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  • How to rewrite a TCP MMOG server designed to run in a single machine, in a distributed way?

    - by Dokkat
    I have a MMOG server running on C++, using winsockets. My server won't support more than 200 players. I had the idea of redesigning it so it will use multiple servers instead of one, so, maybe, for example, each server could take care of a number of players, and, if it was too laggy, it could transfer the responsability of that player to other server. I'm not sure of how to program a consistent game logic like that, though. Are there techniques for this?

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  • Windows Azure Recipe: High Performance Computing

    - by Clint Edmonson
    One of the most attractive ways to use a cloud platform is for parallel processing. Commonly known as high-performance computing (HPC), this approach relies on executing code on many machines at the same time. On Windows Azure, this means running many role instances simultaneously, all working in parallel to solve some problem. Doing this requires some way to schedule applications, which means distributing their work across these instances. To allow this, Windows Azure provides the HPC Scheduler. This service can work with HPC applications built to use the industry-standard Message Passing Interface (MPI). Software that does finite element analysis, such as car crash simulations, is one example of this type of application, and there are many others. The HPC Scheduler can also be used with so-called embarrassingly parallel applications, such as Monte Carlo simulations. Whatever problem is addressed, the value this component provides is the same: It handles the complex problem of scheduling parallel computing work across many Windows Azure worker role instances. Drivers Elastic compute and storage resources Cost avoidance Solution Here’s a sketch of a solution using our Windows Azure HPC SDK: Ingredients Web Role – this hosts a HPC scheduler web portal to allow web based job submission and management. It also exposes an HTTP web service API to allow other tools (including Visual Studio) to post jobs as well. Worker Role – typically multiple worker roles are enlisted, including at least one head node that schedules jobs to be run among the remaining compute nodes. Database – stores state information about the job queue and resource configuration for the solution. Blobs, Tables, Queues, Caching (optional) – many parallel algorithms persist intermediate and/or permanent data as a result of their processing. These fast, highly reliable, parallelizable storage options are all available to all the jobs being processed. Training Here is a link to online Windows Azure training labs where you can learn more about the individual ingredients described above. (Note: The entire Windows Azure Training Kit can also be downloaded for offline use.) Windows Azure HPC Scheduler (3 labs)  The Windows Azure HPC Scheduler includes modules and features that enable you to launch and manage high-performance computing (HPC) applications and other parallel workloads within a Windows Azure service. The scheduler supports parallel computational tasks such as parametric sweeps, Message Passing Interface (MPI) processes, and service-oriented architecture (SOA) requests across your computing resources in Windows Azure. With the Windows Azure HPC Scheduler SDK, developers can create Windows Azure deployments that support scalable, compute-intensive, parallel applications. See my Windows Azure Resource Guide for more guidance on how to get started, including links web portals, training kits, samples, and blogs related to Windows Azure.

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  • With MSDN and BizSpark, Cloud Computing is Closer than You Think

    Cloud computing offers significant advantages for businesses of all sizes, and it's easier to get started than you think. Microsoft makes Windows Azure compute time available for MSDN subscribers, as well as for software start-ups through the Microsoft BizSpark program. Learn why cloud computing is a good fit for you and how you can get started.

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  • With MSDN and BizSpark, Cloud Computing is Closer than You Think

    Cloud computing offers significant advantages for businesses of all sizes, and it's easier to get started than you think. Microsoft makes Windows Azure compute time available for MSDN subscribers, as well as for software start-ups through the Microsoft BizSpark program. Learn why cloud computing is a good fit for you and how you can get started.

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  • Understanding the levels of computing

    - by RParadox
    Sorry, for my confused question. I'm looking for some pointers. Up to now I have been working mostly with Java and Python on the application layer and I have only a vague understanding of operating systems and hardware. I want to understand much more about the lower levels of computing, but it gets really overwhelming somehow. At university I took a class about microprogramming, i.e. how processors get hard-wired to implement the ASM codes. Up to now I always thought I wouldn't get more done if learned more about the "low level". One question I have is: how is it even possible that hardware gets hidden almost completely from the developer? Is it accurate to say that the operating system is a software layer for the hardware? One small example: in programming I have never come across the need to understand what L2 or L3 Cache is. For the typical business application environment one almost never needs to understand assembler and the lower levels of computing, because nowadays there is a technology stack for almost anything. I guess the whole point of these lower levels is to provide an interface to higher levels. On the other hand I wonder how much influence the lower levels can have, for example this whole graphics computing thing. So, on the other hand, there is this theoretical computer science branch, which works on abstract computing models. However, I also rarely encountered situations, where I found it helpful thinking in the categories of complexity models, proof verification, etc. I sort of know, that there is a complexity class called NP, and that they are kind of impossible to solve for a big number of N. What I'm missing is a reference for a framework to think about these things. It seems to me, that there all kinds of different camps, who rarely interact. The last few weeks I have been reading about security issues. Here somehow, much of the different layers come together. Attacks and exploits almost always occur on the lower level, so in this case it is necessary to learn about the details of the OSI layers, the inner workings of an OS, etc.

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  • How DBAs Can Tune Distributed IBM DB2 Applications

    Many critical business applications now execute in an environment separate from that of the enterprise database server. The database administrator often finds monitoring and performance tuning of these "distributed" applications to be especially difficult. This article looks at common performance issues of distributed applications and presents advice to assist the IBM DB2 database administrator in mitigating performance problems.

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  • MSDTC attempts to enlist client machine in a distributed transaction

    - by Ken
    Hi there We're seeing the following intermittent warning logged by MSDTC: A caller has attempted to propagate a transaction to a remote system, but MSDTC network DTC access is currently disabled on machine 'X'. Please review the MS DTC configuration settings. However, MSDTC is disabled on machine X by design - it's a client machine, and has no business being enlisted in the transaction! Several windows service endpoints hosting WCF services over TCP Single SQL Server 2005 instance beneath Linq to Sql Remote client receives event callbacks over WCF/TCP The issue is tricky to reproduce - usually following restart of services. We suspect a callback to the client machine is occurring within the context of a transaction. Just wondering if anyone has seen similar issues?? Ken

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  • Ehcache - Distributed RMI not working

    - by Ted
    Hi, I have this strange problem with ehcache 2.0 that I hope someone can help me with. I have set up a cluster of two hosts, A and B. I can see that heartbeats are received at both ends, so I'm pretty sure the networking and multicast stuff is working. The problem is that is I put an element into the cache at host A, I can see in the logs of host B that it receives a remote put. But when I request the same element from host B, it runs off to the data base and performs a query nonetheless. What may be the cause of this? Thankful for any pointers!

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  • running a python script where dependencies are not avail: distributed computing

    - by sadhu_
    Hi, I have access to a grid (running condor) that would (potentially) allow to very substantially reduce how long by nltk based nlp tasks take. unfortunately, i dont have root access on the cluster so cannot install new packages, only run whatever is available on the linux boxes. python is of course available, but nltk isnt - i was wondering however, if there might be a way around this somehow ? is there a way i can somehow still distribute the task in a self-contained 'package' of some sort? Thanks for your hel

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  • Sphinx search distributed index tuning

    - by Andriy Bohdan
    I'm deciding how to split 3 large sphinx indexes between 3 servers. Each of the 3 indexes is searched separately. What's more effective: to host each index on separate machine Example machine1 - index1 machine2 - index2 machine3 - index3 or to split each index into 3 parts and host each part of the same index on separate machine. Example machine1 - index1_chunk1, index2_chunk1, index3_chunk1 machine2 - index1_chunk2, index2_chunk2, index3_chunk2 machine3 - index1_chunk3, index2_chunk3, index3_chunk3 ?

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  • Distributed Lucene.NET

    - by user72185
    Hi, I have a Terabyte of data, maybe more, which I'd like to index and search with Lucene. I'd like to be able to split the index out to different machines, similar to what Solr does (if I understand Solr correctly). Are there any existing tools to do this on the Windows platform? Thanks!

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  • Advice on designing and building distributed application to track vehicles

    - by dario-g
    I'm working on application for tracking vehicles. There will be about 10k or more vehicles. Each will be sending ~250bytes in each minute. Data contains gps location and everything from CAN Bus (every data that we can read from vehicle computer and dashboard). Data are sent by GSM/GPRS (using UDP protocol). Estimated rows with this data per day is ~2000k. I see there 3 main blocks. 1. Multithreaded Socket Server (MSS) - I have it. MSS stores received data to the queue (using NServiceBus). 2. Rule Processor Server (RPS) - this is core of this system. This block is responsible for parsing received data, storing in the database, processing rules, sending messages to Notifier Server (this will be sending e-mails/sms texts). Rule example. As I said between received bytes there will be information about current speed. When speed will be above 120 then: show alert in web application for specified users, send e-mail, send sms text. (There can be more than one instance of RPS). 3. Web application - allows reporting and defining rules by users, monitoring alerts, etc. I'm looking for advice how to design communication between RPS and Web application. Some questions: - Should Web application and RPS have separated databases or one central database will be enough? I have one domain model in web application. If there will be one central database then can I use the same model (objects) on RPS? So, how to send changed rules to RPS? I try to decouple this blocks as much as possible. I'm planning to create different instance of application for each client (each client will have separated database). One client will be have 10k vehicles, others only 100 vehicles.

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  • Quantum Computing and Encryption Breaking

    - by Earlz
    Ok, I read a while back that Quantum Computers can break most types of hashing and encryption in use today in a very short amount of time(I believe it was mere minutes). How is it possible? I've tried reading articles about it but I get lost at the a quantum bit can be 1, 0, or something else. Can someone explain how this relates to cracking such algorithms in plain English without all the fancy maths?

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  • Distributed Message Ordering

    - by sbanwart
    I have an architectural question on handling message ordering. For purposes of this question, the transport is irrelevant, so I'm not going to specify one. Say we have three systems, a website, a CRM and an ERP. For this example, the ERP will be the "master" system in terms of data ownership. The website and the CRM can both send a new customer message to the ERP system. The ERP system then adds a customer and publishes the customer with the newly assigned account number so that the website and CRM can add the account number to their local customer records. This is a pretty straight forward process. Next we move on to placing orders. The account number is required in order for the CRM or website to place an order with the ERP system. However the CRM will permit the user to place an order even if the customer lacks an account number. (For this example assume we can't modify the CRM behavior) This creates the possibility that a user could create a new customer, and place an order before the account number gets updated in the CRM. What is the best way to handle this scenario? Would it be best to send the order message sans account number and let it go to an error queue? Would it be better to have the CRM endpoint hold the message and wait until the account number is updated in the CRM? Maybe something completely different that I haven't thought of? Thanks in advance for any help.

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  • Best Work Queue service for distributed clusters

    - by onewheelgood
    Hi there. I require a simple work queue type system for asynchronous task management. I have looked at both beanstalkd and gearman. However, both these seem to assume that the client and the queue server are on the same network, and therefore that there will always be a reliable network between them. I need one that can support the client and server being in different places in the world, and be able to manage temporary loss of network connection between clusters. Ideally, this would work in such a way where I post a job to a local proxy that attempts to send it to the main queue server. If there is no network connection, it would try again later, however it would not lose the job or delay the client. Any recommendations?

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  • JPA in distributed Java EE configuration

    - by sof
    Hello, I'm developing a JEE application to run on Glassfish: Database (javaDB, MS SQL, MySQL or Oracle) EJB layer with JPA (Toplink essentials - from Glassfish) for database access JSF/Icefaces based web UI accessing the EJB layer The application will have a lot of concurrent web client, so I want to run it on different physical servers and use a load-balancer. My problem is now how to keep the applications synchronized. I intend to set up multiple servers, each running Glassfish with my EAR app installed. Whenever on one of the servers data is added to or removed from the database (via JPA, no direct SQL queries), this change should be reflected in the JPA layer on the other servers. I've been looking around for solutions to this, but couldn't find anything I really like (the full Toplink from Oracle claims to have a solution, but don't know). Doing a refresh before every access to a JPA entity could work, but is far from efficient. Are there any patterns, libraries, ... that could help here? Thanks a lot!

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  • hibernate distributed 2nd level cache options

    - by ishmeister
    Not really a question but I'm looking for comments/suggestions from anyone who has experiences using one or more of the following: EhCache with RMI EhCache with JGroups EhCache with Terracotta Gigaspaces Data Grid A bit of background: our applications is read only for the most part but there is some user data that is read-write and some that is only written (and can also be reasonably inaccurate). In addition, it would be nice to have tools that enable us to flush and fill the cache at intervals or by admin intervention. Regarding the first option - are there any concerns about the overhead of RMI and performance of Java serialization?

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  • Understanding omission failure in distributed systems

    - by karthik A
    The following text says this which I'm not able to quite agree : client C sends a request R to server S. The time taken by a communication link to transport R over the link is D. P is the maximum time needed by S to recieve , process and reply to R. If omission failure is assumed ; then if no reply to R is received within 2(D+P) , then C will never recieve a reply to R . Why is the time here 2(D+P). As I understand shouldn't it be 2D+P ?

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  • High performance distributed asynchronous RPC in java

    - by unludo
    I would like to do RPC to a list of clients with the following requirements: the server does not know the clients (implies a kind of broker?) and the cleints do not know the server there may be several clients - they share the load to treat the RPC The RPC is asynchronous very fast (round-trip < 1ms) optional : offers a fail-over mechanism. It can be done with underlying tools which are not really intended for that (Hazelcast is an example). What would you use for such requirements? Thanks!

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  • Generic DRM (Distributed resource management) wrapper

    - by Pavel Bernshtam
    I need to write a software, which launches DRM jobs in a customer environment and monitors those jobs status. It should work with various customer environments and DRMs - like LSF, Sun Grid and others. Can you recommend some 3rd party library, which hides DRM differences from me and has API like "launch job", "get list of jobs", "get job status" etc. ? Both Java and native libraries are good for me.

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  • Versioning millions of files with distributed SCM

    - by C. Lawrence Wenham
    I'm looking into the feasibility of using off-the-shelf distributed SCMs such as Git or Mercurial to manage millions of XML files. Each file would be a commercial transaction, such as a purchase order, that would be updated perhaps 10 times during the lifecycle of the transaction until it is "done" and changes no more. And by "manage", I mean that the SCM would be used to not just version the files, but also to replicate them to other machines for redundancy and transfer of IP. Lets suppose, for the sake of example, that a goal is to provide good performance if it was handling the volume of orders that Amazon.com claimed to have at its peak in December 2010: about 150,000 orders per minute. We're expecting the system to be distributed over many servers in order to get reasonable performance. We're also planning to use solid-state drives exclusively. There is a reason why we don't want to use an RDBMS for primary storage, but it's a bit beyond the scope of this question. Does anyone have first-hand experience with the performance of distributed SCMs under such a load, and what strategies were used? Open-source preferred, since the final product is to be FOSS, too.

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