Title: Stream of traffic balance in active cloud infrastructure service virtual machines using ant colony

Authors: Ankita Taneja; Hari Singh; Suresh Chand Gupta

Addresses: CSE Department, Panipat Institute of Engineering and Technology, Panipat, Haryana, India ' CSE&IT Department, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India ' CSE Department, Panipat Institute of Engineering and Technology, Panipat, Haryana, India

Abstract: Cloud load balancing is the manner of distributing computing resources and workloads over a cloud computing infrastructure. It allows an enterprise to manage workloads through appropriate resource allocation in the cloud. Various load balancing techniques in cloud computing are reviewed and the work presented in this paper thoroughly analyses and compares two well-known algorithms in MATLAB, the ant colony optimisation (ACO) algorithm and genetic algorithm (GA). The objective is to produce an optimal solution for cost and execution time through balancing the workload. It is observed through experimental observations that ACO-based load balancing possess incurs low cost and low execution time as compared to the GA for a constant workload over a fixed number of cloud machines. However, the execution time follows a different trend when workload increases and more machines are utilised to handle the increased workload, it rises sharply in ACO as compared to the GA.

Keywords: ant colony optimisation; ACO; genetic algorithm; GA; pheromone matrix; pheromone table; load balancing; cloud computing; infrastructure as a service; IAAS.

DOI: 10.1504/IJCC.2020.112315

International Journal of Cloud Computing, 2020 Vol.9 No.4, pp.373 - 396

Received: 21 Aug 2018
Accepted: 18 Jul 2019

Published online: 08 Jan 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article