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  • "En 2020, 80% des applications tourneront dans le cloud", déclare Microsoft, en expliquant que les DSI devront s'adapter à ces changements

    "En 2020, 80% des applications tourneront dans le cloud", déclare Microsoft, tout en expliquant que les DSI devront s'adapter à ces changements Au premier jour des TechDays 2011, nous avons pu rencontrer Jérôme Trédan, Directeur produits serveurs et infrastructure de cloud computing chez Microsoft France. Sa mission : encadrer une équipe de chefs de produits sur toute les lignes de produits serveurs, dans 3 grands domaines : les infrastructures (Windows Server, System Center et toute l'offre de sécurité de la gamme Forefront) ; l'axe des plateformes applicatives de Microsoft (SQL Server, Biztalk, .Net) ; et la partie infrastructures cloud computing (qui se développe très rapidement avec Windows et SQL Azure).

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  • Global Knowledge présente ses nouvelles formations sur le Cloud pour développeurs, DSI, chefs de projets, consultants ou architectes réseaux

    Global Knowledge présente ses nouvelles formations sur le Cloud Pour développeurs, DSI, chefs de projets, consultants ou architectes réseaux Global Knowledge, le centre de formation continue en informatique et management, vient d'ajouter de nouveaux cours sur le Cloud Computing à son catalogue de formations. On y trouve d'une part, un cursus certifiant de 21 jours conçu en partenariat avec Centrale Paris. Et d'autre part, un catalogue complet et structuré allant des fondamentaux (comprendre le Cloud Computing), à la conception d'une architecture ou encore la mise en oeuvre des solutions techniques comme la virtualisation. Elles s'adressent donc à un public la...

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  • Extending Oracle CEP with Predictive Analytics

    - by vikram.shukla(at)oracle.com
    Introduction: OCEP is often used as a business rules engine to execute a set of business logic rules via CQL statements, and take decisions based on the outcome of those rules. There are times where configuring rules manually is sufficient because an application needs to deal with only a small and well-defined set of static rules. However, in many situations customers don't want to pre-define such rules for two reasons. First, they are dealing with events with lots of columns and manually crafting such rules for each column or a set of columns and combinations thereof is almost impossible. Second, they are content with probabilistic outcomes and do not care about 100% precision. The former is the case when a user is dealing with data with high dimensionality, the latter when an application can live with "false" positives as they can be discarded after further inspection, say by a Human Task component in a Business Process Management software. The primary goal of this blog post is to show how this can be achieved by combining OCEP with Oracle Data Mining® and leveraging the latter's rich set of algorithms and functionality to do predictive analytics in real time on streaming events. The secondary goal of this post is also to show how OCEP can be extended to invoke any arbitrary external computation in an RDBMS from within CEP. The extensible facility is known as the JDBC cartridge. The rest of the post describes the steps required to achieve this: We use the dataset available at http://blogs.oracle.com/datamining/2010/01/fraud_and_anomaly_detection_made_simple.html to showcase the capabilities. We use it to show how transaction anomalies or fraud can be detected. Building the model: Follow the self-explanatory steps described at the above URL to build the model.  It is very simple - it uses built-in Oracle Data Mining PL/SQL packages to cleanse, normalize and build the model out of the dataset.  You can also use graphical Oracle Data Miner®  to build the models. To summarize, it involves: Specifying which algorithms to use. In this case we use Support Vector Machines as we're trying to find anomalies in highly dimensional dataset.Build model on the data in the table for the algorithms specified. For this example, the table was populated in the scott/tiger schema with appropriate privileges. Configuring the Data Source: This is the first step in building CEP application using such an integration.  Our datasource looks as follows in the server config file.  It is advisable that you use the Visualizer to add it to the running server dynamically, rather than manually edit the file.    <data-source>         <name>DataMining</name>         <data-source-params>             <jndi-names>                 <element>DataMining</element>             </jndi-names>             <global-transactions-protocol>OnePhaseCommit</global-transactions-protocol>         </data-source-params>         <connection-pool-params>             <credential-mapping-enabled></credential-mapping-enabled>             <test-table-name>SQL SELECT 1 from DUAL</test-table-name>             <initial-capacity>1</initial-capacity>             <max-capacity>15</max-capacity>             <capacity-increment>1</capacity-increment>         </connection-pool-params>         <driver-params>             <use-xa-data-source-interface>true</use-xa-data-source-interface>             <driver-name>oracle.jdbc.OracleDriver</driver-name>             <url>jdbc:oracle:thin:@localhost:1522:orcl</url>             <properties>                 <element>                     <value>scott</value>                     <name>user</name>                 </element>                 <element>                     <value>{Salted-3DES}AzFE5dDbO2g=</value>                     <name>password</name>                 </element>                                 <element>                     <name>com.bea.core.datasource.serviceName</name>                     <value>oracle11.2g</value>                 </element>                 <element>                     <name>com.bea.core.datasource.serviceVersion</name>                     <value>11.2.0</value>                 </element>                 <element>                     <name>com.bea.core.datasource.serviceObjectClass</name>                     <value>java.sql.Driver</value>                 </element>             </properties>         </driver-params>     </data-source>   Designing the EPN: The EPN is very simple in this example. We briefly describe each of the components. The adapter ("DataMiningAdapter") reads data from a .csv file and sends it to the CQL processor downstream. The event payload here is same as that of the table in the database (refer to the attached project or do a "desc table-name" from a SQL*PLUS prompt). While this is for convenience in this example, it need not be the case. One can still omit fields in the streaming events, and need not match all columns in the table on which the model was built. Better yet, it does not even need to have the same name as columns in the table, as long as you alias them in the USING clause of the mining function. (Caveat: they still need to draw values from a similar universe or domain, otherwise it constitutes incorrect usage of the model). There are two things in the CQL processor ("DataMiningProc") that make scoring possible on streaming events. 1.      User defined cartridge function Please refer to the OCEP CQL reference manual to find more details about how to define such functions. We include the function below in its entirety for illustration. <?xml version="1.0" encoding="UTF-8"?> <jdbcctxconfig:config     xmlns:jdbcctxconfig="http://www.bea.com/ns/wlevs/config/application"     xmlns:jc="http://www.oracle.com/ns/ocep/config/jdbc">        <jc:jdbc-ctx>         <name>Oracle11gR2</name>         <data-source>DataMining</data-source>               <function name="prediction2">                                 <param name="CQLMONTH" type="char"/>                      <param name="WEEKOFMONTH" type="int"/>                      <param name="DAYOFWEEK" type="char" />                      <param name="MAKE" type="char" />                      <param name="ACCIDENTAREA"   type="char" />                      <param name="DAYOFWEEKCLAIMED"  type="char" />                      <param name="MONTHCLAIMED" type="char" />                      <param name="WEEKOFMONTHCLAIMED" type="int" />                      <param name="SEX" type="char" />                      <param name="MARITALSTATUS"   type="char" />                      <param name="AGE" type="int" />                      <param name="FAULT" type="char" />                      <param name="POLICYTYPE"   type="char" />                      <param name="VEHICLECATEGORY"  type="char" />                      <param name="VEHICLEPRICE" type="char" />                      <param name="FRAUDFOUND" type="int" />                      <param name="POLICYNUMBER" type="int" />                      <param name="REPNUMBER" type="int" />                      <param name="DEDUCTIBLE"   type="int" />                      <param name="DRIVERRATING"  type="int" />                      <param name="DAYSPOLICYACCIDENT"   type="char" />                      <param name="DAYSPOLICYCLAIM" type="char" />                      <param name="PASTNUMOFCLAIMS" type="char" />                      <param name="AGEOFVEHICLES" type="char" />                      <param name="AGEOFPOLICYHOLDER" type="char" />                      <param name="POLICEREPORTFILED" type="char" />                      <param name="WITNESSPRESNT" type="char" />                      <param name="AGENTTYPE" type="char" />                      <param name="NUMOFSUPP" type="char" />                      <param name="ADDRCHGCLAIM"   type="char" />                      <param name="NUMOFCARS" type="char" />                      <param name="CQLYEAR" type="int" />                      <param name="BASEPOLICY" type="char" />                                     <return-component-type>char</return-component-type>                                                      <sql><![CDATA[             SELECT to_char(PREDICTION_PROBABILITY(CLAIMSMODEL, '0' USING *))               AS probability             FROM (SELECT  :CQLMONTH AS MONTH,                                            :WEEKOFMONTH AS WEEKOFMONTH,                          :DAYOFWEEK AS DAYOFWEEK,                           :MAKE AS MAKE,                           :ACCIDENTAREA AS ACCIDENTAREA,                           :DAYOFWEEKCLAIMED AS DAYOFWEEKCLAIMED,                           :MONTHCLAIMED AS MONTHCLAIMED,                           :WEEKOFMONTHCLAIMED,                             :SEX AS SEX,                           :MARITALSTATUS AS MARITALSTATUS,                            :AGE AS AGE,                           :FAULT AS FAULT,                           :POLICYTYPE AS POLICYTYPE,                            :VEHICLECATEGORY AS VEHICLECATEGORY,                           :VEHICLEPRICE AS VEHICLEPRICE,                           :FRAUDFOUND AS FRAUDFOUND,                           :POLICYNUMBER AS POLICYNUMBER,                           :REPNUMBER AS REPNUMBER,                           :DEDUCTIBLE AS DEDUCTIBLE,                            :DRIVERRATING AS DRIVERRATING,                           :DAYSPOLICYACCIDENT AS DAYSPOLICYACCIDENT,                            :DAYSPOLICYCLAIM AS DAYSPOLICYCLAIM,                           :PASTNUMOFCLAIMS AS PASTNUMOFCLAIMS,                           :AGEOFVEHICLES AS AGEOFVEHICLES,                           :AGEOFPOLICYHOLDER AS AGEOFPOLICYHOLDER,                           :POLICEREPORTFILED AS POLICEREPORTFILED,                           :WITNESSPRESNT AS WITNESSPRESENT,                           :AGENTTYPE AS AGENTTYPE,                           :NUMOFSUPP AS NUMOFSUPP,                           :ADDRCHGCLAIM AS ADDRCHGCLAIM,                            :NUMOFCARS AS NUMOFCARS,                           :CQLYEAR AS YEAR,                           :BASEPOLICY AS BASEPOLICY                 FROM dual)                 ]]>         </sql>        </function>     </jc:jdbc-ctx> </jdbcctxconfig:config> 2.      Invoking the function for each event. Once this function is defined, you can invoke it from CQL as follows: <?xml version="1.0" encoding="UTF-8"?> <wlevs:config xmlns:wlevs="http://www.bea.com/ns/wlevs/config/application">   <processor>     <name>DataMiningProc</name>     <rules>        <query id="q1"><![CDATA[                     ISTREAM(SELECT S.CQLMONTH,                                   S.WEEKOFMONTH,                                   S.DAYOFWEEK, S.MAKE,                                   :                                         S.BASEPOLICY,                                    C.F AS probability                                                 FROM                                 StreamDataChannel [NOW] AS S,                                 TABLE(prediction2@Oracle11gR2(S.CQLMONTH,                                      S.WEEKOFMONTH,                                      S.DAYOFWEEK,                                       S.MAKE, ...,                                      S.BASEPOLICY) AS F of char) AS C)                       ]]></query>                 </rules>               </processor>           </wlevs:config>   Finally, the last stage in the EPN prints out the probability of the event being an anomaly. One can also define a threshold in CQL to filter out events that are normal, i.e., below a certain mark as defined by the analyst or designer. Sample Runs: Now let's see how this behaves when events are streamed through CEP. We use only two events for brevity, one normal and other one not. This is one of the "normal" looking events and the probability of it being anomalous is less than 60%. Event is: eventType=DataMiningOutEvent object=q1  time=2904821976256 S.CQLMONTH=Dec, S.WEEKOFMONTH=5, S.DAYOFWEEK=Wednesday, S.MAKE=Honda, S.ACCIDENTAREA=Urban, S.DAYOFWEEKCLAIMED=Tuesday, S.MONTHCLAIMED=Jan, S.WEEKOFMONTHCLAIMED=1, S.SEX=Female, S.MARITALSTATUS=Single, S.AGE=21, S.FAULT=Policy Holder, S.POLICYTYPE=Sport - Liability, S.VEHICLECATEGORY=Sport, S.VEHICLEPRICE=more than 69000, S.FRAUDFOUND=0, S.POLICYNUMBER=1, S.REPNUMBER=12, S.DEDUCTIBLE=300, S.DRIVERRATING=1, S.DAYSPOLICYACCIDENT=more than 30, S.DAYSPOLICYCLAIM=more than 30, S.PASTNUMOFCLAIMS=none, S.AGEOFVEHICLES=3 years, S.AGEOFPOLICYHOLDER=26 to 30, S.POLICEREPORTFILED=No, S.WITNESSPRESENT=No, S.AGENTTYPE=External, S.NUMOFSUPP=none, S.ADDRCHGCLAIM=1 year, S.NUMOFCARS=3 to 4, S.CQLYEAR=1994, S.BASEPOLICY=Liability, probability=.58931702982118561 isTotalOrderGuarantee=true\nAnamoly probability: .58931702982118561 However, the following event is scored as an anomaly with a very high probability of  89%. So there is likely to be something wrong with it. A close look reveals that the value of "deductible" field (10000) is not "normal". What exactly constitutes normal here?. If you run the query on the database to find ALL distinct values for the "deductible" field, it returns the following set: {300, 400, 500, 700} Event is: eventType=DataMiningOutEvent object=q1  time=2598483773496 S.CQLMONTH=Dec, S.WEEKOFMONTH=5, S.DAYOFWEEK=Wednesday, S.MAKE=Honda, S.ACCIDENTAREA=Urban, S.DAYOFWEEKCLAIMED=Tuesday, S.MONTHCLAIMED=Jan, S.WEEKOFMONTHCLAIMED=1, S.SEX=Female, S.MARITALSTATUS=Single, S.AGE=21, S.FAULT=Policy Holder, S.POLICYTYPE=Sport - Liability, S.VEHICLECATEGORY=Sport, S.VEHICLEPRICE=more than 69000, S.FRAUDFOUND=0, S.POLICYNUMBER=1, S.REPNUMBER=12, S.DEDUCTIBLE=10000, S.DRIVERRATING=1, S.DAYSPOLICYACCIDENT=more than 30, S.DAYSPOLICYCLAIM=more than 30, S.PASTNUMOFCLAIMS=none, S.AGEOFVEHICLES=3 years, S.AGEOFPOLICYHOLDER=26 to 30, S.POLICEREPORTFILED=No, S.WITNESSPRESENT=No, S.AGENTTYPE=External, S.NUMOFSUPP=none, S.ADDRCHGCLAIM=1 year, S.NUMOFCARS=3 to 4, S.CQLYEAR=1994, S.BASEPOLICY=Liability, probability=.89171554529576691 isTotalOrderGuarantee=true\nAnamoly probability: .89171554529576691 Conclusion: By way of this example, we show: real-time scoring of events as they flow through CEP leveraging Oracle Data Mining.how CEP applications can invoke complex arbitrary external computations (function shipping) in an RDBMS.

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  • IPgallery banks on Solaris SPARC

    - by Frederic Pariente
    IPgallery is a global supplier of converged legacy and Next Generation Networks (NGN) products and solutions, including: core network components and cloud-based Value Added Services (VAS) for voice, video and data sessions. IPgallery enables network operators and service providers to offer advanced converged voice, chat, video/content services and rich unified social communications in a combined legacy (fixed/mobile), Over-the-Top (OTT) and Social Community (SC) environments for home and business customers. Technically speaking, this offer is a scalable and robust telco solution enabling operators to offer new services while controlling operating expenses (OPEX). In its solutions, IPgallery leverages the following Oracle components: Oracle Solaris, Netra T4 and SPARC T4 in order to provide a competitive and scalable solution without the price tag often associated with high-end systems. Oracle Solaris Binary Application Guarantee A unique feature of Oracle Solaris is the guaranteed binary compatibility between releases of the Solaris OS. That means, if a binary application runs on Solaris 2.6 or later, it will run on the latest release of Oracle Solaris.  IPgallery developed their application on Solaris 9 and Solaris 10 then runs it on Solaris 11, without any code modification or rebuild. The Solaris Binary Application Guarantee helps IPgallery protect their long-term investment in the development, training and maintenance of their applications. Oracle Solaris Image Packaging System (IPS) IPS is a new repository-based package management system that comes with Oracle Solaris 11. It provides a framework for complete software life-cycle management such as installation, upgrade and removal of software packages. IPgallery leverages this new packaging system in order to speed up and simplify software installation for the R&D and production environments. Notably, they use IPS to deliver Solaris Studio 12.3 packages as part of the rapid installation process of R&D environments, and during the production software deployment phase, they ensure software package integrity using the built-in verification feature. Solaris IPS thus improves IPgallery's time-to-market with a faster, more reliable software installation and deployment in production environments. Extreme Network Performance IPgallery saw a huge improvement in application performance both in CPU and I/O, when running on SPARC T4 architecture in compared to UltraSPARC T2 servers.  The same application (with the same activation environment) running on T2 consumes 40%-50% CPU, while it consumes only 10% of the CPU on T4. The testing environment comprised of: Softswitch (Call management), TappS (Telecom Application Server) and Billing Server running on same machine and initiating various services in capacity of 1000 CAPS (Call Attempts Per Second). In addition, tests showed a huge improvement in the performance of the TCP/IP stack, which reduces network layer processing and in the end Call Attempts latency. Finally, there is a huge improvement within the file system and disk I/O operations; they ran all tests with maximum logging capability and it didn't influence any benchmark values. "Due to the huge improvements in performance and capacity using the T4-1 architecture, IPgallery has engineered the solution with less hardware.  This means instead of deploying the solution on six T2-based machines, we will deploy on 2 redundant machines while utilizing Oracle Solaris Zones and Oracle VM for higher availability and virtualization" Shimon Lichter, VP R&D, IPgallery In conclusion, using the unique combination of Oracle Solaris and SPARC technologies, IPgallery is able to offer solutions with much lower TCO, while providing a higher level of service capacity, scalability and resiliency. This low-OPEX solution enables the operator, the end-customer, to deliver a high quality service while maintaining high profitability.

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  • Instantiating class with custom allocator in shared memory

    - by recipriversexclusion
    I'm pulling my hair due to the following problem: I am following the example given in boost.interprocess documentation to instantiate a fixed-size ring buffer buffer class that I wrote in shared memory. The skeleton constructor for my class is: template<typename ItemType, class Allocator > SharedMemoryBuffer<ItemType, Allocator>::SharedMemoryBuffer( unsigned long capacity ){ m_capacity = capacity; // Create the buffer nodes. m_start_ptr = this->allocator->allocate(); // allocate first buffer node BufferNode* ptr = m_start_ptr; for( int i = 0 ; i < this->capacity()-1; i++ ) { BufferNode* p = this->allocator->allocate(); // allocate a buffer node } } My first question: Does this sort of allocation guarantee that the buffer nodes are allocated in contiguous memory locations, i.e. when I try to access the n'th node from address m_start_ptr + n*sizeof(BufferNode) in my Read() method would it work? If not, what's a better way to keep the nodes, creating a linked list? My test harness is the following: // Define an STL compatible allocator of ints that allocates from the managed_shared_memory. // This allocator will allow placing containers in the segment typedef allocator<int, managed_shared_memory::segment_manager> ShmemAllocator; //Alias a vector that uses the previous STL-like allocator so that allocates //its values from the segment typedef SharedMemoryBuffer<int, ShmemAllocator> MyBuf; int main(int argc, char *argv[]) { shared_memory_object::remove("MySharedMemory"); //Create a new segment with given name and size managed_shared_memory segment(create_only, "MySharedMemory", 65536); //Initialize shared memory STL-compatible allocator const ShmemAllocator alloc_inst (segment.get_segment_manager()); //Construct a buffer named "MyBuffer" in shared memory with argument alloc_inst MyBuf *pBuf = segment.construct<MyBuf>("MyBuffer")(100, alloc_inst); } This gives me all kinds of compilation errors related to templates for the last statement. What am I doing wrong?

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  • What's faster Model.get(keys) or Model.get_by_id(ids, parent=None)

    - by WooYek
    I'm wondering is there a difference in terms of computing cost for the Model.get(keys) and Model.get_by_id(ids, parent=None) methods? Is there a server side computing advantage of using numeric id's over encoded string keys, or other way around? How big is the difference? PS. Sorry, if it's a dupe. I'm sure I read an article about it, but I cannot find it now.

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  • Creating .lib files in CUDA Toolkit 5

    - by user1683586
    I am taking my first faltering steps with CUDA Toolkit 5.0 RC using VS2010. Separate compilation has me confused. I tried to set up a project as a Static Library (.lib), but when I try to build it, it does not create a device-link.obj and I don't understand why. For instance, there are 2 files: A caller function that uses a function f #include "thrust\host_vector.h" #include "thrust\device_vector.h" using namespace thrust::placeholders; extern __device__ double f(double x); struct f_func { __device__ double operator()(const double& x) const { return f(x); } }; void test(const int len, double * data, double * res) { thrust::device_vector<double> d_data(data, data + len); thrust::transform(d_data.begin(), d_data.end(), d_data.begin(), f_func()); thrust::copy(d_data.begin(),d_data.end(), res); } And a library file that defines f __device__ double f(double x) { return x+2.0; } If I set the option generate relocatable device code to No, the first file will not compile due to unresolved extern function f. If I set it to -rdc, it will compile, but does not produce a device-link.obj file and so the linker fails. If I put the definition of f into the first file and delete the second it builds successfully, but now it isn't separate compilation anymore. How can I build a static library like this with separate source files? [Updated here] I called the first caller file "caller.cu" and the second "libfn.cu". The compiler lines that VS2010 outputs (which I don't fully understand) are (for caller): nvcc.exe -ccbin "C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin" -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\include" -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\include" -G --keep-dir "Debug" -maxrregcount=0 --machine 32 --compile -g -D_MBCS -Xcompiler "/EHsc /W3 /nologo /Od /Zi /RTC1 /MDd " -o "Debug\caller.cu.obj" "G:\Test_Linking\caller.cu" -clean and the same for libfn, then: nvcc.exe -gencode=arch=compute_20,code=\"sm_20,compute_20\" --use-local-env --cl-version 2010 -ccbin "C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin" -rdc=true -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\include" -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\include" -G --keep-dir "Debug" -maxrregcount=0 --machine 32 --compile -g -D_MBCS -Xcompiler "/EHsc /W3 /nologo /Od /Zi /RTC1 /MDd " -o "Debug\caller.cu.obj" "G:\Test_Linking\caller.cu" and again for libfn.

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  • 256 Windows Azure Worker Roles, Windows Kinect and a 90's Text-Based Ray-Tracer

    - by Alan Smith
    For a couple of years I have been demoing a simple render farm hosted in Windows Azure using worker roles and the Azure Storage service. At the start of the presentation I deploy an Azure application that uses 16 worker roles to render a 1,500 frame 3D ray-traced animation. At the end of the presentation, when the animation was complete, I would play the animation delete the Azure deployment. The standing joke with the audience was that it was that it was a “$2 demo”, as the compute charges for running the 16 instances for an hour was $1.92, factor in the bandwidth charges and it’s a couple of dollars. The point of the demo is that it highlights one of the great benefits of cloud computing, you pay for what you use, and if you need massive compute power for a short period of time using Windows Azure can work out very cost effective. The “$2 demo” was great for presenting at user groups and conferences in that it could be deployed to Azure, used to render an animation, and then removed in a one hour session. I have always had the idea of doing something a bit more impressive with the demo, and scaling it from a “$2 demo” to a “$30 demo”. The challenge was to create a visually appealing animation in high definition format and keep the demo time down to one hour.  This article will take a run through how I achieved this. Ray Tracing Ray tracing, a technique for generating high quality photorealistic images, gained popularity in the 90’s with companies like Pixar creating feature length computer animations, and also the emergence of shareware text-based ray tracers that could run on a home PC. In order to render a ray traced image, the ray of light that would pass from the view point must be tracked until it intersects with an object. At the intersection, the color, reflectiveness, transparency, and refractive index of the object are used to calculate if the ray will be reflected or refracted. Each pixel may require thousands of calculations to determine what color it will be in the rendered image. Pin-Board Toys Having very little artistic talent and a basic understanding of maths I decided to focus on an animation that could be modeled fairly easily and would look visually impressive. I’ve always liked the pin-board desktop toys that become popular in the 80’s and when I was working as a 3D animator back in the 90’s I always had the idea of creating a 3D ray-traced animation of a pin-board, but never found the energy to do it. Even if I had a go at it, the render time to produce an animation that would look respectable on a 486 would have been measured in months. PolyRay Back in 1995 I landed my first real job, after spending three years being a beach-ski-climbing-paragliding-bum, and was employed to create 3D ray-traced animations for a CD-ROM that school kids would use to learn physics. I had got into the strange and wonderful world of text-based ray tracing, and was using a shareware ray-tracer called PolyRay. PolyRay takes a text file describing a scene as input and, after a few hours processing on a 486, produced a high quality ray-traced image. The following is an example of a basic PolyRay scene file. background Midnight_Blue   static define matte surface { ambient 0.1 diffuse 0.7 } define matte_white texture { matte { color white } } define matte_black texture { matte { color dark_slate_gray } } define position_cylindrical 3 define lookup_sawtooth 1 define light_wood <0.6, 0.24, 0.1> define median_wood <0.3, 0.12, 0.03> define dark_wood <0.05, 0.01, 0.005>     define wooden texture { noise surface { ambient 0.2  diffuse 0.7  specular white, 0.5 microfacet Reitz 10 position_fn position_cylindrical position_scale 1  lookup_fn lookup_sawtooth octaves 1 turbulence 1 color_map( [0.0, 0.2, light_wood, light_wood] [0.2, 0.3, light_wood, median_wood] [0.3, 0.4, median_wood, light_wood] [0.4, 0.7, light_wood, light_wood] [0.7, 0.8, light_wood, median_wood] [0.8, 0.9, median_wood, light_wood] [0.9, 1.0, light_wood, dark_wood]) } } define glass texture { surface { ambient 0 diffuse 0 specular 0.2 reflection white, 0.1 transmission white, 1, 1.5 }} define shiny surface { ambient 0.1 diffuse 0.6 specular white, 0.6 microfacet Phong 7  } define steely_blue texture { shiny { color black } } define chrome texture { surface { color white ambient 0.0 diffuse 0.2 specular 0.4 microfacet Phong 10 reflection 0.8 } }   viewpoint {     from <4.000, -1.000, 1.000> at <0.000, 0.000, 0.000> up <0, 1, 0> angle 60     resolution 640, 480 aspect 1.6 image_format 0 }       light <-10, 30, 20> light <-10, 30, -20>   object { disc <0, -2, 0>, <0, 1, 0>, 30 wooden }   object { sphere <0.000, 0.000, 0.000>, 1.00 chrome } object { cylinder <0.000, 0.000, 0.000>, <0.000, 0.000, -4.000>, 0.50 chrome }   After setting up the background and defining colors and textures, the viewpoint is specified. The “camera” is located at a point in 3D space, and it looks towards another point. The angle, image resolution, and aspect ratio are specified. Two lights are present in the image at defined coordinates. The three objects in the image are a wooden disc to represent a table top, and a sphere and cylinder that intersect to form a pin that will be used for the pin board toy in the final animation. When the image is rendered, the following image is produced. The pins are modeled with a chrome surface, so they reflect the environment around them. Note that the scale of the pin shaft is not correct, this will be fixed later. Modeling the Pin Board The frame of the pin-board is made up of three boxes, and six cylinders, the front box is modeled using a clear, slightly reflective solid, with the same refractive index of glass. The other shapes are modeled as metal. object { box <-5.5, -1.5, 1>, <5.5, 5.5, 1.2> glass } object { box <-5.5, -1.5, -0.04>, <5.5, 5.5, -0.09> steely_blue } object { box <-5.5, -1.5, -0.52>, <5.5, 5.5, -0.59> steely_blue } object { cylinder <-5.2, -1.2, 1.4>, <-5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, -1.2, 1.4>, <5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <-5.2, 5.2, 1.4>, <-5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, 5.2, 1.4>, <5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <0, -1.2, 1.4>, <0, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <0, 5.2, 1.4>, <0, 5.2, -0.74>, 0.2 steely_blue }   In order to create the matrix of pins that make up the pin board I used a basic console application with a few nested loops to create two intersecting matrixes of pins, which models the layout used in the pin boards. The resulting image is shown below. The pin board contains 11,481 pins, with the scene file containing 23,709 lines of code. For the complete animation 2,000 scene files will be created, which is over 47 million lines of code. Each pin in the pin-board will slide out a specific distance when an object is pressed into the back of the board. This is easily modeled by setting the Z coordinate of the pin to a specific value. In order to set all of the pins in the pin-board to the correct position, a bitmap image can be used. The position of the pin can be set based on the color of the pixel at the appropriate position in the image. When the Windows Azure logo is used to set the Z coordinate of the pins, the following image is generated. The challenge now was to make a cool animation. The Azure Logo is fine, but it is static. Using a normal video to animate the pins would not work; the colors in the video would not be the same as the depth of the objects from the camera. In order to simulate the pin board accurately a series of frames from a depth camera could be used. Windows Kinect The Kenect controllers for the X-Box 360 and Windows feature a depth camera. The Kinect SDK for Windows provides a programming interface for Kenect, providing easy access for .NET developers to the Kinect sensors. The Kinect Explorer provided with the Kinect SDK is a great starting point for exploring Kinect from a developers perspective. Both the X-Box 360 Kinect and the Windows Kinect will work with the Kinect SDK, the Windows Kinect is required for commercial applications, but the X-Box Kinect can be used for hobby projects. The Windows Kinect has the advantage of providing a mode to allow depth capture with objects closer to the camera, which makes for a more accurate depth image for setting the pin positions. Creating a Depth Field Animation The depth field animation used to set the positions of the pin in the pin board was created using a modified version of the Kinect Explorer sample application. In order to simulate the pin board accurately, a small section of the depth range from the depth sensor will be used. Any part of the object in front of the depth range will result in a white pixel; anything behind the depth range will be black. Within the depth range the pixels in the image will be set to RGB values from 0,0,0 to 255,255,255. A screen shot of the modified Kinect Explorer application is shown below. The Kinect Explorer sample application was modified to include slider controls that are used to set the depth range that forms the image from the depth stream. This allows the fine tuning of the depth image that is required for simulating the position of the pins in the pin board. The Kinect Explorer was also modified to record a series of images from the depth camera and save them as a sequence JPEG files that will be used to animate the pins in the animation the Start and Stop buttons are used to start and stop the image recording. En example of one of the depth images is shown below. Once a series of 2,000 depth images has been captured, the task of creating the animation can begin. Rendering a Test Frame In order to test the creation of frames and get an approximation of the time required to render each frame a test frame was rendered on-premise using PolyRay. The output of the rendering process is shown below. The test frame contained 23,629 primitive shapes, most of which are the spheres and cylinders that are used for the 11,800 or so pins in the pin board. The 1280x720 image contains 921,600 pixels, but as anti-aliasing was used the number of rays that were calculated was 4,235,777, with 3,478,754,073 object boundaries checked. The test frame of the pin board with the depth field image applied is shown below. The tracing time for the test frame was 4 minutes 27 seconds, which means rendering the2,000 frames in the animation would take over 148 hours, or a little over 6 days. Although this is much faster that an old 486, waiting almost a week to see the results of an animation would make it challenging for animators to create, view, and refine their animations. It would be much better if the animation could be rendered in less than one hour. Windows Azure Worker Roles The cost of creating an on-premise render farm to render animations increases in proportion to the number of servers. The table below shows the cost of servers for creating a render farm, assuming a cost of $500 per server. Number of Servers Cost 1 $500 16 $8,000 256 $128,000   As well as the cost of the servers, there would be additional costs for networking, racks etc. Hosting an environment of 256 servers on-premise would require a server room with cooling, and some pretty hefty power cabling. The Windows Azure compute services provide worker roles, which are ideal for performing processor intensive compute tasks. With the scalability available in Windows Azure a job that takes 256 hours to complete could be perfumed using different numbers of worker roles. The time and cost of using 1, 16 or 256 worker roles is shown below. Number of Worker Roles Render Time Cost 1 256 hours $30.72 16 16 hours $30.72 256 1 hour $30.72   Using worker roles in Windows Azure provides the same cost for the 256 hour job, irrespective of the number of worker roles used. Provided the compute task can be broken down into many small units, and the worker role compute power can be used effectively, it makes sense to scale the application so that the task is completed quickly, making the results available in a timely fashion. The task of rendering 2,000 frames in an animation is one that can easily be broken down into 2,000 individual pieces, which can be performed by a number of worker roles. Creating a Render Farm in Windows Azure The architecture of the render farm is shown in the following diagram. The render farm is a hybrid application with the following components: ·         On-Premise o   Windows Kinect – Used combined with the Kinect Explorer to create a stream of depth images. o   Animation Creator – This application uses the depth images from the Kinect sensor to create scene description files for PolyRay. These files are then uploaded to the jobs blob container, and job messages added to the jobs queue. o   Process Monitor – This application queries the role instance lifecycle table and displays statistics about the render farm environment and render process. o   Image Downloader – This application polls the image queue and downloads the rendered animation files once they are complete. ·         Windows Azure o   Azure Storage – Queues and blobs are used for the scene description files and completed frames. A table is used to store the statistics about the rendering environment.   The architecture of each worker role is shown below.   The worker role is configured to use local storage, which provides file storage on the worker role instance that can be use by the applications to render the image and transform the format of the image. The service definition for the worker role with the local storage configuration highlighted is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceDefinition name="CloudRay" >   <WorkerRole name="CloudRayWorkerRole" vmsize="Small">     <Imports>     </Imports>     <ConfigurationSettings>       <Setting name="DataConnectionString" />     </ConfigurationSettings>     <LocalResources>       <LocalStorage name="RayFolder" cleanOnRoleRecycle="true" />     </LocalResources>   </WorkerRole> </ServiceDefinition>     The two executable programs, PolyRay.exe and DTA.exe are included in the Azure project, with Copy Always set as the property. PolyRay will take the scene description file and render it to a Truevision TGA file. As the TGA format has not seen much use since the mid 90’s it is converted to a JPG image using Dave's Targa Animator, another shareware application from the 90’s. Each worker roll will use the following process to render the animation frames. 1.       The worker process polls the job queue, if a job is available the scene description file is downloaded from blob storage to local storage. 2.       PolyRay.exe is started in a process with the appropriate command line arguments to render the image as a TGA file. 3.       DTA.exe is started in a process with the appropriate command line arguments convert the TGA file to a JPG file. 4.       The JPG file is uploaded from local storage to the images blob container. 5.       A message is placed on the images queue to indicate a new image is available for download. 6.       The job message is deleted from the job queue. 7.       The role instance lifecycle table is updated with statistics on the number of frames rendered by the worker role instance, and the CPU time used. The code for this is shown below. public override void Run() {     // Set environment variables     string polyRayPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), PolyRayLocation);     string dtaPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), DTALocation);       LocalResource rayStorage = RoleEnvironment.GetLocalResource("RayFolder");     string localStorageRootPath = rayStorage.RootPath;       JobQueue jobQueue = new JobQueue("renderjobs");     JobQueue downloadQueue = new JobQueue("renderimagedownloadjobs");     CloudRayBlob sceneBlob = new CloudRayBlob("scenes");     CloudRayBlob imageBlob = new CloudRayBlob("images");     RoleLifecycleDataSource roleLifecycleDataSource = new RoleLifecycleDataSource();       Frames = 0;       while (true)     {         // Get the render job from the queue         CloudQueueMessage jobMsg = jobQueue.Get();           if (jobMsg != null)         {             // Get the file details             string sceneFile = jobMsg.AsString;             string tgaFile = sceneFile.Replace(".pi", ".tga");             string jpgFile = sceneFile.Replace(".pi", ".jpg");               string sceneFilePath = Path.Combine(localStorageRootPath, sceneFile);             string tgaFilePath = Path.Combine(localStorageRootPath, tgaFile);             string jpgFilePath = Path.Combine(localStorageRootPath, jpgFile);               // Copy the scene file to local storage             sceneBlob.DownloadFile(sceneFilePath);               // Run the ray tracer.             string polyrayArguments =                 string.Format("\"{0}\" -o \"{1}\" -a 2", sceneFilePath, tgaFilePath);             Process polyRayProcess = new Process();             polyRayProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), polyRayPath);             polyRayProcess.StartInfo.Arguments = polyrayArguments;             polyRayProcess.Start();             polyRayProcess.WaitForExit();               // Convert the image             string dtaArguments =                 string.Format(" {0} /FJ /P{1}", tgaFilePath, Path.GetDirectoryName (jpgFilePath));             Process dtaProcess = new Process();             dtaProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), dtaPath);             dtaProcess.StartInfo.Arguments = dtaArguments;             dtaProcess.Start();             dtaProcess.WaitForExit();               // Upload the image to blob storage             imageBlob.UploadFile(jpgFilePath);               // Add a download job.             downloadQueue.Add(jpgFile);               // Delete the render job message             jobQueue.Delete(jobMsg);               Frames++;         }         else         {             Thread.Sleep(1000);         }           // Log the worker role activity.         roleLifecycleDataSource.Alive             ("CloudRayWorker", RoleLifecycleDataSource.RoleLifecycleId, Frames);     } }     Monitoring Worker Role Instance Lifecycle In order to get more accurate statistics about the lifecycle of the worker role instances used to render the animation data was tracked in an Azure storage table. The following class was used to track the worker role lifecycles in Azure storage.   public class RoleLifecycle : TableServiceEntity {     public string ServerName { get; set; }     public string Status { get; set; }     public DateTime StartTime { get; set; }     public DateTime EndTime { get; set; }     public long SecondsRunning { get; set; }     public DateTime LastActiveTime { get; set; }     public int Frames { get; set; }     public string Comment { get; set; }       public RoleLifecycle()     {     }       public RoleLifecycle(string roleName)     {         PartitionKey = roleName;         RowKey = Utils.GetAscendingRowKey();         Status = "Started";         StartTime = DateTime.UtcNow;         LastActiveTime = StartTime;         EndTime = StartTime;         SecondsRunning = 0;         Frames = 0;     } }     A new instance of this class is created and added to the storage table when the role starts. It is then updated each time the worker renders a frame to record the total number of frames rendered and the total processing time. These statistics are used be the monitoring application to determine the effectiveness of use of resources in the render farm. Rendering the Animation The Azure solution was deployed to Windows Azure with the service configuration set to 16 worker role instances. This allows for the application to be tested in the cloud environment, and the performance of the application determined. When I demo the application at conferences and user groups I often start with 16 instances, and then scale up the application to the full 256 instances. The configuration to run 16 instances is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="16" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     About six minutes after deploying the application the first worker roles become active and start to render the first frames of the animation. The CloudRay Monitor application displays an icon for each worker role instance, with a number indicating the number of frames that the worker role has rendered. The statistics on the left show the number of active worker roles and statistics about the render process. The render time is the time since the first worker role became active; the CPU time is the total amount of processing time used by all worker role instances to render the frames.   Five minutes after the first worker role became active the last of the 16 worker roles activated. By this time the first seven worker roles had each rendered one frame of the animation.   With 16 worker roles u and running it can be seen that one hour and 45 minutes CPU time has been used to render 32 frames with a render time of just under 10 minutes.     At this rate it would take over 10 hours to render the 2,000 frames of the full animation. In order to complete the animation in under an hour more processing power will be required. Scaling the render farm from 16 instances to 256 instances is easy using the new management portal. The slider is set to 256 instances, and the configuration saved. We do not need to re-deploy the application, and the 16 instances that are up and running will not be affected. Alternatively, the configuration file for the Azure service could be modified to specify 256 instances.   <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="256" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     Six minutes after the new configuration has been applied 75 new worker roles have activated and are processing their first frames.   Five minutes later the full configuration of 256 worker roles is up and running. We can see that the average rate of frame rendering has increased from 3 to 12 frames per minute, and that over 17 hours of CPU time has been utilized in 23 minutes. In this test the time to provision 140 worker roles was about 11 minutes, which works out at about one every five seconds.   We are now half way through the rendering, with 1,000 frames complete. This has utilized just under three days of CPU time in a little over 35 minutes.   The animation is now complete, with 2,000 frames rendered in a little over 52 minutes. The CPU time used by the 256 worker roles is 6 days, 7 hours and 22 minutes with an average frame rate of 38 frames per minute. The rendering of the last 1,000 frames took 16 minutes 27 seconds, which works out at a rendering rate of 60 frames per minute. The frame counts in the server instances indicate that the use of a queue to distribute the workload has been very effective in distributing the load across the 256 worker role instances. The first 16 instances that were deployed first have rendered between 11 and 13 frames each, whilst the 240 instances that were added when the application was scaled have rendered between 6 and 9 frames each.   Completed Animation I’ve uploaded the completed animation to YouTube, a low resolution preview is shown below. Pin Board Animation Created using Windows Kinect and 256 Windows Azure Worker Roles   The animation can be viewed in 1280x720 resolution at the following link: http://www.youtube.com/watch?v=n5jy6bvSxWc Effective Use of Resources According to the CloudRay monitor statistics the animation took 6 days, 7 hours and 22 minutes CPU to render, this works out at 152 hours of compute time, rounded up to the nearest hour. As the usage for the worker role instances are billed for the full hour, it may have been possible to render the animation using fewer than 256 worker roles. When deciding the optimal usage of resources, the time required to provision and start the worker roles must also be considered. In the demo I started with 16 worker roles, and then scaled the application to 256 worker roles. It would have been more optimal to start the application with maybe 200 worker roles, and utilized the full hour that I was being billed for. This would, however, have prevented showing the ease of scalability of the application. The new management portal displays the CPU usage across the worker roles in the deployment. The average CPU usage across all instances is 93.27%, with over 99% used when all the instances are up and running. This shows that the worker role resources are being used very effectively. Grid Computing Scenarios Although I am using this scenario for a hobby project, there are many scenarios where a large amount of compute power is required for a short period of time. Windows Azure provides a great platform for developing these types of grid computing applications, and can work out very cost effective. ·         Windows Azure can provide massive compute power, on demand, in a matter of minutes. ·         The use of queues to manage the load balancing of jobs between role instances is a simple and effective solution. ·         Using a cloud-computing platform like Windows Azure allows proof-of-concept scenarios to be tested and evaluated on a very low budget. ·         No charges for inbound data transfer makes the uploading of large data sets to Windows Azure Storage services cost effective. (Transaction charges still apply.) Tips for using Windows Azure for Grid Computing Scenarios I found the implementation of a render farm using Windows Azure a fairly simple scenario to implement. I was impressed by ease of scalability that Azure provides, and by the short time that the application took to scale from 16 to 256 worker role instances. In this case it was around 13 minutes, in other tests it took between 10 and 20 minutes. The following tips may be useful when implementing a grid computing project in Windows Azure. ·         Using an Azure Storage queue to load-balance the units of work across multiple worker roles is simple and very effective. The design I have used in this scenario could easily scale to many thousands of worker role instances. ·         Windows Azure accounts are typically limited to 20 cores. If you need to use more than this, a call to support and a credit card check will be required. ·         Be aware of how the billing model works. You will be charged for worker role instances for the full clock our in which the instance is deployed. Schedule the workload to start just after the clock hour has started. ·         Monitor the utilization of the resources you are provisioning, ensure that you are not paying for worker roles that are idle. ·         If you are deploying third party applications to worker roles, you may well run into licensing issues. Purchasing software licenses on a per-processor basis when using hundreds of processors for a short time period would not be cost effective. ·         Third party software may also require installation onto the worker roles, which can be accomplished using start-up tasks. Bear in mind that adding a startup task and possible re-boot will add to the time required for the worker role instance to start and activate. An alternative may be to use a prepared VM and use VM roles. ·         Consider using the Windows Azure Autoscaling Application Block (WASABi) to autoscale the worker roles in your application. When using a large number of worker roles, the utilization must be carefully monitored, if the scaling algorithms are not optimal it could get very expensive!

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  • Buying replacement Macbook Pro Battery - Genuine vs. eBay

    - by Nicolas Webb
    I'm looking to replace my Macbook Pro's battery (15", last model before the Unibody). It's currently at 55% capacity (as reported in System Information and Battery Health Monitor). I've reset the SMC firmware, calibrated the battery, and it's just not lasting that long anymore. I've seen some genuine replacements that are "pulls" (pulled from used computers) that are rated at least 90% capacity (iFixit, MacSales). I've also seen a variety of batteries on eBay that look more like clones than genuine batteries, but are new. A new battery from Apple is $129, and when I brought my laptop in they ran the Battery test and said if I bought the battery right then they'd give me a discount (around $100). Anyone out there used one of these "OEM Compatible" batteries? Fit/finish good? (I don't want a funky color or a corner that sticks out.) Or, should I just suck it up and get the genuine replacement (for about twice the price)?

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  • growing EBS RAID volume

    - by Ryan Fernandes
    I've created a RAID0 configuration with two 1GB EBS volumes, mounted at /dev/md0 using mdadm and formatted with XFS Next, I copied some files over to fill the volume to around 30% of its capacity (of 2GB) I then created snapshots of the volumes using ec2-consistent-snapshot and created volumes of the said snapshots but specified the volume size to be 2GB (effective doubling the capacity on each disk) I then spun up a new instance, assembled the RAID0 configuration on /dev/md0 from the 2 volumes mentioned above and mount it to /vol df -hT showed /vol as 2GB (as expected) Now I ran sudo xfs_growfs -d /vol. The command completed normally but reported blocks changed from 523776 to 524160 (only!) and df -hT still showed /vol as 2GB (instead of the expected 4GB) I rebooted, remounted, reassembled the RAID but it still reports the old size. Any clue as to what went wrong?

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  • Macbook pro asks me to "Service Battery"

    - by Uwe Honekamp
    A couple of weeks ago I checked the battery and at that point in time (after 160 load cycles) it still had a capacity of 5000 mAh. Today, my 2006 macbook pro tells me via the battery menu to "Service Battery". According to Coconut Battery the current capacity is only 1590 mAh. The corresponding help text suggests contacting an Apple Authorized Service Provider (AASP) to have the computer checked. Before I decide to throw money at the AASP I'd like to understand what the AASP could possibly do to eliminate the problem. Isn't it more likely that the battery simply broke at 160 cycles and needs to be replaced? Are there any means by software or firmware to influence battery behavior? Of course, the computer hardware might be broken but how could this result in the described effect? And yes, I'm currently trying a calibration cycle but I have some doubts that it will save the day.

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  • Slow Windows Explorer on Windows 7

    - by MadBoy
    I have Laptop with i7 (4 cores), 8GB ram and SSD OCZ Vertex 3 MaxIOPS which in testing that I did just now does 400mb/s+ read/write. However the responsiveness of Windows Explorer is far from being perfect. Opening up Computer, Documents, going into folders is very slow (1-5seconds). I don't have any viruses or spyware and I have tried changing properties to optimize view for General Items. I tried disabling Search Indexer but it made search in Outlook 2010 crawl and didn't bring any other effect. Even double clicking on file takes some time to open things up (like clicking a Word document). I don't have any drives mapped, my computer is not joined to domain. I have multiple VPN connections that I connect to but they all have disabled default gateways. I tried using CC Cleaner or some Windows 7 Tweaks app to disable some things. I am power user using Visual Studio, Tortoise SVN and other developer/administration apps. Any non obvious ideas? Edit: So I've been trying to pinpoint where the issue comes from and it seems that straight after reboot Windows Explorer opens very fast, when I load 3-4 programs (Royal TS, Visual Studio, Outlook) it's noticeably slower and the more programs I have it gets worse. After I start closing programs it starts working better and if I leave 2 open it's fast again. I tried doing some research with DiskMon and other programs from sysinternals but couldn't find anything suspicious. Below are stats during normal usage with a lots of programs open: - Ram usage with a lot of programs open and no swap file (i disabled it for testing): 6.95GB - CPU usage: 15%, none of the cores takes more then 50% (I have VS 2010 open x 4) HD Tune Pro: OCZ-VERTEX3 MI Benchmark Test capacity: full Read transfer rate Transfer Rate Minimum : 363.9 MB/s Transfer Rate Maximum : 505.5 MB/s Transfer Rate Average : Access Time : Burst Rate : CPU Usage : HD Tune Pro: OCZ-VERTEX3 MI File Benchmark Drive C: Transfer rate test File Size: 500 MB Sequential read 484102 KB/s Sequential write 444714 KB/s Random read 7779 IOPS Random write 16888 IOPS Random read (queue depth = 32) 73007 IOPS Random write (queue depth = 32) 69790 IOPS HD Tune Pro: OCZ-VERTEX3 MI Random Access Test capacity: full Read test Transfer size operations / sec avg. access time max. access time avg. speed 512 bytes 3260 IOPS 0.306 ms 2.106 ms 1.592 MB/s 4 KB 4161 IOPS 0.240 ms 2.006 ms 16.256 MB/s 64 KB 2382 IOPS 0.419 ms 2.367 ms 148.934 MB/s 1 MB 449 IOPS 2.225 ms 4.197 ms 449.407 MB/s Random 809 IOPS 1.235 ms 6.551 ms 410.527 MB/s HD Tune Pro: OCZ-VERTEX3 MI Extra Tests Test capacity: full Random seek 3975 IOPS 0.252 ms 1.941 MB/s Random seek 4 KB 4245 IOPS 0.236 ms 16.583 MB/s Butterfly seek 4086 IOPS 0.245 ms 1.995 MB/s Random seek / size 64 KB 3812 IOPS 0.262 ms 58.606 MB/s Random seek / size 8 MB 120 IOPS 8.348 ms 485.737 MB/s Sequential outer 4524 IOPS 0.221 ms 282.721 MB/s Sequential middle 4429 IOPS 0.226 ms 276.818 MB/s Sequential inner 5504 IOPS 0.182 ms 344.000 MB/s Burst rate 4472 IOPS 0.224 ms 279.475 MB/s

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  • Is it a good idea to have the operating system on a solid state drive?

    - by Kenji Kina
    There is something I don't quite understand. I know a SSD helps with OS load times, but I'm not sure if all this boost is only noticeable/interesting when booting, or gives an all around considerably better experience thereafter. I am interested in having a quick and responsive environment after booting, which leads me to think that it'd be better to spend the SSD capacity in my most used apps (and the page file? Another inside question) and not the OS itself. This, of course, means that I don't know just how much the OS reads/writes its files during normal usage. So, how good an idea is it to dump the whole 20GB+ of Windows 7 OS into the SSD (considering the hefty price per GB of SSD capacity) if I can put up with the usual hard disk boot times? Would I be missing on a lot if I didn't?

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  • Is it safe to buy a replacement laptop battery that has slightly different voltage than the original?

    - by Hugoagogo
    I'm looking for a new battery for my Acer Aspire 4741g laptop. I am trying to get a higher capacity battery. Many sites (like this one and this one) list batteries that are interchangeable with the battery that came with the laptop (AS10D41). These batteries have higher capacity, but also marginally lower voltage (11.1V vs. 11.8V). Is this a problem? Does it mean that the battery is actually incompatible? The replacement battery I am looking at is AS10G3E, and is listed in many places as being compatible with the AS10D41, but nobody makes any mention of the differing voltages. I am trying to find out what kind of problems a lower voltage battery can cause. I know P=IV; with reduced voltage, will the machine draw more current, possibly damaging components? I'm just speculating, but I'm worried about the chance that using a battery at a lower voltage will damage my laptop.

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