Efficient retrieval of replicated data from multiple disks is a challenging problem. Traditional retrieval techniques assume that replication is done at a single site using homogeneous disk arrays having no initial load or network delay. Recently, generalized retrieval algorithms are proposed to cover heterogeneous disk arrays, initial loads, and network delays. Generalized retrieval algorithms achieve the optimal response time retrieval schedule by performing multiple runs of a maximum flow algorithm. Since the maximum flow algorithm is used as a black box technique, flow values of the previous runs cannot be conserved to speed up the process. In this paper, we propose integrated maximum flow algorithms for the generalized optimal response time retrieval problem. Our first algorithm uses Ford-Fulkerson method and the second algorithm uses Push-relabel algorithm. Besides the sequential implementations, a multi-threaded version of the push-relabel algorithm is also implemented. Proposed algorithms are investigated using various replication schemes, query types, query loads, disk specifications, and system delays. Experimental results show that the sequential integrated push-relabel algorithm runs up to 2.5X faster than the black box version. Furthermore, parallel integrated push-relabel implementation achieves up to 1.7X speed up (∼1.2X on average) over the sequential algorithm using two threads, which makes the integrated algorithm up to 4.25X (∼3X on average) faster than its black box counterpart.

Declustering techniques reduce query response times through parallel I/O by distributing data among parallel disks. Recently, replication-based approaches were proposed to further reduce the response time. Efficient retrieval of replicated data from multiple disks is a challenging problem. Existing retrieval techniques are designed for storage arrays with identical disks, having no initial load or network delay. In this article, we consider the generalized retrieval problem of replicated data where the disks in the system might be heterogeneous, the disks may have initial load, and the storage arrays might be located on different sites. We first formulate the generalized retrieval problem using a Linear Programming (LP) model and solve it with mixed integer programming techniques. Next, the generalized retrieval problem is formulated as a more efficient maximum flow problem. We prove that the retrieval schedule returned by the maximum flow technique yields the optimal response time and this result matches the LP solution. We also propose a low-complexity online algorithm for the generalized retrieval problem by not guaranteeing the optimality of the result. Performance of proposed and state of the art retrieval strategies are investigated using various replication schemes, query types, query loads, disk specifications, network delays, and initial loads.

No abstract

No abstract

Abstract