Current perspective of symbiotic organisms search technique in cloud computing environment: a review

Nature-inspired algorithms in computer science and engineering are algorithms that take their inspiration from living things and imitate their actions in order to construct functional models. The SOS algorithm (symbiotic organisms search) is a new promising metaheuristic algorithm. It is based on the symbiotic relationship that exists between different species in an ecosystem. Organisms develop symbiotic bonds like mutualism, commensalism, and parasitism to survive in their environment. Standard SOS has since been modified several times, either by hybridization or as better versions of the original algorithm. Most of these modifications came from engineering construction works and other discipline like medicine and finance. However, little improvement on the standard SOS has been noticed on its application in cloud computing environment, especially cloud task scheduling. As a result, this paper provides an overview of SOS applications in task scheduling problem and suggest a new enhanced method for better performance of the technique in terms of fast convergence speed

Convergence-based task scheduling techniques in cloud computing: A review

The cloud computing promises various benefits that are striking to establishments and consumers of their services. These benefits encourage more business establishments, institutes, and users in need of computing resources to move to the cloud because of efficient task scheduling. Task scheduling is a means by which the tasks or job specified by users are mapped to the resources that execute them. Task scheduling problems in cloud, has been considered as a hard Nondeterministic Polynomial time (Np-hard) optimization problem. Task Scheduling is use to map the task to the available cloud resources like server, CPU memory, storage, and bandwidth for better utilization of resource in cloud. Some of the problems in the task scheduling include load-balancing, low convergence issues, makespan, etc. Convergence in task scheduling signifies a point in the search space that optimize an objective function. The non-independent tasks has been scheduled based on some parameters which includes makespan, response time, throughput and cost. In this paper, an extensive review on existing convergence based task scheduling techniques was carried out spanning through 2015 to 2019. This review would provide clarity on the current trends in task scheduling techniques based on convergence issues and the problem solved. It is intended to contribute to the prevailing body of research and will assist the researchers to gain more knowledge on task scheduling in cloud based on convergence issues

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

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