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  • Entity Framework and distributed Systems

    - by Dirk Beckmann
    I need some help or maybe only a hint for the right direction. I've got a system that is sperated into two applications. An existing VB.NET desktop client using Entity Framework 5 with code first approach and a asp.net Web Api client in C# that will be refactored right yet. It should be possible to deliver OData. The system and the datamodel is still involving and so migrations will happen in undefined intervalls. So I'm now struggling how to manage my database access on the web api system. So my favourd approch would be us Entity Framework on both systems but I'm running into trouble while creating new migrations. Two solutions I've thought about: Shared Data Access dll The first idea was to separate the data access layer to a seperate project an reference from each of the systems. The context would be the same as long as the dll is up to date in each system. This way both soulutions would be able to make a migration. The main problem ist that it is much more complicate to update a web api system than it is with the client Click Once Update Solution and not every migration is important for the web api. This would couse more update trouble and out of sync libraries Database First on Web Api The second idea was just to use the database first approch an on web api side. But it seems that all annotations will be lost by each model update. Other solutions with stored procedures have been discarded because of missing OData support and maintainability. Does anyone run into same conflicts or has any advices how such a problem can be solved!

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  • How are Reads Distributed in a Workload

    - by Bill Graziano
    People have uploaded nearly one millions rows of trace data to TraceTune.  That’s enough data to start to look at the results in aggregate.  The first thing I want to look at is logical reads.  This is the easiest metric to identify and fix. When you upload a trace, I rank each statement based on the total number of logical reads.  I also calculate each statement’s percentage of the total logical reads.  I do the same thing for CPU, duration and logical writes.  When you view a statement you can see all the details like this: This single statement consumed 61.4% of the total logical reads on the system while we were tracing it.  I also wanted to see the distribution of reads across statements.  That graph looks like this: On average, the highest ranked statement consumed just under 50% of the reads on the system.  When I tune a system, I’m usually starting in one of two modes: this “piece” is slow or the whole system is slow.  If a given piece (screen, report, query, etc.) is slow you can usually find the specific statements behind it and tune it.  You can make that individual piece faster but you may not affect the whole system. When you’re trying to speed up an entire server you need to identity those queries that are using the most disk resources in aggregate.  Fixing those will make them faster and it will leave more disk throughput for the rest of the queries. Here are some of the things I’ve learned querying this data: The highest ranked query averages just under 50% of the total reads on the system. The top 3 ranked queries average 73% of the total reads on the system. The top 10 ranked queries average 91% of the total reads on the system. Remember these are averages across all the traces that have been uploaded.  And I’m guessing that people mainly upload traces where there are performance problems so your mileage may vary. I also learned that slow queries aren’t the problem.  Before I wrote ClearTrace I used to identify queries by filtering on high logical reads using Profiler.  That picked out individual queries but those rarely ran often enough to put a large load on the system. If you look at the execution count by rank you’d see that the highest ranked queries also have the highest execution counts.  The graph would look very similar to the one above but flatter.  These queries don’t look that bad individually but run so often that they hog the disk capacity. The take away from all this is that you really should be tuning the top 10 queries if you want to make your system faster.  Tuning individually slow queries will help those specific queries but won’t have much impact on the system as a whole.

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  • Scrum Board for a distributed team

    - by Falcon
    I am looking for recommendations on a digital Scrum Board which can be shared over the internet. I imagine something like a big tablet on which you can draw and which remote users can access, too. I dislike Scrum software because I think one major benefit of a Scrum Board is its physical presence. It should be hard to ignore. The best solution would be two big tablets on which you can draw and which can be synchronized. Has anyone got product recommendations for something like this? Or would you rather use a software? Kind regards, Falcon

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  • Headaches using distributed version control for traditional teams?

    - by J Cooper
    Though I use and like DVCS for my personal projects, and can totally see how it makes managing contributions to your project from others easier (e.g. your typical Github scenario), it seems like for a "traditional" team there could be some problems over the centralized approach employed by solutions like TFS, Perforce, etc. (By "traditional" I mean a team of developers in an office working on one project that no one person "owns", with potentially everyone touching the same code.) A couple of these problems I've foreseen on my own, but please chime in with other considerations. In a traditional system, when you try to check your change in to the server, if someone else has previously checked in a conflicting change then you are forced to merge before you can check yours in. In the DVCS model, each developer checks in their changes locally and at some point pushes to some other repo. That repo then has a branch of that file that 2 people changed. It seems that now someone must be put in charge of dealing with that situation. A designated person on the team might not have sufficient knowledge of the entire codebase to be able to handle merging all conflicts. So now an extra step has been added where someone has to approach one of those developers, tell him to pull and do the merge and then push again (or you have to build an infrastructure that automates that task). Furthermore, since DVCS tends to make working locally so convenient, it is probable that developers could accumulate a few changes in their local repos before pushing, making such conflicts more common and more complicated. Obviously if everyone on the team only works on different areas of the code, this isn't an issue. But I'm curious about the case where everyone is working on the same code. It seems like the centralized model forces conflicts to be dealt with quickly and frequently, minimizing the need to do large, painful merges or have anyone "police" the main repo. So for those of you who do use a DVCS with your team in your office, how do you handle such cases? Do you find your daily (or more likely, weekly) workflow affected negatively? Are there any other considerations I should be aware of before recommending a DVCS at my workplace?

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  • Is there a Distributed SAN/Storage System out there?

    - by Joel Coel
    Like many other places, we ask our users not to save files to their local machines. Instead, we encourage that they be put on a file server so that others (with appropriate permissions) can use them and that the files are backed up properly. The result of this is that most users have large hard drives that are sitting mainly empty. It's 2010 now. Surely there is a system out there that lets you turn that empty space into a virtual SAN or document library? What I envision is a client program that is pushed out to users' PCs that coordinates with a central server. The server looks to users just like a normal file server, but instead of keeping entire file contents it merely keeps a record of where those files can be found among various user PCs. It then coordinates with the right clients to serve up file requests. The client software would be able to respond to such requests directly, as well as be smart enough to cache recent files locally. For redundancy the server could make sure files are copied to multiple PCs, perhaps allowing you to define groups in different locations so that an instance of the entire repository lives in each group to protect against a disaster in one building taking down everything else. Obviously you wouldn't point your database server here, but for simpler things I see several advantages: Files can often be transferred from a nearer machine. Disk space grows automatically as your company does. Should ultimately be cheaper, as you don't need to keep a separate set of disks I can see a few downsides as well: Occasional degradation of user pc performance, if the machine has to serve or accept a large file transfer during a busy period. Writes have to be propogated around the network several times (though I suspect this isn't really much of a problem, as reading happens in most places more than writing) Still need a way to send a complete copy of the data offsite occasionally, and this would make it very hard to do differentials Think of this like a cloud storage system that lives entirely within your corporate LAN and makes use of your existing user equipment. Our old main file server is due for retirement in about 2 years, and I'm looking into replacing it with a small SAN. I'm thinking something like this would be a better fit. As a school, we have a couple computer labs I can leave running that would be perfect for adding a little extra redundancy to the system. Unfortunately, the closest thing I can find is Dienst, and it's just a paper that dates back to 1994. Am I just using the wrong buzzwords in my searches, or does this really not exist? If not, is there a big downside that I'm missing?

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  • I.T. Chargeback : Core to Cloud Computing

    - by Anand Akela
    Contributed by Mark McGill Consolidation and Virtualization have been widely adopted over the years to help deliver benefits such as increased server utilization, greater agility and lower cost to the I.T. organization. These are key enablers of cloud, but in themselves they do not provide a complete cloud solution. Building a true enterprise private cloud involves moving from an admin driven world, where the I.T. department is ultimately responsible for the provisioning of servers, databases, middleware and applications, to a world where the consumers of I.T. resources can provision their infrastructure, platforms and even complete application stacks on demand. Switching from an admin-driven provisioning model to a user-driven model creates some challenges. How do you ensure that users provisioning resources will not provision more than they need? How do you encourage users to return resources when they have finished with them so that others can use them? While chargeback has existed as a concept for many years (especially in mainframe environments), it is the move to this self-service model that has created a need for a new breed of chargeback applications for cloud. Enabling self-service without some form of chargeback is like opening a shop where all of the goods are free. A successful chargeback solution will be able to allocate the costs of shared I.T. infrastructure based on the relative consumption by the users. Doing this creates transparency between the I.T. department and the consumers of I.T. When users are able to understand how their consumption translates to cost they are much more likely to be prudent when it comes to their use of I.T. resources. This also gives them control of their I.T. costs, as moderate usage will translate to a lower charge at the end of the month. Implementing Chargeback successfully create a win-win situation for I.T. and the consumers. Chargeback can help to ensure that I.T. resources are used for activities that deliver business value. It also improves the overall utilization of I.T. infrastructure as I.T. resources that are not needed are not left running idle. Enterprise Manager 12c provides an integrated metering and chargeback solution for Enterprise Manager Targets. This solution is built on top of the rich configuration and utilization information already available in Enterprise Manager. It provides metering not just for virtual machines, but also for physical hosts, databases and middleware. Enterprise Manager 12c provides metering based on the utilization and configuration of the following types of Enterprise Manager Target: Oracle VM Host Oracle Database Oracle WebLogic Server Using Enterprise Manager Chargeback, administrators are able to create a set of Charge Plans that are used to attach prices to the various metered resources. These plans can contain fixed costs (eg. $10/month/database), configuration based costs (eg. $10/month if OS is Windows) and utilization based costs (eg. $0.05/GB of Memory/hour) The self-service user provisioning these resources is then able to view a report that details their usage and helps them understand how this usage translates into cost. Armed with this information, the user is able to determine if the resources are delivering adequate business value based on what is being charged. Figure 1: Chargeback in Self-Service Portal Enterprise Manager 12c provides a variety of additional interfaces into this data. The administrator can access summary and trending reports. Summary reports allow the administrator to drill-down through the cost center hierarchy to identify, for example, the top resource consumers across the organization. Figure 2: Charge Summary Report Trending reports can be used for I.T. planning and budgeting as they show utilization and charge trends over a period of time. Figure 3: CPU Trend Report We also provide chargeback reports through BI Publisher. This provides a way for users who do not have an Enterprise Manager login (such as Line of Business managers) to view charge and usage information. For situations where a bill needs to be produced, chargeback can be integrated with billing applications such as Oracle Billing and Revenue Management (BRM). Further information on Enterprise Manager 12c’s integrated metering and chargeback: White Paper Screenwatch Cloud Management on OTN

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  • Oracle auf der CeBIT 2011 in Hannover

    - by franziska.schneider(at)oracle.com
    Cloud Computing als Organisationsstrategie in heterogenen Umgebungen 02.03.2011, 15:40 - 16:00 Halle 4, Stand A 58 Veranstalter: BITKOM Veranstaltungsreihe: Cloud Computing World Referent: Helene Lengler, Vice President, ORACLE Deutschland B.V. & Co. KG   Weiterhin können Sie viele Oracle Partner auf der CeBIT treffen. Schreiben Sie uns einfach mit Ihrem Themenwunsch an und wir organisieren einen Termin.

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