NDLI: Predictive job scheduling in a connection limited system using parallel genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Neduncheliyan, S. Pramod, S.
Copyright Year	2007
Description	Author affiliation: Dept. of Comput. Sci. & Eng.(M.E), Anna Univ., Tiruchirapalli (Neduncheliyan, S.; Pramod, S.)
Abstract	Job scheduling is the key feature of any computing environment and the efficiency of computing depends largely on the scheduling technique used. Intelligence is the key factor which is lacking in the job scheduling techniques of today. Genetic algorithms are powerful search techniques based on the mechanisms of natural selection and natural genetics. Multiple jobs are handled by the scheduler and the resource the job needs are in remote locations. Here we assume that the resource a job needs are in a location and not split over nodes and each node that has a resource runs a fixed number of jobs. The existing algorithms used are non predictive and employs greedy based algorithms or a variant of it. The efficiency of the job scheduling process would increase if previous experience and the genetic algorithms are used. In this paper, A new technique is proposed as a model of the scheduling algorithm where the scheduler can learn from previous experiences and an effective job scheduling is achieved as time progresses.
Starting Page	560
Ending Page	564
File Size	450905
Page Count	5
File Format	PDF
ISBN	9781424413553
DOI	10.1109/ICIAS.2007.4658450
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-25
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Processor scheduling Genetics Scheduling Encoding History Biological cells Genetic algorithms
Content Type	Text
Resource Type	Article

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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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