An Optimization of Makespan, Energy Consumption, and Load Balancing on the Task Scheduling in Cloud Computing using Particle Swarm Optimization (PSO)
Fajar Kusumaningayu1, Antoni Wibowo2
1Fajar Kusumaningayu, Binus Graduate Program – Master of Computer Science Bina Nusantara University , Anggrek Campus Jl. Kebon Jeruk Raya No. 27, Kebon Jeruk, West Jakarta, Indonesia.
2Antoni Wibowo, Binus Graduate Program – Master of Computer Science Bina Nusantara University , Anggrek Campus Jl. Kebon Jeruk Raya No. 27, Kebon Jeruk, West Jakarta, Indonesia.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 26, 2019. | Manuscript published on 30 November, 2019. | PP: 3040-3049 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7738118419/2019©BEIESP | DOI: 10.35940/ijrte.D7738.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cloud computing is widely used resource sharing computational technology to provide fast, reliable, and scalable computational process for organizations and companies without the need to build and maintain their own server. The research area about cloud computing is dynamic and versatile. One may have concern on the privacy, security, networking, optimization, etc. Due to huge demand for cloud computing, it creates several problems such as makespan, energy consumption, and load balancing. Task scheduling is one of the technologies that have been applied to solve those objectivities. However, task scheduling is one of the well-known NP-hard problems, and it is difficult to find the optimum solution. In order to solve this problem, previous studies have utilized meta-heuristic method to find the best solution based on the solution spaces. This study proposed Particle Swarm Optimization (PSO) to solve the multi-objective task scheduling to achieve the optimum solution. The effectiveness of the proposed algorithm will be compared with Genetic Algorithm (GA), Clonal Selection Algorithm (CSA), and Bat Algorithm (BA). This study converts three objectivities into single objectivity optimization with each objectivity act as variable assigned with weight that present its priority and has implemented those meta-heuristics. The simulation result from ten data set shows that PSO able to outperform GA, CSA, and BA especially for makespan and energy consumption without the cost of algorithm duration since PSO has fast convergence rate compare to the other three algorithms and making it a good choice for dynamic task scheduling in data center cloud computing where the algorithm duration is one of important factor.
Keywords: Cloud Computing, Multi-Objectivities, Particle Swarm Optimization, Task Scheduling.
Scope of the Article: Cloud Computing.