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Achieve bottleneck-free grid applications

Learn how to eliminate data bottlenecks for grid computing

By Christopher Keene, JavaWorld.com, 08/02/04

Grid computing is a natural evolution of distributed computing. For compute-heavy applications such as scientific calculations, grid computing has delivered strong cost savings. Yet for more data-intensive enterprise applications, data bottlenecks between grid applications and databases can easily wipe the grid's cost advantages.

Achieving the cost-effective scalability promised by the compute grid (shown in Figure 1) can be achieved only when grid application performance is not throttled by data bottlenecks. The data grid is a way to "grid-enable" any data-intensive application, using distributed caching to eliminate bottlenecks between databases and grid applications.

This article discusses how to overcome the data management bottlenecks associated with grid computing and describes the three critical data services that make up the data grid:

• Caching: Staging frequently-used data in-memory near the application for optimal performance and scalability
• Replication: Ensuring each cache has up-to-date data even for dynamically-changing information
• Mapping: Transforming relational data into the appropriate object format to be used by Java, C++, or C# applications

Finally, this article presents a case study for a mission-critical, data grid deployment in the financial services industry. The case study describes how one bank is using grid data services to provision data to more than 40 distributed grid applications for front and middle office equity trading. These applications process over billion a day in trades, with peak transaction rates of more than 5,000 transactions per second.

Grid computing: Virtualizing IT infrastructure

In many industries such as financial services, brutal pricing pressures are forcing firms to turn increasingly to IT for a competitive advantage. Firms can maintain profits by only lowering IT costs and differentiating their services, which requires a robust, cost-effective, and extensible infrastructure.

The traditional approach to custom application development is to provide every application with its own dedicated database, hardware environment, and tightly coupled application modules. Today, these application silos are seen as inflexible and expensive. Key issues driving companies away from standalone silos include:

1. Dedicated silo hardware is expensive: Many silo applications run on dedicated application server and database computers. This equipment is typically provisioned to handle the worst case load, and is usually highly underutilized.
2. Synchronizing silo data is complex and error-prone: Most silo applications have their own operational datastores, thereby creating a complex data synchronization infrastructure, particularly for shared data about products, partners, and customers.
3. Integrating silo applications is difficult: Getting silo applications to talk to one another is an ongoing challenge, particularly when the underlying reference data between two silos is out of sync.

Data: The Achilles heel for grids

The basic constraint addressed by grid computing is the ability to scale an application across multiple computers. Where multithreading allows an application to scale across multiple CPUs, grid computing allows an application to scale across multiple host computers. However, this increased compute power is only as good as the data infrastructure that supports it.

 

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