Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Abstract

Abstract-Since the beginning of cloud computing technology, task scheduling problem has never been an easy work. Because of its NP-complete problem nature, a large number of task scheduling techniques have been suggested by different researchers to solve this complicated optimization problem. It is found worth to employ heuristics methods to get optimal or to arrive at near-optimal solutions. In this work, a combination of two heuristics algorithms was proposed: particle swarm optimization (PSO) and genetic algorithm (GA). Firstly, we list pros and cons of each algorithm and express its best interest to maximize the resource utilization. Secondly, we conduct a performance comparison approach based on two most critical objective functions of task scheduling problems which are execution time and computation cost of tasks in cloud computing. Thirdly, we compare our results with other existing heuristics algorithms from the literatures. The experimental results was examined with benchmark functions and results showed that the particle swarm optimization (PSO) performs better than genetic algorithm (GA) but they both present a similarity because of their population based search methods. The results also showed that the proposed hybrid models outperform the standard PSO and reduces dramatically the execution time and lower the processing cost on the computing resources.

TD‐PSO: Task distribution approach based on particle swarm optimization for vehicular ad hoc network

“…Therefore, the TD-PSO can determine the near-optimal solution in a minimal amount of time compared with the GA. Third, our approach did not contain any overlapping and mutation calculations as it is PSO-based. 31 In summary, the TD-PSO outperformed the GA in terms of computational efficiency.…”

“…Several schemes were proposed to manage and schedule the tasks in a heterogeneous environment. [30][31][32] Zhao et al 33 proposed a performance impact algorithm for task-distribution algorithms to solve the time-critical problem. Several scholars have proposed schemes based on heuristic algorithms to reduce operational costs.…”

Finding Minimum and Maximum Values of Variables in Mathematical Equations by Applying Firefly and PSO Algorithm

“…To explain the work of the algorithm, it is assumed that each single solution is a bird within the distance of search is called particle. All particles have positions and velocities that leads these flying components that fly within the space of problem through following the best component so far [10]. Swarm represents a possible solution for the problem that can be determined by fitness function.…”

Particle swarm optimization for solving thesis defense timetabling problem

“…The PSO algorithm has excellent robustness and useful in different application environments with little modification [6]. The PSO algorithm also delivers the same optimal solution than other algorithms with faster computing time and a faster convergence rate than other algorithms, such as the genetic TELKOMNIKA Telecommun Comput El Control  Particle swarm optimization for solving thesis defense timetabling proble (Gilbert Christopher) 763 algorithm [7]. PSO algorithm also successfully implemented in some computer science problem, such as knapsack problem [8,9] and job-shop problem [10,11] and some real-life cases, such as optimization of reservoir operation [12], task scheduling in grid [13,14] resource-constrained project scheduling [15], cloud computing [7,16,17], and employee scheduling [18].…”