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|dc.contributor.author||Sowmya, Kamath S.||-|
|dc.identifier.citation||Lecture Notes in Electrical Engineering, 2014, Vol.284 LNEE, , pp.81-92||en_US|
|dc.description.abstract||The problem of efficiently scheduling jobs on several machines is an important consideration for Cloud computing. Task scheduling in Cloud Environment is a recognised NP-hard problem and hence methods that focus on producing an exact solution can prove insufficient in finding an optimal resolution to JSSP. Hence, in such cases, heuristic methods can be employed to find a good solution within reasonable time. In this paper, we study the conventional ACO algorithm and propose two Load Balancing ACO algorithms for task scheduling in Cloud Environment. We also present the observed results, and discuss them with reference to the FCFS scheduling algorithm currently used. It is observed that the proposed algorithm gives better results for every problem size. Also the proposed algorithms are adapted and applied to Task scheduling in Cloud Environment and is found to give better results. � 2014 Springer International Publishing Switzerland.||en_US|
|dc.title||An equal share ant colony optimization algorithm for job shop scheduling adapted to cloud environments||en_US|
|Appears in Collections:||2. Conference Papers|
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