Mean latency optimization in erasure-coded distributed storage systems

Modern distributed storage systems represent an elegant solution to accommodate the exponentially increasing need of storage space. They often use erasure coding because of its high resiliency with low storage overhead. Further, distributed data-center applications are known to be susceptible to long response time, and higher latency leads to reduction in customers' quality of experience. However, to the best of our knowledge, quantifying the impact of erasure coding on th mean latency is still an open problem for distributed erasure-coded storage. In this paper, we propose a framework for quantifying and optimizing the mean latency in erasure-coded storage systems. In particulate, we characterize mean latency in a tight upper bound. Then, we formulate an optimization problem to optimize the mean latency of all files over the choice of storage servers and auxiliary bound parameters. An alternating optimization algorithm is used to efficiently solve the nonconvex problem. Our evaluation results show the superiority of our approach as compared to the state of the art algorithms.