scholarly journals Low-Energy-Orientated Resource Scheduling in Cloud Computing by Particle Swarm Optimization

2018 ◽  
Vol 36 (2) ◽  
pp. 339-344
Author(s):  
Jia Jia ◽  
Dejun Mu
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Energy Cost ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Multiobjective Optimization Problem ◽  
Swarm Optimization ◽  
Scheduling Method

In order to reduce the energy cost in cloud computing, this paper represents a novel energy-orientated resource scheduling method based on particle swarm optimization. The energy cost model in cloud computing environment is studied first. The optimization of energy cost is then considered as a multiobjective optimization problem, which generates the Pareto optimization set. To solve this multiobjective optimization problem, the particle swarm optimization is involved. The states of one particle consist of both the allocation plan for servers and the frequency plans on servers. Each particle in this algorithm obtains its Pareto local optimization. After the assembly of local optimizations, the algorithm generates the Pareto global optimization for one server plan. The final solution to our problem is the optimal one among all server plans. Experimental results show the good performance of the proposed method. Comparing with the widely-used Round robin scheduling method, the proposed method requires only 45.5% dynamic energy cost.

scholarly journals Evaluation of cloud computing resource scheduling based on improved optimization algorithm

2020 ◽  
Author(s):  
Huafeng Yu
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Operation Time ◽  
Swarm Optimization ◽  
Ipso Algorithm

Abstract Cloud computing, as a new computing mode in recent years, has been pursued by many users who have computational requirements, and the service quality of cloud computing depends largely on the efficiency of resource scheduling. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed to improve the efficiency of resource scheduling, and simulation experiments were carried out on the IPSO algorithm and the traditional particle swarm optimization using CloudSim simulation platform. The phenomenon of premature appeared with the increase of the number of iterations, and the globally optimal solution was not found. The IPSO algorithm was more efficient in exploring the globally optimal solution, and the phenomenon of premature did not appear. As the number of tasks increased, the operation time of both algorithms increased, but the IPSO algorithm increased more slowly. The IPSO algorithm had more advantages when there were a large amount of tasks. Virtual machines in the two algorithms had different loads, and the load of the virtual machine in the IPSO algorithm was more balanced.

scholarly journals Dynamic Multiaircraft Cooperative Suppression Interference Array Optimization by Dynamic MOPSO Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Huan Zhang ◽  
Rennong Yang ◽  
Changyue Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Air Defense ◽  
Multiobjective Particle Swarm Optimization ◽  
Dynamic Multiobjective Optimization

Dynamic multiaircraft cooperative suppression interference array (MACSIA) optimization problem is a typical dynamic multiobjective optimization problem. In this paper, the sum of the distance between each jamming aircraft and the enemy air defense radar network center and the minimum width of the safety area for route planning are taken as the objective functions. The dynamic changes in the battlefield environment are reduced to two cases. One is that the location of the enemy air defense radar is mobile, but the number remains the same. The other is that the number of the enemy air defense radars is variable, but the original location remains unchanged. Thus, two dynamic multiobjective optimization models of dynamic MACSIA are constructed. The dynamic multiobjective particle swarm optimization algorithm is used to solve the two models, respectively. The optimal dynamic MACSIA schemes which satisfy the limitation of the given suppression interference effect and ensure the safety of the jamming aircraft themselves are obtained by simulation experiments. And then verify the correctness of the constructed dynamic multiobjective optimization model, as well as the feasibility and effectiveness of the dynamic multiobjective particle swarm optimization algorithm in solving dynamic MACSIA problem.

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.

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