Shruthi G.; Monica R. Mundada; S. Supreeth; Bryan Gardiner

Deep Learning-based Resource Prediction and Mutated Leader Algorithm Enabled Load Balancing in Fog Computing

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Author(s)

Shruthi G. ^1,2,* Monica R. Mundada ³ S. Supreeth ² Bryan Gardiner ⁴

1. Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, VTU, Bengaluru, 590018, India

2. School of Computer Science and Engineering, REVA University, Bengaluru, 560064, India

3. Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bengaluru, 560054, India

4. School of Computing, Engineering and Intelligent Systems, Ulster University, Northlands Road, Magee Campus, BT48 7JL Londonderry, Northern Ireland, United Kingdom

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2023.04.08

Received: 14 Sep. 2022 / Revised: 7 Nov. 2022 / Accepted: 3 Mar. 2023 / Published: 8 Aug. 2023

Index Terms

Fog Computing, Mutated Leader Algorithm (MLA), Virtual Machine (VM), Deep Residual Network (DRN), Load Balancing

Abstract

Load balancing plays a major part in improving the performance of fog computing, which has become a requirement in fog layer for distributing all workload in equal manner amongst the current Virtual machines (VMs) in a segment. The distribution of load is a complicated process as it consists of numerous users in fog computing environment. Hence, an effectual technique called Mutated Leader Algorithm (MLA) is proposed for balancing load in fogging environment. Firstly, fog computing is initialized with fog layer, cloud layer and end user layer. Then, task is submitted from end user under fog layer with cluster of nodes. Afterwards, load balancing process is done in each cluster and the resources for each VM are predicted using Deep Residual Network (DRN). The load balancing is accomplished by allocating and reallocating the task from the users to the VMs in the cloud based on the resource constraints optimally using MLA. Here, the load balancing is needed for optimizing resources and objectives. Lastly, if VMs are overloaded and then the jobs are pulled from associated VM and allocated to under loaded VM. Thus the proposed MLA achieved minimum execution time is 1.472ns, cost is $69.448 and load is 0.0003% respectively.

Cite This Paper

Shruthi G., Monica R. Mundada, S. Supreeth, Bryan Gardiner, "Deep Learning-based Resource Prediction and Mutated Leader Algorithm Enabled Load Balancing in Fog Computing", International Journal of Computer Network and Information Security(IJCNIS), Vol.15, No.4, pp.84-95, 2023. DOI:10.5815/ijcnis.2023.04.08

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