Title: Cloud resource management using adaptive firefly algorithm and artificial neural network

Authors: S.K. Manigandan; S. Manjula; V. Nagaraju; D. Ramya; B.R. TapasBapu

Addresses: Department of Computer Science and Engineering, VelTech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai, India ' Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India ' Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India ' Department of Information Technology, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India ' Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, India

Abstract: Cloud computing can be characterised in basic terms as one of the stages for giving conveyed figuring assets. Registering assets may incorporate capacity, data transfer capacity, memory space, handling components, etc. These assets are leased to the clients utilising the compensation per-utilised model. The interest for the assets may not be static, yet they can be mentioned on-request because of the developing web office. The clients of the cloud want to have a limited reaction time and cost yet then again, the cloud supplier centres on accomplishing proficient cloud assets distribution and limiting the support costs. Resource management is a practice of provisioning and managing the cloud resources efficiently. It also provides the techniques to provision the resources, schedule the jobs, and balance the loads. This proposal provides a resource management technique for efficient provisioning of resources and the scheduling of jobs on the static and dynamic cloudlet requests.

Keywords: cloud computing; cloudlet; adaptive firefly algorithm; AFFA; artificial neural network; ANN; virtual machine; physical machine; service level agreement; resource management; scheduling allocation; coldspot and hotspot.

DOI: 10.1504/IJCC.2022.128693

International Journal of Cloud Computing, 2022 Vol.11 No.5/6, pp.480 - 491

Received: 19 Dec 2019
Accepted: 28 Mar 2020
Published online: 02 Feb 2023 *

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