Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment (original) (raw)
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Symbiotic Organism Search optimization based task scheduling in cloud computing environment
Efficient task scheduling is one of the major steps for effectively harnessing the potential of cloud computing. In cloud computing, a number of tasks may need to be scheduled on different virtual machines in order to minimize makespan and increase system utilization. Task scheduling problem is NP-complete, hence finding an exact solution is intractable especially for large task sizes. This paper presents a Discrete Symbiotic Organism Search (DSOS) algorithm for optimal scheduling of tasks on cloud resources. Symbiotic Organism Search (SOS) is a newly developed metaheuristic optimization technique for solving numerical optimization problems. SOS mimics the symbiotic relationships (mutualism, commensalism, and parasitism) exhibited by organisms in an ecosystem. Simulation results revealed that DSOS outperforms Particle Swarm Optimization (PSO) which is one of the most popular heuristic optimization techniques used for task scheduling problems. DSOS converges faster when the search gets larger which makes it suitable for large-scale scheduling problems. Analysis of the proposed method conducted using t-test showed that DSOS performance is significantly better than that of PSO particularly for large search space.
An Enhanced Discrete Symbiotic Organism Search Algorithm for Optimal Task Scheduling in the Cloud
Algorithms
Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain number of tasks are scheduled to use diverse resources (virtual machines) to minimise the makespan and achieve the optimum utilisation of the system by reducing the response time within the cloud environment. The task scheduling problem is NP-complete; as such, obtaining a precise solution is difficult, particularly for large-scale tasks. Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. Our proposed algorithm is an extension of the standard symbiotic organism search (SOS), a nature-inspired algorithm that has been implemented to solve various numeric...
Sensors, 2022
The search algorithm based on symbiotic organisms' interactions is a relatively recent bioinspired 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.
Journal of Network and Computer Applications (ISI & Scopus indexed, IF = 3.991), 2019
In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The performance of the proposed CMSOS algorithm is evaluated on CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, the performance of the proposed CMSOS algorithm was found to be competitive with the existing with the existing multi-objective task scheduling optimization algorithms. The CMSOS algorithm obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) with no computational overhead. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery.