Optorsim: A Grid Simulator for Studying Dynamic Data Replication Strategies

TLDR

Computational grids solve large problems on small data sets, whereas data grids handle large computational tasks that generate massive data, and replication—creating geographically dispersed copies—is a key optimization to reduce access costs, with OptorSim offering a modular framework to study such strategies under varied grid configurations. The paper discusses several replication algorithms and aims to explore the stability and transient behavior of selected optimization techniques. OptorSim, a modular grid simulator, was used to design and implement the study, enabling analysis of various replication algorithms across different grid workloads and configurations. The study details OptorSim’s design and implementation and analyzes various replication algorithms across different grid workloads.

Abstract

Computational grids process large, computationally intensive problems on small data sets. In contrast, data grids process large computational problems that in turn require evaluating, mining and producing large amounts of data. Replication, creating geographically disparate identical copies of data, is regarded as one of the major optimization techniques for reducing data access costs. In this paper, several replication algorithms are discussed. These algorithms were studied using the Grid simulator: OptorSim. OptorSim provides a modular framework within which optimization strategies can be studied under different Grid configurations. The goal is to explore the stability and transient behaviour of selected optimization techniques. We detail the design and implementation of OptorSim and analyze various replication algorithms based on different Grid workloads.