A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

Abstract

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm’s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

“…EPSOCHO uses standard benchmark worklogs to check the efficacy of an algorithm, and the results showed impact on existing approaches for mentioned parameters. The authors in [ 14 ] proposed a task-scheduling algorithm in IaaS clouds for a large scale of tasks. In this approach, the authors used the modified symbiotic search optimization technique by using a simplified mutualism process by using variations in respective arithmetic and geometric means of mutual vectors in the process of generation of next-generation solutions.…”

“…Cloudsim was used as simulating environment by considering standard configuration settings. In [ 11 , 12 , 13 , 14 , 15 , 16 ], the authors doscuss task scheduling with respect to the movement of data, task execution cost, penalty and the deadline of tasks. These are also crucial factors while scheduling tasks onto precise VMs so as to follow SLA accurately.…”

Design methodologies and engineering applications for ecosystem biomimicry: an interdisciplinary review spanning cyber, physical, and cyber-physical systems

“…Modifications of the standard SOS abound, including both hybridization and algorithmic improvements. As some examples, G_SOS [38] simplifies the algorithm's mutualism phase to enable cloud task scheduling applications. With Orthogonal SOS [45], Panda and Pani incorporate orthogonal array strategies to enhance the exploration capacity.…”

Task scheduling optimization considering SLA for big data analytics-as-a-service in cloud computing services

“…Task scheduling has been an important problem in the field of cloud services [1][2][3] . Most studies focus on independent tasks scheduling, such as proposing ant colony algorithms [4] , and whale optimization algorithms [5] to reduce costs [6,7] .…”