Real-Time Task Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization

2015 ◽  
pp. 141-152 ◽  
Author(s):  
Huangning Chen ◽  
Wenzhong Guo
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Real Time ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Time Task ◽  
Task Scheduling Algorithm

A Novel Task-Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization

2021 ◽  
Vol 13 (2) ◽  
pp. 1-15
Author(s):  
Zhou Wu ◽  
Jun Xiong
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Computing System ◽  
Swarm Optimization ◽  
Task Scheduling Algorithm ◽  
Cloud Computing System

With the characteristics of low cost, high availability, and scalability, cloud computing has become a high demand platform in the field of information technology. Due to the dynamic and diversity of cloud computing system, the task and resource scheduling has become a challenging issue. This paper proposes a novel task scheduling algorithm of cloud computing based on particle swarm optimization. Firstly, the resource scheduling problem in cloud computing system is modeled, and the objective function of the task execution time is formulated. Then, the modified particle swarm optimization algorithm is introduced to schedule applications' tasks and enhance load balancing. It uses Copula function to explore the relation of the random parameters random numbers and defines the local attractor to avoid the fitness function to be trapped into local optimum. The simulation results show that the proposed resource scheduling and allocation model can effectively improve the resource utilization of cloud computing and greatly reduce the completion time of tasks.

scholarly journals Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Weizhe Zhang ◽  
Hucheng Xie ◽  
Boran Cao ◽  
Albert M. K. Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Real Time ◽  
Task Scheduling ◽  
Particle Swarm ◽  
Energy Aware ◽  
Swarm Optimization ◽  
Heterogeneous Multiprocessors ◽  
Time Task ◽  
Feasible Solutions

Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO) based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

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