Continuous Retrieval of Replicated Data from Heterogeneous Storage Arrays

Replicated declustering techniques reduce response times of disk requests by distributing data among multiple disks and retrieving in parallel. Efficient retrieval of replicated data from multiple disks is a challenging problem, especially for heterogeneous storage architectures receiving continuous disk requests. Existing techniques either do not consider the heterogeneity of the disks or handle the requests in a discrete manner assuming the storage system is idle. In this paper, we focus on continuous retrieval techniques in heterogeneous storage architectures to minimize the response time of disk requests considering waiting time and service time of the requests as well as the execution time of the retrieval algorithm. We investigate multiple trade-offs between these three factors affecting the response time of a disk request and propose a maximum flow based adaptive retrieval strategy. Performance of the proposed and existing continuous retrieval techniques are evaluated using simulations driven by real world traces and various homogeneous and heterogeneous multi-disk storage configurations.