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Intelligent scheduling and replication: a synergistic approach
Author(s) -
Elghirani Ali,
Subrata Riky,
Zomaya Albert Y.
Publication year - 2008
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1354
Subject(s) - computer science , replica , job scheduler , scheduling (production processes) , distributed computing , grid , data grid , tabu search , grid computing , execution time , data management , replication (statistics) , job shop scheduling , database , cloud computing , schedule , operating system , engineering , statistics , art , operations management , geometry , mathematics , artificial intelligence , visual arts
In large‐scale data‐intensive applications data play a pivotal role in the execution of these applications, and data transfer is a primary cause of job execution delay. In environments such as the data grids where execution of jobs that require large amounts of data is undertaken, a smart collaborative environment between the scheduling and data management services to achieve a synergistic effect on the performance of the grid becomes essential. This paper presents an intelligent data grid framework where job scheduling and data and replica management are coupled to provide an integrated environment for efficient access to data and job scheduling. The data management service predicts and estimates the appropriate locations of replica and proactively replicates the data sets in these locations while the intelligent tabu‐search‐based scheduler incorporating information about the data sets dispatches the jobs to the sites expected to facilitate minimum job execution time and better overall system utilization. Evaluation of the framework shows significant improvement in the performance of the grid and job execution time. Copyright © 2008 John Wiley & Sons, Ltd.