Businesses today face many challenges in meeting their growing IT requirements. Investments made years ago in networks and infrastructure are aging. As the business grows, the challenge becomes how to use the existing infrastructure at its highest capacity to handle the growth in business.

Scalability is the ability of a system to handle increasing load in a graceful manner, implying that a system can be readily extended as your business grows. The ability to scale directly impacts the business applications and the business unit that owns the applications. You want to be able to scale your product with predictable costs and without major disruption when the business grows.

The traditional method of scaling systems is to improve the memory and processing power of existing systems and to add systems to spread the workload. In most cases, the web systems visible to the users and the middleware systems that host the business applications can be effectively scaled with additional servers to provide additional memory and processing cycles to service requests. However, as these systems are scaled to handle additional load, the load is also increased on the data servers that contain business and application data, often creating a bottleneck for throughput.

Data servers present their own challenges when scaling to handle the demand for large amounts of data. While the same approach taken to scale web and middleware systems is a viable approach, challenges arise in keeping the data servers synchronized for data integrity and crash recovery. The communication between servers that is required for synchronization can grow exponentially as the number of data servers increases, ultimately limiting their scalability.

A typical approach to resolving performance and, therefore, scalability problems that are inherent with data servers is to implement some form of caching of the data. Simply defined, the is a copy of frequently accessed data that is held in memory to reduce the access time to the data. Caching data has the effect of placing the data closer to the application, which improves performance and throughput. It also reduces the number of requests to the database and, thus, reduces that resource’s potential as a bottleneck in the application. A cache, then, can extend the storage capability and can act as a to the database.

The IBM® WebSphere® DataPower® XC10 Appliance provides a solution to the scalability challenge for data. The WebSphere DataPower XC10 Appliance caches data from the data servers for high-speed access by applications. The cache acts as a cushion for the data servers by storing frequently used data, thus reducing the amount of read and write accesses to the data servers. This caching tier is situated between the application server tier (which hosts the business applications) and the data tier (). The cache is easily expandable and can store and manage large amounts of data securely with integrity and with consistent performance.

This IBM Redguide™ publication provides an overview of the WebSphere DataPower XC10. It explains how you can save time and money using distributed caching with the WebSphere DataPower XC10 Appliance. Although business executives and solution architects will find this information helpful, the guide is also useful to anyone who is interested in learning more about the XC10.