Combining Data Replication Algorithms and Job Scheduling Heuristics in the Data Grid

Tang, Ming; Lee, Bu-Sung; Tang, Xueyan; Yeo, Chai Kiat

doi:10.1007/11549468_45

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…They used OptorSim for simulation and proved, it provides better performance compared with other algorithms. In [7] Ming Tang, Bu-Sung LeeX, et al, evaluated various replication algorithms performance with various simulations and proved it reducing the job turnaround time remarkably. In [8] TaoWang, Shihong Yao et al, prosed a well designed dynamic replication strategy which consists of replica replacement algorithm, replica layout algorithm and replica selection algorithm.…”

Section: A Related Workmentioning

confidence: 99%

Efficient Data Management in Grid Computing using DMDR Algorithm

Prema.¹

2019

IJRASET

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Replication is a widely used method in data grid which aims to reduce bandwidth consumption, improve response time and maintain reliability. In data grid, for performance enhancement file correlations become an important consideration. The scrutiny of actual data intensive grid applications suggests that correlations can be broken for improving the efficiency of replication strategies and acknowledge that job requests for group of correlated files. In this paper an algorithm namely, Data mining based dynamic replication algorithm (DMDR) has been implemented, which consider a set of files as granularity. This work collects files based on a relationship of concurrent accesses between files by jobs as well as stores related files at the same time. So DM (Data Mining) is introduced to find out these correlations. All confidence measure is chosen for correlation measure.

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Section: A Related Workmentioning

confidence: 99%

Efficient Data Management in Grid Computing using DMDR Algorithm

Prema.¹

2019

IJRASET

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“…Genetic Algorithm based scheduler is introduced for decomposable Data Grid applications [13]. The performance comparison of a centralized and distributed dynamic replication algorithm in combination with different grid scheduling heuristics, Shortest Turnaround Time (STT), Least Relative Load (LRL) and DP (Data Present) is presented in [14]. [15] developed the Hierarchical Cluster Scheduling (HCS) algorithm and the Hierarchical Replication Strategy (HRS), which maximize the required data in a region in order to fetch replicas faster.…”

Section: Related Workmentioning

confidence: 99%

“…Various dynamic replication strategies within a simulated grid environment are discussed in [7] [8] and their combination with scheduling algorithms are studied in [9][10] [14]. Job scheduling and data replication policies for central and tier model organization of data grids have been simulated based on the Grid Datafarm architecture [11].…”

Section: Related Workmentioning

confidence: 99%

JS2DR2: An Effective Two-Level Job Scheduling Algorithm and Two-Phase Dynamic Replication Strategy for Data Grid

Suri

Singh²

2009

2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies

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Data grid supports a variety of scientific applications that require access to large amount of data with various qualities of service requirements. Ensuring efficient access to such large and widely distributed datasets is hindered by high latencies. Effective scheduling in grid can reduce the amount of data transferred among nodes by dispatching a job to node where most of the required data is available. In addition to efficient scheduling, dynamic replication strategies are regarded as one of the major optimization technique for reducing access latency. In this paper, we propose a two-phase dynamic replication strategy coupled with two-stage job scheduling to provide an integrated environment for efficient access to data and job scheduling. Simulation results demonstrate that 2 2

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“…In Tang et al (2005), the performances of the data replication algorithms combined with different Grid scheduling heuristics were evaluated through simulations. Network and storage capacities limits both the number of replicas that may be made as well as the number of jobs that may use each replica (Thain et al, 2001).…”

Section: Related Workmentioning

confidence: 99%

Adaptive hierarchical scheduling policy for enterprise grid computing systems

Abawajy

2009

Journal of Network and Computer Applications

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Combining Data Replication Algorithms and Job Scheduling Heuristics in the Data Grid

Cited by 9 publications

References 5 publications

Efficient Data Management in Grid Computing using DMDR Algorithm

Efficient Data Management in Grid Computing using DMDR Algorithm

JS2DR2: An Effective Two-Level Job Scheduling Algorithm and Two-Phase Dynamic Replication Strategy for Data Grid

Adaptive hierarchical scheduling policy for enterprise grid computing systems

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