Spider Monkey Optimization Algorithm for
Load Balancing in Cloud Computing
Environments
Sawsan Alshattnawi and
Mohammad AL-Marie
Department of Computer
Science, Yarmouk University, Jordan
Abstract: Scheduling of tasks is one of the main concerns in
the Cloud Computing environment. The whole system performance depends on the
used scheduling algorithm. The scheduling objective is to distribute tasks between the Virtual Machines and
balance the load to prevent any virtual machine from being overloaded while other
is underloaded. The problem of scheduling is considered an NP-hard optimization
problem. Therefore, many heuristics have been proposed to solve this problem up
to now. In this paper, we propose a new Spider Monkeys algorithm for load
balancing called Spider Monkey Optimization Inspired Load Balancing (SMO-LB)
based on mimicking the foraging behavior of Spider Monkeys. It aims to balance
the load among virtual machines to increase the performance by reducing
makespan and response time. Experimental results show that our proposed method
reduces tasks' average response time to 10.7 seconds compared to 24.6 and 30.8
seconds for Round Robin and Throttled methods respectively. Also, the makespan
was reduced to 21.5 seconds compared to 35.5 and 53.0 seconds for Round Robin
and Throttled methods respectively.
Keywords: Cloud computing, load balancing,
metaheuristic optimization, spider monkeys optimization, tasks scheduling.
Received April 1, 2020; accepted January
6, 2021