TY - GEN
T1 - A two-fitness resource scheduling strategy based on improved particle swarm optimization
AU - Qiao, Xueming
AU - Chen, Meng
AU - Zhang, Xiangkun
AU - Zhu, Weiyi
AU - Liu, Yanhong
AU - Huo, Zhixin
AU - Sun, Ruiqi
AU - Zhu, Dongjie
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2020.
PY - 2020
Y1 - 2020
N2 - The performance optimization of cloud platform for big data processing is a research hotspot, among which resource scheduling is the most important. Through the analysis of the internal resource scheduling mechanism of CloudStack, the two-level scheduling of resources plays an important role in task optimal span, load balance and other aspects. In this paper, aiming at optimizing IaaS service performance and taking CloudStack platform as the research object, a dual fitness resource scheduling strategy based on improved particle swarm optimization is proposed. First of all, PSO algorithm with high precision and fast convergence speed is used to optimize the two-level resource scheduling, which can shorten the scheduling time when the scheduling requirements are met. Secondly, aiming at the problem of “prematurity” of particle swarm optimization (PSO), this paper USES simulated annealing algorithm to optimize the traditional PSO. Finally, aiming at the two pole resource scheduling, this paper proposes the virtual machine deployment algorithm based on improved particle swarm and the dual fitness task scheduling algorithm based on Improved Particle Swarm respectively, and carries out simulation in CloudSim simulation tool. The simulation results show that the algorithm proposed in this paper can effectively improve the optimal span and optimize the load balance.
AB - The performance optimization of cloud platform for big data processing is a research hotspot, among which resource scheduling is the most important. Through the analysis of the internal resource scheduling mechanism of CloudStack, the two-level scheduling of resources plays an important role in task optimal span, load balance and other aspects. In this paper, aiming at optimizing IaaS service performance and taking CloudStack platform as the research object, a dual fitness resource scheduling strategy based on improved particle swarm optimization is proposed. First of all, PSO algorithm with high precision and fast convergence speed is used to optimize the two-level resource scheduling, which can shorten the scheduling time when the scheduling requirements are met. Secondly, aiming at the problem of “prematurity” of particle swarm optimization (PSO), this paper USES simulated annealing algorithm to optimize the traditional PSO. Finally, aiming at the two pole resource scheduling, this paper proposes the virtual machine deployment algorithm based on improved particle swarm and the dual fitness task scheduling algorithm based on Improved Particle Swarm respectively, and carries out simulation in CloudSim simulation tool. The simulation results show that the algorithm proposed in this paper can effectively improve the optimal span and optimize the load balance.
KW - CLOUDSTACK
KW - Cloud computing
KW - IaaS
KW - Improved particle swarm
KW - Resource scheduling
UR - https://www.scopus.com/pages/publications/85092192852
U2 - 10.1007/978-981-15-9031-3_24
DO - 10.1007/978-981-15-9031-3_24
M3 - 会议稿件
AN - SCOPUS:85092192852
SN - 9789811590306
T3 - Communications in Computer and Information Science
SP - 263
EP - 277
BT - Security and Privacy in Social Networks and Big Data - 6th International Symposium, SocialSec 2020, Proceedings
A2 - Xiang, Yang
A2 - Liu, Zheli
A2 - Li, Jin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020
Y2 - 26 September 2020 through 27 September 2020
ER -