The Impact of Data Replicatino on Job Scheduling Performance in Hierarchical data Grid

TL;DR

This paper presents a hierarchical replication strategy and job scheduling policy to reduce data access time and improve job performance in data grid environments, demonstrating a 12% improvement over existing methods.

Contribution

The paper introduces a novel dynamic data replication strategy called HRS and a job scheduling policy tailored for hierarchical data grids, enhancing data access efficiency.

Findings

Achieved a 12% improvement in data access efficiency.

Developed a new hierarchical replication strategy (HRS).

Validated approach through simulation studies.

Abstract

In data-intensive applications data transfer is a primary cause of job execution delay. Data access time depends on bandwidth. The major bottleneck to supporting fast data access in Grids is the high latencies of Wide Area Networks and Internet. Effective scheduling can reduce the amount of data transferred across the internet by dispatching a job to where the needed data are present. Another solution is to use a data replication mechanism. Objective of dynamic replica strategies is reducing file access time which leads to reducing job runtime. In this paper we develop a job scheduling policy and a dynamic data replication strategy, called HRS (Hierarchical Replication Strategy), to improve the data access efficiencies. We study our approach and evaluate it through simulation. The results show that our algorithm has improved 12% over the current strategies.