Irfan Ahmad

Application and Storage caches in modern systems must be manually tuned and sized in response to changing application’s workload. A balance must be achieved between cost, performance and revenue loss from cache sizing mis-matches. However, caches are inherently nonlinear systems making this exercise equivalent to solving a maze in the dark.
Until now!

Caches in modern storage systems lack the ability to adapt automatically and optimize for dynamic workload mixes. Despite the potential for huge improvements in cost, performance, and predictability, such adaptability is extremely challenging, due to inherently complex, non-linear, and workload-dependent behavior. Even when manually-tunable controls are provided to support dynamic cache sizing, partitioning, and parameter tuning, administrators simply don’t have the information required to make good decisions.

It is well-known that cache performance is non-linear in cache size and the benefit of caches varies widely by workload. Irrespective of whether the cache is in a storage system, database or application tier, no two real workload mixes have the same cache behavior! Existing techniques for profiling workloads don’t measure data reuse, nor do they predict changes in performance as cache allocations are varied. Recently, a new, revolutionary set of techniques have been discovered for online cache optimization.

It is well-known that storage cache performance is non-linear in cache size and the benefit of caches varies widely by workload. This means that no two real workload mixes have the same cache behavior! Existing techniques for profiling workloads don’t measure data reuse, nor does they predict changes in performance as cache allocations are varied. Since caches are a scarce resource, workload-aware cache behavior profiling is highly valuable with many applications.

The benefits of storage caches are notoriously difficult to model and control, varying widely by workload, and exhibiting complex, nonlinear behaviors. However, recent advances make it possible to analyze and optimize high-performance storage caches using lightweight, continuously-updated miss ratio curves (MRCs). Previously relegated to offline modeling, MRCs can now be computed so inexpensively that they are practical for dynamic, online cache management, even in the most demanding environments.

Is your VM is rightsized?
Is your VM is on the right datastore?
Is your VM I/O bound?

These are the kinds of questions that come up frequently when you’re doing capacity management, solving performance problems, and making procurement decisions. The root of the answer to all of them is the shape of your workload. Irfan Ahmad, the tech lead of VMware’s own Swap-to-SSD, Storage DRS and Storage I/O Control features will teach you the five steps to discovering the shape of your workload and applying that knowledge to capacity and performance decisions.

The benefits of storage caches are notoriously difficult to model and control, varying widely by workload, and exhibiting complex, nonlinear behaviors. However, recent advances make it possible to analyze and optimize high-performance storage caches using lightweight, continuously-updated miss ratio curves (MRCs). Previously relegated to offline modeling, MRCs can now be computed so inexpensively that they are practical for dynamic, online cache management, even in the most demanding environments.