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
Energy Efficient Load Balancing Aware Task Scheduling in Cloud Computing using MultiObjective Chaotic Darwinian Chicken Swarm Optimization
