Notes from MS Developer Conference for Financial Services
Last week a few folks from our company attended MS Developer Conference for Financial Services in Manhattan. The event was targeting Wall Street developers that came to learn what Microsoft is doing in the area of HPC. It is exciting to see Microsoft get serious about performance and scale-out computing and have a broad plan to address the needs of this already vast and rapidly expanding space.
I will not get into the details of technologies being showcased in this blog. Rather, I'd like to talk a little bit - selfishly - about what we were doing there, and then share one discovery - a grid company we didn't know about that has an interesting data caching solution for .NET community.
- If you wanted realtime data embedded in Excel app, how'd you deliver that data to the spreadsheet?
- If you wanted really computationally intensive spreadsheets, where'd you take the resources?
- If you wanted to share your application with others, how'd you do that?
Turns out, grid computing offers an elegant answer:
Step 1: Move the data from spreadsheet to Data Grid
Step 2: Move the computations from spreadsheet to Compute Grid
Step 3: Instrument Excel to both push the computations to the Grid and pull the results off the Grid as appropriate.
Step 4: Share the "dumb client" easily, it's now light-weight
Many grid vendors - and their Excel customers - are recognizing the business benefits and building solutions that makes Excel-on-the-Grid work better, ideally someday out-of-the-box. Amongst such vendors are Microsoft, GigaSpaces, Data Synapse, and others. We at Grid Dynamics were asked to develop a demo that showed Microsoft Windows Compute Cluster Server 2008 as a computational engine behind Excel-on-the-Grid, backed by GigaSpaces XAP 6.0 as in-memory data grid.
The demo works as follows:
1. Excel sends the job to MS WCCS
2. The job is directed to & executed on the server that runs the right local data cache by XAP
3. Excel gets a notification when the job is finished and displays the results
The main points of the demo are three-fold:
a. Excel-on-the-Grid is COOL
b. One can mix-and-match various compute and data grid solutions to achieve business objectives
c. Data-aware job scheduling (which is the ability of job scheduler to co-locate job execution local to the cached data) gives 2x - 3x performance boost to data-hungry jobs. What's especially important to point out is that the job itself is a "black box" to the grid - no changes are done to the algorithm itself, it may be considered a legacy code for all intents and purposes.
Traditionally, Java community had been way ahead of the game on Data Grids. GigaSpaces, Tangosol and GemStone - the three big mature commercial players in data grid space - are all Java-based technologies. Yet the applications that require data grid access are a good mix between Java and .NET these days (there is always some C/C++ and other varieties, of course). While these Java technologies are supporting .NET clients in various ways, it makes sense for pureplay .NET caching and data grid solutions to be available on the market as well.
We weren't aware of such solutions when we ran into ScaleOut Software last week. They offer replicated data grid with a lot of nice features. They don't have two big ticket items that leading Java caching vendors do: data partitioning and strong out-of-the-box DB persistence. Still, for application session data, and many other stateful application needs in Windows environments, I can see ScaleOut providing a good solution for developers. We haven't really had a chance to do anything with ScaleOut software yet, but I hope to try it out soon.
I will not get into the details of technologies being showcased in this blog. Rather, I'd like to talk a little bit - selfishly - about what we were doing there, and then share one discovery - a grid company we didn't know about that has an interesting data caching solution for .NET community.
1. Excel-on-the-Grid
We were showing a demo of Excel application acting as a client for a grid-based processing. This is a powerful concept that is gaining wide spread on Wall Street. Excel is traditionally used in financial applications for heavy-duty data aggregation, analysis and visualization, all of which requires local data and processing on the desktop. This approach has serious limitations:- If you wanted realtime data embedded in Excel app, how'd you deliver that data to the spreadsheet?
- If you wanted really computationally intensive spreadsheets, where'd you take the resources?
- If you wanted to share your application with others, how'd you do that?
Turns out, grid computing offers an elegant answer:
Step 1: Move the data from spreadsheet to Data Grid
Step 2: Move the computations from spreadsheet to Compute Grid
Step 3: Instrument Excel to both push the computations to the Grid and pull the results off the Grid as appropriate.
Step 4: Share the "dumb client" easily, it's now light-weight
Many grid vendors - and their Excel customers - are recognizing the business benefits and building solutions that makes Excel-on-the-Grid work better, ideally someday out-of-the-box. Amongst such vendors are Microsoft, GigaSpaces, Data Synapse, and others. We at Grid Dynamics were asked to develop a demo that showed Microsoft Windows Compute Cluster Server 2008 as a computational engine behind Excel-on-the-Grid, backed by GigaSpaces XAP 6.0 as in-memory data grid.
The demo works as follows:
1. Excel sends the job to MS WCCS
2. The job is directed to & executed on the server that runs the right local data cache by XAP
3. Excel gets a notification when the job is finished and displays the results
The main points of the demo are three-fold:
a. Excel-on-the-Grid is COOL
b. One can mix-and-match various compute and data grid solutions to achieve business objectives
c. Data-aware job scheduling (which is the ability of job scheduler to co-locate job execution local to the cached data) gives 2x - 3x performance boost to data-hungry jobs. What's especially important to point out is that the job itself is a "black box" to the grid - no changes are done to the algorithm itself, it may be considered a legacy code for all intents and purposes.
2. ScaleOut Software
An interesting discovery of the conference for our team had been a vendor, ScaleOut Software, who has a pure .NET implementation of data grid with seemingly good architecture and nice features.Traditionally, Java community had been way ahead of the game on Data Grids. GigaSpaces, Tangosol and GemStone - the three big mature commercial players in data grid space - are all Java-based technologies. Yet the applications that require data grid access are a good mix between Java and .NET these days (there is always some C/C++ and other varieties, of course). While these Java technologies are supporting .NET clients in various ways, it makes sense for pureplay .NET caching and data grid solutions to be available on the market as well.
We weren't aware of such solutions when we ran into ScaleOut Software last week. They offer replicated data grid with a lot of nice features. They don't have two big ticket items that leading Java caching vendors do: data partitioning and strong out-of-the-box DB persistence. Still, for application session data, and many other stateful application needs in Windows environments, I can see ScaleOut providing a good solution for developers. We haven't really had a chance to do anything with ScaleOut software yet, but I hope to try it out soon.
Labels: convergence, gigaspaces, grid computing, grid consulting, ~Victoria Livschitz
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11:43 PM
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