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An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment

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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.

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Abbreviations

VM:

Virtual machine

PCA:

Principal component analysis

HPSO-MGA:

Hybrid particle swarm optimization and modified genetic algorithm

Krill herd (LB):

Krill herd load balancing

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

MPCA:

Modified PCA

IDSA:

Improved differential search algorithm

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  1. Computer Science and Engineering, Hindustan Institute of Technology and Science, Chennai, TN, India

    Vadivel Ramasamy & SudalaiMuthu Thalavai Pillai

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  1. Vadivel Ramasamy
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  2. SudalaiMuthu Thalavai Pillai
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Correspondence to Vadivel Ramasamy.

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Ramasamy, V., Thalavai Pillai, S. An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment. Cluster Comput 23, 1711–1724 (2020). https://doi.org/10.1007/s10586-020-03118-x

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  • DOI: https://doi.org/10.1007/s10586-020-03118-x

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