NDLI: Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments

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Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments

Content Provider	Springer Nature Link
Author	Mansouri, Najme
Copyright Year	2014
Abstract	Data Grid integrates graphically distributed resources for solving data intensive scientific applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Scheduling is a traditional problem in parallel and distributed system. However, due to special issues and goals of Grid, traditional approach is not effective in this environment any more. Therefore, it is necessary to propose methods specialized for this kind of parallel and distributed system. Another solution is to use a data replication strategy to create multiple copies of files and store them in convenient locations to shorten file access times. To utilize the above two concepts, in this paper we develop a job scheduling policy, called hierarchical job scheduling strategy (HJSS), and a dynamic data replication strategy, called advanced dynamic hierarchical replication strategy (ADHRS), to improve the data access efficiencies in a hierarchical Data Grid. HJSS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. Moreover, due to the limited storage capacity, a good replica replacement algorithm is needed. We present a novel replacement strategy which deletes files in two steps when free space is not enough for the new replica: first, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, number of intercommunications, number of replications, hit ratio, computing resource usage and storage usage.
Starting Page	391
Ending Page	408
Page Count	18
File Format	PDF
ISSN	20952228
Journal	Frontiers of Computer Science in China
Volume Number	8
Issue Number	3
e-ISSN	20952236
Language	English
Publisher	Higher Education Press
Publisher Date	2014-03-06
Publisher Institution	Chinese Universities
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	data replication data grid optorSim job scheduling simulation Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Job scheduling and dynamic data replication in data grid environment

57 analysis of scheduling and replica optimisation strategies for data grids using optorsim.

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Analysing Scheduling and Replica Optimisation Strategies for Data Grids with OptorSim (2004)

Analysing Scheduling and Replica Optimisation Strategies for Data Grids with OptorSim (2004)

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A Threshold-based Dynamic Data Replication and Parallel Job Scheduling strategy to enhance Data Grid

Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments

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Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments