Optimizing Differentiated Latency in Multi-Tenant, Erasure-Coded Storage

Yu Xiang, Tian Lan, Vaneet Aggarwal, Yih-Farn Chen

Abstract: Erasure codes are widely used in distributed storage systems since they provide space-optimal data redundancy to protect against data loss. Despite recent progress on quantifying average service latency when erasure codes are employed, there is very little work on providing differentiated latency among multiple tenants that may have different latency requirements. This paper proposes a novel framework for providing and optimizing differentiated latency in erasure-coded storage by investigating two policies, weighted queue and priority queue, for scheduling tenant requests. For both policies, we quantify service latency for different tenant classes for homogeneous files with arbitrary placement and service time distributions. We develop an optimization framework that jointly minimizes differentiated latency over three decision spaces: 1) data placement; 2) request scheduling; and 3) resource management. Efficient algorithms harnessing bipartite matching and convex optimization techniques are developed to solve the proposed optimization. Our solution enables elastic service-level agreements to meet heterogeneous application requirements. We further prototype our solution with both queuing models applied in an open-source, cloud storage deployment that simulates three geographically distributed data centers through bandwidth reservations. Experimental results validate our theoretical delay analysis and show significant joint latency reduction for different classes of files, providing valuable insights into service differentiation and elastic quality of service in erasure-coded storage systems.