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

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.Comment: 11 pages, 7 figure

E2DR: Energy Efficient Data Replication in Data Grid

Abstract— Data grids are an important branch of gird computing which provide mechanisms for the management of large volumes of distributed data. Energy efficiency has recently emerged as a hot topic in large distributed systems. The development of computing systems is traditionally focused on performance improvements driven by the demand of client's applications in scientific and business domains. High energy consumption in computer systems leads to their limited performance because of the increased consumption of carbon dioxide and amount of electricity bills. Thus, the goal of design of computer systems has been shifted to power and energy efficiency. Data grids can solve large scale applications that require a large amount of data. Data replication is a common solution to improve availability and file access time in such environments. This solution replicates the data file in many different sites. In this paper, a new data replication method is proposed that is not only data aware, but also is energy efficient. Simulation results with CLOUDSIM show that the proposed method gives better energy consumption, average response time, and network usage than other algorithms and prevents the unnecessary creation of replica, which leads to efficient storage usage