An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment
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An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment Vadivel Ramasamy1 • SudalaiMuthu Thalavai Pillai1 Received: 18 February 2020 / Revised: 22 March 2020 / Accepted: 20 April 2020 Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user’s tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one. Keywords Virtual machine (VM) Dynamic resource allocation Modified principle component analysis (MPCA) Hybrid particle swarm optimization-modified genetic algorithm (HPSO-MGA) Abbreviations VM Virtual machine PCA Principal component analysis HPSO-MGA Hybrid particle swarm optimization and modified genetic algorithm Krill herd Krill herd load balancing (LB) VMM Virtual machine monitors DPRA Dynamic power-saving resource allocation PM Physical machine SLA Service level-agreement DBN Deep belief networks OVMAP Online incentive-compatible mechanism PSO Particle swarm optimization & Vadivel Ramasamy [email protected] SudalaiMuthu Thalavai Pillai [email protected] 1
Computer Science and Engineering, Hindustan Institute of Technology and Science, Chennai, TN, India
MPCA IDSA
Modified PCA Improved differential search algorithm
1 Introduction Cloud computing has emerged as the most popular paradigm for on-demand, pay-per-use model of computing [1] The basic principle of cloud computing is that user data is not stored locally but is stored in the data center of the internet. So the user can access the data anywhere at any time by using API which is provided by the cloud [2]. Cloud computing entrusts services with a
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