Conference Papers

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    A modified Ant Colony optimization algorithm with load balancing for job shop scheduling
    (IEEE Computer Society help@computer.org, 2013) Chaukwale, R.; Kamath S․, S.S.
    The problem of efficiently scheduling jobs on several machines is an important consideration when using Job Shop scheduling production system (JSP). JSP is known to be a NP-hard problem and hence methods that focus on producing an exact solution can prove insufficient in finding an optimal resolution to JSP. 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 a Load Balancing ACO algorithm for JSP. We also present the observed results, and discuss them with reference to the conventional ACO. It is observed that the proposed algorithm gives better results when compared to conventional ACO. © 2013 IEEE.
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    GWOTS: Grey Wolf Optimization Based Task Scheduling at the Green Cloud Data Center
    (Institute of Electrical and Electronics Engineers Inc., 2018) Natesha, B.V.; Sharma, N.; Domanal, S.; Guddeti, R.M.
    Task Scheduling is a key challenging issue of Infrastructure as a Service (IaaS) based cloud data center and it is well-known NP-complete problem. As the number of users' requests increases then the load on the cloud data center will also increase gradually. To manage the heavy load on the cloud data center, in this paper, we propose multiobjective Grey Wolf Optimization (GWO) technique for task scheduling. The main objective of our proposed GWO based scheduling algorithm is to achieve optimum utilization of cloud resources for reducing both the energy consumption of the data center and total makespan of the scheduler for the given list of tasks while providing the services as requested by the users. Our proposed scheduling algorithm is compared with non meta-heuristic algorithms (First-Come-First-Serve (FCFS) and Modified Throttle (MT)), and meta-heuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO)). Experimental results demonstrate that the proposed GWO based scheduler outperforms all algorithms considered for performance evaluation in terms of makespan for the list of tasks, resource utilization and energy consumption. © 2018 IEEE.