Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Chandrasekaran, K.Subject: search.filters.subject.Genetic algorithm

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    A novel family genetic approach for virtual machine allocation
    (Elsevier B.V., 2015) Joseph, C.T.; Chandrasekaran, K.; Cyriac, R.
    The concept of virtualization forms the heart of systems like the Cloud and Grid. Efficiency of systems that employ virtualization greatly depends on the efficiency of the technique used to allocate the virtual machines to suitable hosts. The literature contains many evolutionary approaches to solve the virtual machine allocation problem, a broad category of which employ Genetic Algorithm. This paper proposes a novel technique to allocate virtual machines using the Family Gene approach. Experimental analysis proves that the proposed approach reduces energy consumption and the rate of migrations, and hence offers much scope for future research. © 2015 Published by Elsevier B.V.
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    Survey on meta heuristic optimization techniques in cloud computing
    (Institute of Electrical and Electronics Engineers Inc., 2016) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
    Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on demand access to shared resources over the Internet in a self-service, dynamically scalable and metered manner. To reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map the job to resources that optimize more than one objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes long time to find an optimal solution. In cloud environment, it is best to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey on optimization algorithms for cloud environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and a novel technique: League Championship Algorithm (LCA). © 2016 IEEE.
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    Using genetic algorithm for process migration in multicore kernels
    (Springer Verlag service@springer.de, 2017) Shravya, K.S.; Deepak, A.; Chandrasekaran, K.
    Process migration is used in multicore operating systems to improve their performance. The implementation of the migration event contributes largely to the performance of the scheduling algorithm and hence decides how effective a multicore kernel is. There have been several effective algorithms which decide how a process can be migrated from one core to another in a multicore operating system. This paper looks further into the mechanism of process migration in multicore operating systems. The main aim of this paper is not to answer how the process migration should take place but it aims to answer when process migration should take place and to decide the site of process migration. For this, an artificial intelligence concept called genetic algorithm is used. Genetic algorithm works on the theory of survival of the fittest to find an optimally good solution during decision making phase. © Springer Nature Singapore Pte Ltd. 2017.