hand.get(handCardIndex) instanceof MonsterCard)) { return false; } return true; } @Override public void performAction(final Player player) { Objects.requireNonNull(player); if (!isActionAllowed(player)) { throw new PlayerActionNotAllowedException(); } Hand hand = player.getHand(); Field field = player.getField(); field.setMonster(fieldMonsterIndex, (MonsterCard)hand.play(handCardIndex)); } } We can observe the need for the following tests: Constructor test with valid input Constructor test with invalid inputs isActionAllowed test with valid input isActionAllowed test with invalid inputs performAction test with valid input performAction test with invalid inputs My idea mainly focuses on the isActionAllowed test with invalid inputs. Writing these tests is not fun, you need to ensure a number of conditions and you check whether it really returns false, this can be extended to performAction, where an exception needs to be thrown in that case. The goal of my idea is to generate those tests, by indicating (through GUI of IDE hopefully) that you want to generate tests based on a specific branch. The implementation by example User clicks on "Generate code for branch if (handCardIndex >= hand.getCapacity())". Now the tool needs to find a case where that holds. (I haven't added the relevant code as that may clutter the post ultimately) To invalidate the branch, the tool needs to find a handCardIndex and hand.getCapacity() such that the condition >= holds. It needs to construct a Player with a Hand that has a capacity of at least 1. It notices that the capacity private int of Hand needs to be at least 1. It searches for ways to set it to 1. Fortunately it finds a constructor that takes the capacity as an argument. It uses 1 for this. Some more work needs to be done to succesfully construct a Player instance, involving the creation of objects that have constraints that can be seen by inspecting the source code. It has found the hand with the least capacity possible and is able to construct it. Now to invalidate the test it will need to set handCardIndex = 1. It constructs the test and asserts it to be false (the returned value of the branch) What does the tool need to work? In order to function properly, it will need the ability to scan through all source code (including JDK code) to figure out all constraints. Optionally this could be done through the javadoc, but that is not always used to indicate all constraints. It could also do some trial and error, but it pretty much stops if you cannot attach source code to compiled classes. Then it needs some basic knowledge of what the primitive types are, including arrays. And it needs to be able to construct some form of "modification trees". The tool knows that it needs to change a certain variable to a different value in order to get the correct testcase. Hence it will need to list all possible ways to change it, without using reflection obviously. What this tool will not replace is the need to create tailored unit tests that tests all kinds of conditions when a certain method actually works. It is purely to be used to test methods when they invalidate constraints. My questions: Is creating such a tool feasible? Would it ever work, or are there some obvious problems? Would such a tool be useful? Is it even useful to automatically generate these testcases at all? Could it be extended to do even more useful things? Does, by chance, such a project already exist and would I be reinventing the wheel? If not proven useful, but still possible to make such thing, I will still consider it for fun. If it's considered useful, then I might make an open source project for it depending on the time. For people searching more background information about the used Player and Hand classes in my example, please refer to this repository. At the time of writing the PutMonsterOnFieldAction has not been uploaded to the repo yet, but this will be done once I'm done with the unit tests.

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  • Data Source Security Part 4

    - by Steve Felts
    So far, I have covered Client Identity and Oracle Proxy Session features, with WLS or database credentials.  This article will cover one more feature, Identify-based pooling.  Then, there is one more topic to cover - how these options play with transactions.Identity-based Connection Pooling An identity based pool creates a heterogeneous pool of connections.  This allows applications to use a JDBC connection with a specific DBMS credential by pooling physical connections with different DBMS credentials.  The DBMS credential is based on either the WebLogic user mapped to a database user or the database user directly, based on the “use database credentials” setting as described earlier. Using this feature enabled with “use database credentials” enabled seems to be what is proposed in the JDBC standard, basically a heterogeneous pool with users specified by getConnection(user, password). The allocation of connections is more complex if Enable Identity Based Connection Pooling attribute is enabled on the data source.  When an application requests a database connection, the WebLogic Server instance selects an existing physical connection or creates a new physical connection with requested DBMS identity. The following section provides information on how heterogeneous connections are created:1. At connection pool initialization, the physical JDBC connections based on the configured or default “initial capacity” are created with the configured default DBMS credential of the data source.2. An application tries to get a connection from a data source.3a. If “use database credentials” is not enabled, the user specified in getConnection is mapped to a DBMS credential, as described earlier.  If the credential map doesn’t have a matching user, the default DBMS credential is used from the datasource descriptor.3b. If “use database credentials” is enabled, the user and password specified in getConnection are used directly.4. The connection pool is searched for a connection with a matching DBMS credential.5. If a match is found, the connection is reserved and returned to the application.6. If no match is found, a connection is created or reused based on the maximum capacity of the pool: - If the maximum capacity has not been reached, a new connection is created with the DBMS credential, reserved, and returned to the application.- If the pool has reached maximum capacity, based on the least recently used (LRU) algorithm, a physical connection is selected from the pool and destroyed. A new connection is created with the DBMS credential, reserved, and returned to the application. It should be clear that finding a matching connection is more expensive than a homogeneous pool.  Destroying a connection and getting a new one is very expensive.  If you can use a normal homogeneous pool or one of the light-weight options (client identity or an Oracle proxy connection), those should be used instead of identity based pooling. Regardless of how physical connections are created, each physical connection in the pool has its own DBMS credential information maintained by the pool. Once a physical connection is reserved by the pool, it does not change its DBMS credential even if the current thread changes its WebLogic user credential and continues to use the same connection. To configure this feature, select Enable Identity Based Connection Pooling.  See http://docs.oracle.com/cd/E24329_01/apirefs.1211/e24401/taskhelp/jdbc/jdbc_datasources/EnableIdentityBasedConnectionPooling.html  "Enable identity-based connection pooling for a JDBC data source" in Oracle WebLogic Server Administration Console Help. You must make the following changes to use Logging Last Resource (LLR) transaction optimization with Identity-based Pooling to get around the problem that multiple users will be accessing the associated transaction table.- You must configure a custom schema for LLR using a fully qualified LLR table name. All LLR connections will then use the named schema rather than the default schema when accessing the LLR transaction table.  - Use database specific administration tools to grant permission to access the named LLR table to all users that could access this table via a global transaction. By default, the LLR table is created during boot by the user configured for the connection in the data source. In most cases, the database will only allow access to this user and not allow access to mapped users. Connections within Transactions Now that we have covered the behavior of all of these various options, it’s time to discuss the exception to all of the rules.  When you get a connection within a transaction, it is associated with the transaction context on a particular WLS instance. When getting a connection with a data source configured with non-XA LLR or 1PC (using the JTS driver) with global transactions, the first connection obtained within the transaction is returned on subsequent connection requests regardless of the values of username/password specified and independent of the associated proxy user session, if any. The connection must be shared among all users of the connection when using LLR or 1PC. For XA data sources, the first connection obtained within the global transaction is returned on subsequent connection requests within the application server, regardless of the values of username/password specified and independent of the associated proxy user session, if any.  The connection must be shared among all users of the connection within a global transaction within the application server/JVM.

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  • What is the best private cloud storage setup

    - by vdrmrt
    I need to create a private cloud and I'm searching for the best setup. These are my 2 most important requirements 1. Disk and system redundant 2. Price / GB as low as possible The system is going to be used as backup setup which will receive data 24/7 over SFTP and rsync. High throughput is not that important. I'm planning to use glusterfs and consumer grade 4TB hard-drives. I have worked out 3 possible setups 3 servers with 11 4TB HDD Setup up a replica 3 glusterfs and setup each hard drive as a separate ext4 brick. Total capacity: 44TB HDD / TB ratio of 0.75 (33HDD / 44TB) 2 servers with 11 4TB HDD The 11 hard-drives are combined in a RAIDZ3 ZFS storage pool. With a replica 2 gluster setup. Total capacity: 32TB (+ zfs compression) HDD / TB ratio of 0.68 (22HDD / 32TB) 3 servers with 11 4TB consumer hard-drives Setup up a replica 3 glusterfs and setup each hard-drive as a separate zfs storage pool and export each pool as a brick. Total capacity: 32TB (+ zfs compression) HDD / TB ratio of 0.68 (22HDD / 32TB) (Cheapest) My remarks and concerns: If a hard drive fails which setup will recover the quickest? In my opinion setup 1 and 3 because there only the contents of 1 hard-drive needs to be copied over the network. Instead of setup 2 were the hard-drive needs te be reconstructed by reading the parity of all the other harddrives in the system. Will a zfs pool on 1 harddrive give me extra protection against for example bit rot? With setup 1 and 3 I can loose 2 systems and still be up and running with setup 2 I can only loose 1 system. When I use ZFS I can enable compression which will give me some extra storage.

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  • How to calibrate ASUS k52f battery on Ubuntu?

    - by cutalion
    I'm not sure if the problem is in software or my battery is dying, I'll move my question to another forum if it's not a SO question I have a problem with the battery on my laptop - ASUS K52F. It shows incorrect information about capacity. When I unplug the charger it can work some time, but then it will power off without any warnings. Sometimes it will power off right after I unplug the charger. Here is some info I could get: > uname -a Linux alligator 3.5.0-18-generic #29-Ubuntu SMP Fri Oct 19 10:26:51 UTC 2012 x86_64 x86_64 x86_64 GNU/Linux > acpi -i Battery 0: Charging, 99%, 18:25:15 until charged Battery 0: design capacity 5235 mAh, last full capacity 69964 mAh = 100% > cat /sys/class/power_supply/BAT0/uevent POWER_SUPPLY_NAME=BAT0 POWER_SUPPLY_STATUS=Charging POWER_SUPPLY_PRESENT=1 POWER_SUPPLY_TECHNOLOGY=Li-ion POWER_SUPPLY_CYCLE_COUNT=0 POWER_SUPPLY_VOLTAGE_MIN_DESIGN=10800000 POWER_SUPPLY_VOLTAGE_NOW=9246000 POWER_SUPPLY_POWER_NOW=176000 POWER_SUPPLY_ENERGY_FULL_DESIGN=**48400000** POWER_SUPPLY_ENERGY_FULL=**646822000** POWER_SUPPLY_ENERGY_NOW=**643588000** POWER_SUPPLY_MODEL_NAME=K52F-44 POWER_SUPPLY_MANUFACTURER=ASUSTek POWER_SUPPLY_SERIAL_NUMBER= I noticed, that POWER_SUPPLY_ENERGY_NOW and POWER_SUPPLY_ENERGY_FULL are greater than POWER_SUPPLY_ENERGY_FULL_DESIGN. I don't think it's ok :) I can run any additional commands.

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  • external drive and CentOS - Reset high speed USB device number

    - by Phil
    I have 2 external drives (3TB) and both will not work with my centOS Box. Tested them in windows ( different machine ) No problems ( 2.6.32-279.9.1.el6.i686 ) dmesg reports: usb 2-2: new high speed USB device number 3 using ehci_hcd usb 2-2: New USB device found, idVendor=2109, idProduct=0700 usb 2-2: New USB device strings: Mfr=1, Product=2, SerialNumber=3 usb 2-2: Product: USB 3.0 SATA Bridge usb 2-2: Manufacturer: VIA Labs, Inc. usb 2-2: SerialNumber: 0000000000006121 usb 2-2: configuration #1 chosen from 1 choice scsi6 : SCSI emulation for USB Mass Storage devices usb-storage: device found at 3 usb-storage: waiting for device to settle before scanning usb-storage: device scan complete scsi 6:0:0:0: Direct-Access ST3000DM 001-9YN166 CC4B PQ: 0 ANSI: 2 sd 6:0:0:0: Attached scsi generic sg3 type 0 sd 6:0:0:0: [sdd] Very big device. Trying to use READ CAPACITY(16). sd 6:0:0:0: [sdd] 5860533165 512-byte logical blocks: (3.00 TB/2.72 TiB) sd 6:0:0:0: [sdd] Write Protect is off sd 6:0:0:0: [sdd] Mode Sense: 00 06 00 00 sd 6:0:0:0: [sdd] Assuming drive cache: write through sd 6:0:0:0: [sdd] Very big device. Trying to use READ CAPACITY(16). sd 6:0:0:0: [sdd] Assuming drive cache: write through sdd: sdd1 sd 6:0:0:0: [sdd] Very big device. Trying to use READ CAPACITY(16). sd 6:0:0:0: [sdd] Assuming drive cache: write through sd 6:0:0:0: [sdd] Attached SCSI disk Tyring to use cfdisk / fdisk / gdisk or even fdisk -l results in the program hanging and dmesg reports: usb 2-2: reset high speed USB device number 3 using ehci_hcd usb 2-2: reset high speed USB device number 3 using ehci_hcd usb 2-2: reset high speed USB device number 3 using ehci_hcd I have the same 2 drives physically installed in the computer via SATA Any Ideas?

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  • How to boost playback volume in real time on media recorded with a very low volume.

    - by L Marksman
    I have never heard a satisfactory answer to this often misunderstood question, let me explain. Lets say I have a sound card and earphones/speakers that can play back audio loud enough in most cases. This is great but the problem is that you always find people who do not know how to record audio, from Youtube video's to music. So now you end up with a audio playback that only uses 10% or less of the capacity of your sound hardware, in vista/win 7 you will see this frequently in the mixer with the volume pushed up to max but the green sound level only goes up a millimeter or two. I am looking for (preferably free) software or a method to boost the sound level of any audio from any source in real time to use more of my hardware capacity similar to what VLC media player can do. Oh and please, do not tell me it is impossible. I am not trying to boost the volume past what my hardware is capable of, I am just trying to use my hardware's full capacity. Also please do not tell met to buy new hardware, I know I can use hardware amplification, I don't want to (like many others) spend money on a simple little problem like this. Thanks!

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  • Why is shrink_to_fit non-binding?

    - by Roger Pate
    The C++0x FCD states in 23.3.6.2 vector capacity: void shrink_to_fit(); Remarks: shrink_to_fit is a non-binding request to reduce capacity() to size(). [Note: The request is non-binding to allow latitude for implementation-specific optimizations. —end note] Why is it non-binding, and what optimizations are intended to be allowed?

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  • Eta/Eta-squared routines in R

    - by aL3xa
    Apart from graphical estimation of linearity (gaze-at-scatterplot method), which is utilized before applying some technique from GLM family, there are several ways to do this estimation arithmetically (i.e. without graphs). Right now, I'll focus on Fisher's eta-squared - correlation ratio: arithmetically, it's equal to squared Pearson's r (coef. of determination: R2) if relationship between two variables is linear. Hence, you can compare values of eta and r and make an assessment about type of relation (linear or not). It provides an information about percent of variance in the dependent variable explained (linearly or not) by the independent variable. Therefore, you can apply it when linearity assumptions are not met. Simply stated: is there a routine for eta/eta-squared in R?

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  • Convert NSData into Hex NSString

    - by Dawson
    With reference to the following question: Convert NSData into HEX NSSString I have solved the problem using the solution provided by Erik Aigner which is: NSData *data = ...; NSUInteger capacity = [data length] * 2; NSMutableString *stringBuffer = [NSMutableString stringWithCapacity:capacity]; const unsigned char *dataBuffer = [data bytes]; NSInteger i; for (i=0; i<[data length]; ++i) { [stringBuffer appendFormat:@"%02X", (NSUInteger)dataBuffer[i]]; } However, there is one small problem in that if there are extra zeros at the back, the string value would be different. For eg. if the hexa data is of a string @"3700000000000000", when converted using a scanner to integer: unsigned result = 0; NSScanner *scanner = [NSScanner scannerWithString:stringBuffer]; [scanner scanHexInt:&result]; NSLog(@"INTEGER: %u",result); The result would be 4294967295, which is incorrect. Shouldn't it be 55 as only the hexa 37 is taken? So how do I get rid of the zeros? EDIT: (In response to CRD) Hi, thanks for clarifying my doubts. So what you're doing is to actually read the 64-bit integer directly from a byte pointer right? However I have another question. How do you actually cast NSData to a byte pointer? To make it easier for you to understand, I'll explain what I did originally. Firstly, what I did was to display the data of the file which I have (data is in hexadecimal) NSData *file = [NSData dataWithContentsOfFile:@"file path here"]; NSLog(@"Patch File: %@",file); Output: Next, what I did was to read and offset the first 8 bytes of the file and convert them into a string. // 0-8 bytes [file seekToFileOffset:0]; NSData *b = [file readDataOfLength:8]; NSUInteger capacity = [b length] * 2; NSMutableString *stringBuffer = [NSMutableString stringWithCapacity:capacity]; const unsigned char *dataBuffer = [b bytes]; NSInteger i; for (i=0; i<[b length]; ++i) { [stringBuffer appendFormat:@"%02X", (NSUInteger)dataBuffer[i]]; } NSLog(@"0-8 bytes HEXADECIMAL: %@",stringBuffer); As you can see, 0x3700000000000000 is the next 8 bytes. The only changes I would have to make to access the next 8 bytes would be to change the value of SeekFileToOffset to 8, so as to access the next 8 bytes of data. All in all, the solution you gave me is useful, however it would not be practical to enter the hexadecimal values manually. If formatting the bytes as a string and then parsing them is not the way to do it, then how do I access the first 8 bytes of the data directly and cast them into a byte pointer?

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  • using dictionaries with WebServices

    - by umit-alba
    Hi! I tried to pass a dictionary via WebServices. However it is not serializeable. So i wrote an Own Class that makes it serializeable: using System; using System.Net; using System.Windows; using System.Collections.Generic; using System.Xml.Serialization; using System.Xml; using System.Xml.Schema; namespace Platform { public class SaDictionary<TKey, TValue> : Dictionary<TKey, TValue>, IXmlSerializable { #region Constructors public SaDictionary() : base() { } public SaDictionary(IDictionary<TKey, TValue> dictionary) : base(dictionary) { } public SaDictionary(IEqualityComparer<TKey> comparer) : base(comparer) { } public SaDictionary(int capacity) : base(capacity) { } public SaDictionary(IDictionary<TKey, TValue> dictionary, IEqualityComparer<TKey> comparer) : base(dictionary, comparer) { } public SaDictionary(int capacity, IEqualityComparer<TKey> comparer) : base(capacity, comparer) { } //protected SaDictionary(SerializationInfo info, StreamingContext context) // : base(info, context) //{ //} #endregion public XmlSchema GetSchema() { return null; } public void ReadXml(XmlReader reader) { XmlSerializer keySerializer = new XmlSerializer(typeof(TKey)); XmlSerializer valueSerializer = new XmlSerializer(typeof(TValue)); bool wasEmpty = reader.IsEmptyElement; reader.Read(); if (wasEmpty) return; while (reader.NodeType != XmlNodeType.EndElement) { reader.ReadStartElement("item"); reader.ReadStartElement("key"); TKey key = (TKey)keySerializer.Deserialize(reader); reader.ReadEndElement(); //key reader.ReadStartElement("value"); TValue value = (TValue)valueSerializer.Deserialize(reader); reader.ReadEndElement(); //value this.Add(key, value); reader.ReadEndElement(); //item // reader.MoveToContent(); } reader.ReadEndElement(); } public void WriteXml(XmlWriter writer) { XmlSerializer keySerializer = new XmlSerializer(typeof(TKey)); XmlSerializer valueSerializer = new XmlSerializer(typeof(TValue)); foreach (TKey key in this.Keys) { writer.WriteStartElement("item"); writer.WriteStartElement("key"); keySerializer.Serialize(writer, key); writer.WriteEndElement(); //key writer.WriteStartElement("value"); TValue value = this[key]; valueSerializer.Serialize(writer, value); writer.WriteEndElement(); //value writer.WriteEndElement(); //item } } } } However i get an ArrayOfXElement back. Is there a way to cast it back to a Dictionary? greets

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  • inline and member initializers

    - by Alexander
    When should I inline a member function and when should I use member initializers? My code is below.. I would like to modify it so I could make use some inline when appropriate and member initializers: #include "Books.h" Book::Book(){ nm = (char*)""; thck = 0; wght = 0; } Book::Book(const char *name, int thickness, int weight){ nm = strdup(name); thck = thickness; wght = weight; } Book::~Book(){ } const char* Book::name(){ return nm; } int Book::thickness(){ return thck; } int Book::weight(){ return wght; } // // Prints information about the book using this format: // "%s (%d mm, %d dg)\n" // void Book::print(){ printf("%s (%d mm, %d dg)\n", nm, thck, wght); } Bookcase::Bookcase(int id){ my_id = id; no_shelf = 0; } int Bookcase::id(){ return my_id; } Bookcase::~Bookcase(){ for (int i = 0; i < no_shelf; i++) delete my_shelf[i]; } bool Bookcase::addShelf(int width, int capacity){ if(no_shelf == 10) return false; else{ my_shelf[no_shelf] = new Shelf(width, capacity); no_shelf++; return true; } } bool Bookcase::add(Book *bp){ int index = -1; int temp_space = -1; for (int i = 0; i < no_shelf; i++){ if (bp->weight() + my_shelf[i]->curCapacity() <= my_shelf[i]->capacity()){ if (bp->thickness() + my_shelf[i]->curWidth() <= my_shelf[i]->width() && temp_space < (my_shelf[i]->width() - my_shelf[i]->curWidth())){ temp_space = (my_shelf[i]->width()- my_shelf[i]->curWidth()); index = i; } } } if (index != -1){ my_shelf[index]->add(bp); return true; }else return false; } void Bookcase::print(){ printf("Bookcase #%d\n", my_id); for (int i = 0; i < no_shelf; i++){ printf("--- Shelf (%d mm, %d dg) ---\n", my_shelf[i]->width(), my_shelf[i]->capacity()); my_shelf[i]->print(); } }

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  • Calling a Add-in function from Excel's VBA

    - by graham
    I am using an Excel Add-in for an Erlangs: http://abstractmicro.com/erlang/helppages/ref-erlbblockage.htm I try to call the Erlang-B function within the Add-in from within VBA thus: Function Erl(Erlangs As Double, Capacity As Double) Erl = Application.WorksheetFunction.ErlbBlockage(Capacity, Erlangs) End Function ...but it doesn't work. I get #VALUE! returned in the Excel cell. I think it is because the function is not part of standard Excel (it is in the Add-in). So how do I call it?

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  • The Benefits of Smart Grid Business Software

    - by Sylvie MacKenzie, PMP
    Smart Grid Background What Are Smart Grids?Smart Grids use computer hardware and software, sensors, controls, and telecommunications equipment and services to: Link customers to information that helps them manage consumption and use electricity wisely. Enable customers to respond to utility notices in ways that help minimize the duration of overloads, bottlenecks, and outages. Provide utilities with information that helps them improve performance and control costs. What Is Driving Smart Grid Development? Environmental ImpactSmart Grid development is picking up speed because of the widespread interest in reducing the negative impact that energy use has on the environment. Smart Grids use technology to drive efficiencies in transmission, distribution, and consumption. As a result, utilities can serve customers’ power needs with fewer generating plants, fewer transmission and distribution assets,and lower overall generation. With the possible exception of wind farm sprawl, landscape preservation is one obvious benefit. And because most generation today results in greenhouse gas emissions, Smart Grids reduce air pollution and the potential for global climate change.Smart Grids also more easily accommodate the technical difficulties of integrating intermittent renewable resources like wind and solar into the grid, providing further greenhouse gas reductions. CostsThe ability to defer the cost of plant and grid expansion is a major benefit to both utilities and customers. Utilities do not need to use as many internal resources for traditional infrastructure project planning and management. Large T&D infrastructure expansion costs are not passed on to customers.Smart Grids will not eliminate capital expansion, of course. Transmission corridors to connect renewable generation with customers will require major near-term expenditures. Additionally, in the future, electricity to satisfy the needs of population growth and additional applications will exceed the capacity reductions available through the Smart Grid. At that point, expansion will resume—but with greater overall T&D efficiency based on demand response, load control, and many other Smart Grid technologies and business processes. Energy efficiency is a second area of Smart Grid cost saving of particular relevance to customers. The timely and detailed information Smart Grids provide encourages customers to limit waste, adopt energy-efficient building codes and standards, and invest in energy efficient appliances. Efficiency may or may not lower customer bills because customer efficiency savings may be offset by higher costs in generation fuels or carbon taxes. It is clear, however, that bills will be lower with efficiency than without it. Utility Operations Smart Grids can serve as the central focus of utility initiatives to improve business processes. Many utilities have long “wish lists” of projects and applications they would like to fund in order to improve customer service or ease staff’s burden of repetitious work, but they have difficulty cost-justifying the changes, especially in the short term. Adding Smart Grid benefits to the cost/benefit analysis frequently tips the scales in favor of the change and can also significantly reduce payback periods.Mobile workforce applications and asset management applications work together to deploy assets and then to maintain, repair, and replace them. Many additional benefits result—for instance, increased productivity and fuel savings from better routing. Similarly, customer portals that provide customers with near-real-time information can also encourage online payments, thus lowering billing costs. Utilities can and should include these cost and service improvements in the list of Smart Grid benefits. What Is Smart Grid Business Software? Smart Grid business software gathers data from a Smart Grid and uses it improve a utility’s business processes. Smart Grid business software also helps utilities provide relevant information to customers who can then use it to reduce their own consumption and improve their environmental profiles. Smart Grid Business Software Minimizes the Impact of Peak Demand Utilities must size their assets to accommodate their highest peak demand. The higher the peak rises above base demand: The more assets a utility must build that are used only for brief periods—an inefficient use of capital. The higher the utility’s risk profile rises given the uncertainties surrounding the time needed for permitting, building, and recouping costs. The higher the costs for utilities to purchase supply, because generators can charge more for contracts and spot supply during high-demand periods. Smart Grids enable a variety of programs that reduce peak demand, including: Time-of-use pricing and critical peak pricing—programs that charge customers more when they consume electricity during peak periods. Pilot projects indicate that these programs are successful in flattening peaks, thus ensuring better use of existing T&D and generation assets. Direct load control, which lets utilities reduce or eliminate electricity flow to customer equipment (such as air conditioners). Contracts govern the terms and conditions of these turn-offs. Indirect load control, which signals customers to reduce the use of on-premises equipment for contractually agreed-on time periods. Smart Grid business software enables utilities to impose penalties on customers who do not comply with their contracts. Smart Grids also help utilities manage peaks with existing assets by enabling: Real-time asset monitoring and control. In this application, advanced sensors safely enable dynamic capacity load limits, ensuring that all grid assets can be used to their maximum capacity during peak demand periods. Real-time asset monitoring and control applications also detect the location of excessive losses and pinpoint need for mitigation and asset replacements. As a result, utilities reduce outage risk and guard against excess capacity or “over-build”. Better peak demand analysis. As a result: Distribution planners can better size equipment (e.g. transformers) to avoid over-building. Operations engineers can identify and resolve bottlenecks and other inefficiencies that may cause or exacerbate peaks. As above, the result is a reduction in the tendency to over-build. Supply managers can more closely match procurement with delivery. As a result, they can fine-tune supply portfolios, reducing the tendency to over-contract for peak supply and reducing the need to resort to spot market purchases during high peaks. Smart Grids can help lower the cost of remaining peaks by: Standardizing interconnections for new distributed resources (such as electricity storage devices). Placing the interconnections where needed to support anticipated grid congestion. Smart Grid Business Software Lowers the Cost of Field Services By processing Smart Grid data through their business software, utilities can reduce such field costs as: Vegetation management. Smart Grids can pinpoint momentary interruptions and tree-caused outages. Spatial mash-up tools leverage GIS models of tree growth for targeted vegetation management. This reduces the cost of unnecessary tree trimming. Service vehicle fuel. Many utility service calls are “false alarms.” Checking meter status before dispatching crews prevents many unnecessary “truck rolls.” Similarly, crews use far less fuel when Smart Grid sensors can pinpoint a problem and mobile workforce applications can then route them directly to it. Smart Grid Business Software Ensures Regulatory Compliance Smart Grids can ensure compliance with private contracts and with regional, national, or international requirements by: Monitoring fulfillment of contract terms. Utilities can use one-hour interval meters to ensure that interruptible (“non-core”) customers actually reduce or eliminate deliveries as required. They can use the information to levy fines against contract violators. Monitoring regulations imposed on customers, such as maximum use during specific time periods. Using accurate time-stamped event history derived from intelligent devices distributed throughout the smart grid to monitor and report reliability statistics and risk compliance. Automating business processes and activities that ensure compliance with security and reliability measures (e.g. NERC-CIP 2-9). Grid Business Software Strengthens Utilities’ Connection to Customers While Reducing Customer Service Costs During outages, Smart Grid business software can: Identify outages more quickly. Software uses sensors to pinpoint outages and nested outage locations. They also permit utilities to ensure outage resolution at every meter location. Size outages more accurately, permitting utilities to dispatch crews that have the skills needed, in appropriate numbers. Provide updates on outage location and expected duration. This information helps call centers inform customers about the timing of service restoration. Smart Grids also facilitates display of outage maps for customer and public-service use. Smart Grids can significantly reduce the cost to: Connect and disconnect customers. Meters capable of remote disconnect can virtually eliminate the costs of field crews and vehicles previously required to change service from the old to the new residents of a metered property or disconnect customers for nonpayment. Resolve reports of voltage fluctuation. Smart Grids gather and report voltage and power quality data from meters and grid sensors, enabling utilities to pinpoint reported problems or resolve them before customers complain. Detect and resolve non-technical losses (e.g. theft). Smart Grids can identify illegal attempts to reconnect meters or to use electricity in supposedly vacant premises. They can also detect theft by comparing flows through delivery assets with billed consumption. Smart Grids also facilitate outreach to customers. By monitoring and analyzing consumption over time, utilities can: Identify customers with unusually high usage and contact them before they receive a bill. They can also suggest conservation techniques that might help to limit consumption. This can head off “high bill” complaints to the contact center. Note that such “high usage” or “additional charges apply because you are out of range” notices—frequently via text messaging—are already common among mobile phone providers. Help customers identify appropriate bill payment alternatives (budget billing, prepayment, etc.). Help customers find and reduce causes of over-consumption. There’s no waiting for bills in the mail before they even understand there is a problem. Utilities benefit not just through improved customer relations but also through limiting the size of bills from customers who might struggle to pay them. Where permitted, Smart Grids can open the doors to such new utility service offerings as: Monitoring properties. Landlords reduce costs of vacant properties when utilities notify them of unexpected energy or water consumption. Utilities can perform similar services for owners of vacation properties or the adult children of aging parents. Monitoring equipment. Power-use patterns can reveal a need for equipment maintenance. Smart Grids permit utilities to alert owners or managers to a need for maintenance or replacement. Facilitating home and small-business networks. Smart Grids can provide a gateway to equipment networks that automate control or let owners access equipment remotely. They also facilitate net metering, offering some utilities a path toward involvement in small-scale solar or wind generation. Prepayment plans that do not need special meters. Smart Grid Business Software Helps Customers Control Energy Costs There is no end to the ways Smart Grids help both small and large customers control energy costs. For instance: Multi-premises customers appreciate having all meters read on the same day so that they can more easily compare consumption at various sites. Customers in competitive regions can match their consumption profile (detailed via Smart Grid data) with specific offerings from competitive suppliers. Customers seeing inexplicable consumption patterns and power quality problems may investigate further. The result can be discovery of electrical problems that can be resolved through rewiring or maintenance—before more serious fires or accidents happen. Smart Grid Business Software Facilitates Use of Renewables Generation from wind and solar resources is a popular alternative to fossil fuel generation, which emits greenhouse gases. Wind and solar generation may also increase energy security in regions that currently import fossil fuel for use in generation. Utilities face many technical issues as they attempt to integrate intermittent resource generation into traditional grids, which traditionally handle only fully dispatchable generation. Smart Grid business software helps solves many of these issues by: Detecting sudden drops in production from renewables-generated electricity (wind and solar) and automatically triggering electricity storage and smart appliance response to compensate as needed. Supporting industry-standard distributed generation interconnection processes to reduce interconnection costs and avoid adding renewable supplies to locations already subject to grid congestion. Facilitating modeling and monitoring of locally generated supply from renewables and thus helping to maximize their use. Increasing the efficiency of “net metering” (through which utilities can use electricity generated by customers) by: Providing data for analysis. Integrating the production and consumption aspects of customer accounts. During non-peak periods, such techniques enable utilities to increase the percent of renewable generation in their supply mix. During peak periods, Smart Grid business software controls circuit reconfiguration to maximize available capacity. Conclusion Utility missions are changing. Yesterday, they focused on delivery of reasonably priced energy and water. Tomorrow, their missions will expand to encompass sustainable use and environmental improvement.Smart Grids are key to helping utilities achieve this expanded mission. But they come at a relatively high price. Utilities will need to invest heavily in new hardware, software, business process development, and staff training. Customer investments in home area networks and smart appliances will be large. Learning to change the energy and water consumption habits of a lifetime could ultimately prove even more formidable tasks.Smart Grid business software can ease the cost and difficulties inherent in a needed transition to a more flexible, reliable, responsive electricity grid. Justifying its implementation, however, requires a full understanding of the benefits it brings—benefits that can ultimately help customers, utilities, communities, and the world address global issues like energy security and climate change while minimizing costs and maximizing customer convenience. This white paper is available for download here. For further information about Oracle's Primavera Solutions for Utilities, please read our Utilities e-book.

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  • How to implement a genetic algorithm with distance, time, and cost

    - by ari
    I want to make a solution to find the optimum route of school visit. For example, I want to visit 5 schools (A, B, C, D, E) in my city. Then I must find out what school I should visit first, then the second, then the third etc. with distance, time, and cost criteria. The problem is, I am confused about how to use distance with time and cost (fuel usage) estimation in genetic algorithm to find the optimum route?

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