Adaptive policies for balancing performance and lifetime of mixed SSD arrays through workload sampling

Solid-state drives (SSDs) have become promising storage components to serve large I/O demands in modern storage systems. Enterprise class (high-end) SSDs are faster and more resilient than client class (low-end) SSDs but they are expensive to be deployed in large scale storage systems. It is an attractive and practical alternative to exploit the high-end SSDs as a cache and low-end SSDs as main storage. This paper explores how to optimize a mixed SSD array in terms of performance and lifetime. This paper shows that simple integration of different classes of SSDs in traditional caching policies results in poor reliability. This paper also reveals that caching policies with static workload distribution are not always efficient. In this paper, we propose a sampling based adaptive approach that achieves fair workload distribution across the cache and the storage. The proposed algorithm enables fine-grained control of the workload distribution which minimizes latency over lifetime of mixed SSD arrays. We show that our adaptive algorithm is very effective in improving the latency over lifetime metric, on an average, by up to 2.36 times over LRU, across a number of workloads.