Memory in the Data Center - part III

This is a continuation from Memory in the Data Center - Part II, and Memory in the Data Center - Part I.

So far we've covered a bit about media, and also architecture. Now let’s talk about installation, use, and administration across these different approaches.

Perhaps it’s best to step back for a second and look at the differences between the memory-as-disk approach compared to the memory-as-cache approach.

In the memory-as-disk approach administrators must decide what size memory footprint they need. This is far easier said than done because few tools exist to figure out the "active data set" within an existing LUN. So inevitably manual intervention in this process leads to gross over provisioning of memory resources for the entire data set as compared to the active, frequently used data.

The next task is to create and provision LUNs, and then migrate data to the new LUN(s). Once in place, the LUN must be protected with backup and recovery procedures, overall storage management, and the ongoing monitoring to determine if that data set is growing beyond the LUN size. If the data set exceeds the size of the LUN, administrators must be able to rapidly provision additional space (of the same speed and performance) while not disrupting the application.

In the memory-as-caching approach, the goal is to enhance the existing disk infrastructure as a seamless complement that delivers performance without creating additional maintenance and management items.

With caching, particularly when deployed as a network resource, applications request data from any amount of storage capacity, but will “view” that infinite capacity through a caching appliance. The network-based cache only retains the actively used data, dynamically populating the cache based on application requests and letting all unused (but important) data remain on disk-based, protected persistent storage.

By enhancing a traditional or clustered file system with a network-based cache, customers get the capacity depth of a their chosen file system, and the optimized performance of a cache sized perfectly to their active data set.

As workloads shift and change, the intelligence of a network-based caching appliance automatically adjusts to the data I/O patterns. This continuously alleviates hot-spots on and across systems.

Using Technology Efficiently

An important trigger for market growth and adoption is how to use more memory, more effectively in the data center. This memory can be in any shape or form but we need to turn our attention to the system level implementation and specifically how we do use memory to remove action items from the administrative to-do list, not add to it.

Using memory as a persistent storage device will tend to add way more to-do items than it might alleviate. Using memory as an intelligent cache will remove action items and migrate data centers to more automated, dynamic operation.

Stay tuned for the final installment Memory in the Data Center - part IV - Overall Data Center Impact