zahidraza@mail.jnu.ac.in. AbstractâScheduling of jobs in distributed environment is proven to be NP-complete and has been addressed by various heuristics ...
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%DWFK6FKHGXOLQJ0RGHOIRU'LVWULEXWHG6\VWHPV 7DM$ODP 'HSDUWPHQWRI&RPSXWHU6FLHQFH -D\SHH,QVWLWXWHRI,QIRUPDWLRQ7HFKQRORJ\ 1RLGD,QGLD WDMDODP#MLLWDFLQ &RUUHVSRQGLQJ$XWKRU Abstract²6cheduling of jobs in distributed environment is proven to be NP-complete and has been addressed by various heuristics and metaheuristic approaches. It is always desired from a scheduling policy to dispense the load evenly on the available resources so as to have maximum utilization of resources while meeting the scheduling objective(s). Metaheuristics for job scheduling have gained immense popularity due to their effectiveness in providing near optimal solution in a reasonable time. This paper presents the working of batch scheduling model based on bacterial foraging. The model generates the schedule minimizing the node idle time and the makespan while exhibiting a balanced load distribution. Keywords— distributed system; scheduling; load balancing; bacterial foraging; utilization; makespan.
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7KHDOJRULWKPIRUWKHVDPHLVSUHVHQWHGEHORZ BFBSM () { Initialize No. of Processors M & Tasks N; Initialize the BFO parameters: Population, S, Nc, Nre, Sr, Ned, Ped; Generate ETC Matrix using N, M Generate Initial Population by Random Allotment IRUHOO 1HG ^ Elimination Loop IRUN 1UH ^ Reproduction Loop IRUM 1F ^ Chemotaxis Loop IRUL 6 Current Population ^ Each bacteria evaluates the current location using the fitness function using equation (ii) Save the Best Makespan Next, generate a random direction via Tumble identifying the task to be remapped Move the selected task to other processors till fitness value improves: ` ` Chemotaxis loop ends here Sort the population according to Health
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