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  • ArchBeat Link-o-Rama for 2012-04-12

    - by Bob Rhubart
    2012 Real World Performance Tour Dates |Performance Tuning | Performance Engineering www.ioug.org Coming to your town: a full day of real world database performance with Tom Kyte, Andrew Holdsworth, and Graham Wood. Rochester, NY - March 8 Los Angeles, CA - April 30 Orange County, CA - May 1 Redwood Shores, CA - May 3 Oracle Technology Network Developer Day: MySQL - New York www.oracle.com Wednesday, May 02, 2012 8:00 AM – 4:30 PM Grand Hyatt New York 109 East 42nd Street, Grand Central Terminal New York, NY 10017 Webcast Series: Data Warehousing Best Practices event.on24.com April 19, 2012 - Best Practices for Workload Management of a Data Warehouse on Oracle Exadata May 10, 2012 - Best Practices for Extreme Data Warehouse Performance on Oracle Exadata Webcast: Untangle Your Business with Oracle Unified SOA and Data Integration event.on24.com Date: Tuesday, April 24, 2012 Time: 10:00 AM PT / 1:00 PM ET Speakers: Mala Narasimharajan - Senior Product Marketing Manager, Oracle Data Integration, Oracle Bruce Tierney - Director of Product Marketing, Oracle SOA Suite, Oracle The Increasing Focus on Architecture (ArchBeat) blogs.oracle.com As a "third wave" of computing, Cloud computing is changing how IT organizations and individuals within those organizations approach the creation of solutions. Updated SOA Documents now available in ITSO Reference Library blogs.oracle.com Nine updated documents have just been added to the IT Strategies from Oracle library, including SOA Practitioner Guides, SOA Reference Architectures, and SOA White Papers and Data Sheets. Access to all documents within the ITSO library is free to those with a free Oracle.com membership. WebLogic JMS Clustering and Spring | Rene van Wijk middlewaremagic.com Oracle ACE Rene van Wijk sets up a WebLogic cluster that includes a JMS environment, which will be used by Spring. Running Built-In Test Simulator with SOA Suite Healthcare 11g in PS4 and PS5 | Shub Lahiri blogs.oracle.com Shub Lahiri shows how the pre-installed simulator that comes with the SOA Suite for Healthcare Integration pack can be used as an external endpoint to generate inbound and outbound HL7 traffic on specified MLLP ports. In the cloud era, let's start calling IT what it is: 'Innovation Team' | Joe McKendrick www.zdnet.com Cloud, the third great shift in 50 years of computing, presents a golden opportunity for IT to get out in front and lead. Thought for the Day "Why do we never have time to do it right, but always have time to do it over?" — Anonymous

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  • You may be tempted by IaaS, but you should PaaS on that or your database cloud journey will be a short one

    - by B R Clouse
    Before we examine Consolidation, the next step in the journey to cloud, let's take a short detour to address a critical choice you will face at the outset of your journey: whether to deploy your databases in virtual machines or not. A common misconception we've encountered is the belief that moving to cloud computing can be accomplished by simply hosting one's current operating environment as-is within virtual machines, and then stacking those VMs together in a consolidated environment.  This solution is often described as "Infrastructure as a Service" (IaaS) because the building block for deployments is a VM, which behaves like a full complement of infrastructure.  This approach is easy to understand and may feel like a good first step, but it won't take your databases very far in the journey to cloud computing.  In fact, if you follow the IaaS fork in the road, your journey will end quickly, without realizing the full benefits of cloud computing.  The better option to is to rationalize the deployment stack so that VMs are needed only for exceptional cases.  By settling on a standard operating system and patch level, you create an infrastructure that potentially all of your databases can share.  Now, the building block will be database instances or possibly schemas within databases.  These components are the platforms on which you will deploy workloads, hence this is known as "Platform as a Service" (PaaS). PaaS opens the door to higher degrees of consolidation than IaaS, because with PaaS you will not need to accommodate the footprint (operating system, hypervisor, processes, ...) that each VM brings with it.  You will also reduce your maintenance overheard if you move forward without the VMs and their O/Ses to patch and monitor.  So while IaaS simply shuffles complex and varied environments into VMs,  PaaS actually reduces complexity by rationalizing to the small possible set of components.  Now we're ready to look at the consolidation options that PaaS provides -- in our next blog posting.

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  • OpenJDK In The News: AMD and Oracle to Collaborate in the OpenJDK Community [..]

    - by $utils.escapeXML($entry.author)
    During the JavaOne™ 2012 Strategy Keynote, AMD (NYSE: AMD) announced its participation in OpenJDK™ Project “Sumatra” in collaboration with Oracle and other members of the OpenJDK community to help bring heterogeneous computing capabilities to Java™ for server and cloud environments. The OpenJDK Project “Sumatra” will explore how the Java Virtual Machine (JVM), as well as the Java language and APIs, might be enhanced to allow applications to take advantage of graphics processing unit (GPU) acceleration, either in discrete graphics cards or in high-performance graphics processor cores such as those found in AMD accelerated processing units (APUs).“Affirming our plans to contribute to the OpenJDK Project represents the next step towards bringing heterogeneous computing to millions of Java developers and can potentially lead to future developments of new hardware models, as well as server and cloud programming paradigms,” said Manju Hegde, corporate vice president, Heterogeneous Applications and Developer Solutions at AMD. “AMD has an established track record of collaboration with open-software development communities from OpenCL™ to the Heterogeneous System Architecture (HSA) Foundation, and with this initiative we will help further the development of graphics acceleration within the Java community.”“We expect our work with AMD and other OpenJDK participants in Project “Sumatra” will eventually help provide Java developers with the ability to quickly leverage GPU acceleration for better performance,” said Georges Saab, vice president, Software Development, Java Platform Group at Oracle. "We hope individuals and other organizations interested in this exciting development will follow AMD's lead by joining us in Project “Sumatra."Quotes taken from the first press release from AMD mentioning OpenJDK, titled "AMD and Oracle to Collaborate in the OpenJDK Community to Explore Heterogeneous Computing for Java ".

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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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  • Yammer, Berkeley DB, and the 3rd Platform

    - by Eric Jensen
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Cambria","serif"; mso-ascii-font-family:Cambria; mso-ascii-theme-font:major-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:major-fareast; mso-hansi-font-family:Cambria; mso-hansi-theme-font:major-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-bidi; mso-bidi-language:EN-US;} If you read the news, you know that the latest high-profile social media acquisition was just confirmed. Microsoft has agreed to acquire Yammer for 1.2 billion. Personally, I believe that Yammer’s amazing success can be mainly attributed to their wise decision to use Berkeley DB Java Edition as their backend data store. :-) I’m only kidding, of course. However, as Ryan Kennedy points out in the video I recently blogged about, BDB JE did provide the right feature set that allowed them to reliably grow their business. Which in turn allowed them to focus on their core value add. As it turns out, their ‘add’ is quite valuable! This actually makes sense to me, a lot more sense than certain other recent social acquisitions, and here’s why. Last year, IDC declared that we are entering a new computing era, the era of the “3rd Platform.” In case you’re curious, the first 2 were terminal computing and client/server computing, IIRC. Anyway, this 3rd one is more complicated. This year, IDC refined the concept further. It now involves 4 distinct buzzwords: cloud, social, mobile, and big data. Yammer is a social media platform that runs in the cloud, designed to be used from mobile devices. Their approach, using Berkeley DB Java Edition with High Availability, qualifies as big data. This means that Yammer is sitting right smack in the center if IDC’s new computing era. Another way to put it is: the folks at Yammer were prescient enough to predict where things were headed, and get there first. They chose Berkeley DB to handle their data. Maybe you should too!

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  • The Growing Importance of Network Virtualization

    - by user12608550
    The Growing Importance of Network Virtualization We often focus on server virtualization when we discuss cloud computing, but just as often we neglect to consider some of the critical implications of that technology. The ability to create virtual environments (or VEs [1]) means that we can create, destroy, activate and deactivate, and more importantly, MOVE them around within the cloud infrastructure. This elasticity and mobility has profound implications for how network services are defined, managed, and used to provide cloud services. It's not just servers that benefit from virtualization, it's the network as well. Network virtualization is becoming a hot topic, and not just for discussion but for companies like Oracle and others who have recently acquired net virtualization companies [2,3]. But even before this topic became so prominent, Solaris engineers were working on technologies in Solaris 11 to virtualize network services, known as Project Crossbow [4]. And why is network virtualization so important? Because old assumptions about network devices, topology, and management must be re-examined in light of the self-service, elasticity, and resource sharing requirements of cloud computing infrastructures. Static, hierarchical network designs, and inter-system traffic flows, need to be reconsidered and quite likely re-architected to take advantage of new features like virtual NICs and switches, bandwidth control, load balancing, and traffic isolation. For example, traditional multi-tier Web services (Web server, App server, DB server) that share net traffic over Ethernet wires can now be virtualized and hosted on shared-resource systems that communicate within a larger server at system bus speeds, increasing performance and reducing wired network traffic. And virtualized traffic flows can be monitored and adjusted as needed to optimize network performance for dynamically changing cloud workloads. Additionally, as VEs come and go and move around in the cloud, static network configuration methods cannot easily accommodate the routing and addressing flexibility that VE mobility implies; virtualizing the network itself is a requirement. Oracle Solaris 11 [5] includes key network virtualization technologies needed to implement cloud computing infrastructures. It includes features for the creation and management of virtual NICs and switches, and for the allocation and control of the traffic flows among VEs [6]. Additionally it allows for both sharing and dedication of hardware components to network tasks, such as allocating specific CPUs and vNICs to VEs, and even protocol-specific management of traffic. So, have a look at your current network topology and management practices in view of evolving cloud computing technologies. And don't simply duplicate the physical architecture of servers and connections in a virtualized environment…rethink the traffic flows among VEs and how they can be optimized using Oracle Solaris 11 and other Oracle products and services. [1] I use the term "virtual environment" or VE here instead of the more commonly used "virtual machine" or VM, because not all virtualized operating system environments are full OS kernels under the control of a hypervisor…in other words, not all VEs are VMs. In particular, VEs include Oracle Solaris zones, as well as SPARC VMs (previously called LDoms), and x86-based Solaris and Linux VMs running under hypervisors such as OEL, Xen, KVM, or VMware. [2] Oracle follows VMware into network virtualization space with Xsigo purchase; http://www.mercurynews.com/business/ci_21191001/oracle-follows-vmware-into-network-virtualization-space-xsigo [3] Oracle Buys Xsigo; http://www.oracle.com/us/corporate/press/1721421 [4] Oracle Solaris 11 Networking Virtualization Technology, http://www.oracle.com/technetwork/server-storage/solaris11/technologies/networkvirtualization-312278.html [5] Oracle Solaris 11; http://www.oracle.com/us/products/servers-storage/solaris/solaris11/overview/index.html [6] For example, the Solaris 11 'dladm' command can be used to limit the bandwidth of a virtual NIC, as follows: dladm create-vnic -l net0 -p maxbw=100M vnic0

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  • Cloud Adoption Challenges

    - by Herve Roggero
    Originally posted on: http://geekswithblogs.net/hroggero/archive/2013/11/07/cloud-adoption-challenges.aspxWhile cloud computing makes sense for most organizations and countless projects, I have seen customers significantly struggle with cloud adoption challenges. This blog post is not an attempt to provide a generic assessment of cloud adoption; rather it is an account of personal experiences in the field, some of which may or may not apply to your organization. Cloud First, Burst? In the rush to cloud adoption some companies have made the decision to redesign their core system with a cloud first approach. However a cloud first approach means that the system may not work anymore on-premises after it has been redesigned, specifically if the system depends on Platform as a Service (PaaS) components (such as Azure Tables). While PaaS makes sense when your company is in a position to adopt the cloud exclusively, it can be difficult to leverage with systems that need to work in different clouds or on-premises. As a result, some companies are starting to rethink their cloud strategy by designing for on-premises first, and modify only the necessary components to burst when needed in the cloud. This generally means that the components need to work equally well in any environment, which requires leveraging Infrastructure as a Service (IaaS) or additional investments for PaaS applications, or both.  What’s the Problem? Although most companies can benefit from cloud computing, not all of them can clearly identify a business reason for doing so other than in very generic terms. I heard many companies claim “it’s cheaper”, or “it allows us to scale”, without any specific metric or clear strategy behind the adoption decision. Other companies have a very clear strategy behind cloud adoption and can precisely articulate business benefits, such as “we have a 500% increase in traffic twice a year, so we need to burst in the cloud to avoid doubling our network and server capacity”. Understanding the problem being solved through by adopting cloud computing can significantly help organizations determine the optimum path and timeline to adoption. Performance or Scalability? I stopped counting the number of times I heard “the cloud doesn’t scale; our database runs faster on a laptop”.  While performance and scalability are related concepts, they are nonetheless different in nature. Performance is a measure of response time under a given load (meaning with a specific number of users), while scalability is the performance curve over various loads. For example one system could see great performance with 100 users, but timeout with 1,000 users, in which case the system wouldn’t scale. However another system could have average performance with 100 users, but display the exact same performance with 1,000,000 users, in which case the system would scale. Understanding that cloud computing does not usually provide high performance, but instead provides the tools necessary to build a scalable system (usually using PaaS services such as queuing and data federation), is fundamental to proper cloud adoption. Uptime? Last but not least, you may want to read the Service Level Agreement of your cloud provider in detail if you haven’t done so. If you are expecting 99.99% uptime annually you may be in for a surprise. Depending on the component being used, there may be no associated SLA at all! Other components may be restarted at any time, or services may experience failover conditions weekly ( or more) based on current overall conditions of the cloud service provider, most of which are outside of your control. As a result, for PaaS cloud environments (and to a certain extent some IaaS systems), applications need to assume failure and gracefully retry to be successful in the cloud in order to provide service continuity to end users. About Herve Roggero Herve Roggero, Windows Azure MVP, is the founder of Blue Syntax Consulting (http://www.bluesyntax.net). Herve's experience includes software development, architecture, database administration and senior management with both global corporations and startup companies. Herve holds multiple certifications, including an MCDBA, MCSE, MCSD. He also holds a Master's degree in Business Administration from Indiana University. Herve is the co-author of "PRO SQL Azure" and “PRO SQL Server 2012 Practices” from Apress, a PluralSight author, and runs the Azure Florida Association.

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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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  • generation of random numbers in java

    - by S.PRATHIBA
    Hi all, I want to create 30 tables which consists of the following fields.For example, Service_ID Service_Type consumer_feedback 75 Computing 1 35 Printer 0 33 Printer -1 3 rows in set (0.00 sec) mysql select * from consumer2; Service_ID Service_Type consumer_feedback 42 data 0 75 computing 0 mysql select * from consumer3; Service_ID Service_Type consumer_feedback 43 data -1 41 data 1 72 computing -1 As you can infer from the above tables, i am getting the feedback values.I have generated these consumer_feedback values,Service_ID,Service_Type using the concept of random numbers .I have used the funtion int min1=31;//printer int max1=35;//the values are generated if the Service_Type is printer. int provider1 = (int) (Math.random() * (max1 - min1 + 1) ) + min1; int min2=41;//data int max2 =45 int provider2 = (int) (Math.random() * (max2 - min2 + 1) ) + min2; int min3=71;//computing int max3=75; int provider3 = (int) (Math.random() * (max3 - min3 + 1) ) + min3; int min5 = -1;//feedback values int max5 =1; int feedback = (int) (Math.random() * (max5 - min5 + 1) ) + min5; I need the Service_Types to be distributed uniformly in all the 30 tables.Similarly I need feedback value of 1 to be generated many times other than 0 and -1.Please Help me.

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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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  • Scope of Mainframe Technologies Today?

    - by Vaibhav Bajpai
    I have been recently allocated to training in Mainframe Technologies at my company (where I am currently working as a Trainee). I am slated to learn DB2, JCL, CICS, and Cobol during the programme. I am from a C++ background, and curious how the community here feels of these technologies. I am also curious to know, how mainframe computers fit into today's computing scenario where distributed computing has taken over almost completely.

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