A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm

被引:104
|
作者
Naseri, Afshin [1 ]
Navimipour, Nima Jafari [1 ]
机构
[1] Islamic Azad Univ, Tabriz Branch, Dept Comp Engn, Tabriz, Iran
关键词
Cloud computing; Service composition; Cloud mobile agent; Particle swarm optimization; MODEL; MANAGEMENT;
D O I
10.1007/s12652-018-0773-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Cloud computing as a new computing paradigm has a great capacity for storing and accessing the remote data and services. Presently, many organizations decide to reduce the burden of local resources and support them by outsourcing the resources to the cloud. Typically, scalable resources are provided as services over the Internet. The way of choosing appropriate services in the cloud computing is done by determining the different Quality of Service (QoS) parameters to perform optimized resource allocation. Therefore, service composition as a developing approach combines the existing services to increase the number of cloud applications. Independent services can be integrated into complex composited services through service composition. In this paper, a new hybrid method is proposed for efficient service composition in the cloud computing. The agent-based method is also used to compose services by identifying the QoS parameters and the particle swarm optimization (PSO) algorithm is employed for selecting the best services based on fitness function. The simulation results have shown the performance of the method in terms of reducing the combined resources and waiting time.
引用
收藏
页码:1851 / 1864
页数:14
相关论文
共 50 条
  • [31] A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition
    Bhushan, S. Bharath
    Reddy, Pradeep C. H.
    INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH, 2018, 15 (02) : 1 - 20
  • [32] A QoS-Aware Service Optimization Method Based on History Records and Clustering
    Meng Shunmei
    Liu Zhenxing
    Dou Wanchun
    SECOND INTERNATIONAL CONFERENCE ON CLOUD AND GREEN COMPUTING / SECOND INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING AND ITS APPLICATIONS (CGC/SCA 2012), 2012, : 89 - 96
  • [33] QoS-aware Service Composition Using Fuzzy Set Theory and Genetic Algorithm
    Jiajun Xu
    Lin Guo
    Ruxia Zhang
    Hualang Hu
    Fei Wang
    Zhiyuan Pei
    Wireless Personal Communications, 2018, 102 : 1009 - 1028
  • [34] A QoS-Aware Service Composition Mechanism in the Internet of Things Using a Hidden-Markov-Model-Based Optimization Algorithm
    Sefati, Seyedsalar
    Navimipour, Nima Jafari
    IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (20) : 15620 - 15627
  • [35] QoS-aware Service Composition Using Fuzzy Set Theory and Genetic Algorithm
    Xu, Jiajun
    Guo, Lin
    Zhang, Ruxia
    Hu, Hualang
    Wang, Fei
    Pei, Zhiyuan
    WIRELESS PERSONAL COMMUNICATIONS, 2018, 102 (02) : 1009 - 1028
  • [36] An adaptive algorithm for QoS-aware service composition in grid environments
    Luo J.-Z.
    Zhou J.-Y.
    Wu Z.-A.
    Service Oriented Computing and Applications, 2009, 3 (3) : 217 - 226
  • [37] AN improved Ant Colony Optimization Algorithm for QoS-Aware Dynamic Web Service Composition
    Zhao Shanshan
    Ma Lin
    Wang Lei
    Wen Zepeng
    2012 INTERNATIONAL CONFERENCE ON INDUSTRIAL CONTROL AND ELECTRONICS ENGINEERING (ICICEE), 2012, : 1998 - 2001
  • [38] QoS-aware Service Composition using HTN Planner
    Song, Yue
    Sun, Qibo
    Zhou, Ao
    Wang, Shangguang
    Li, Jinglin
    2018 IEEE 8TH INTERNATIONAL SYMPOSIUM ON CLOUD AND SERVICE COMPUTING (SC2), 2018, : 107 - 110
  • [39] A Dynamic QoS-Aware Logistics Service Composition Algorithm Based on Social Network
    Yu, Yang
    Chen, Jian
    Lin, Shangquan
    Wang, Ying
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 2014, 2 (04) : 399 - 410
  • [40] Optimal international logistics service composition algorithm based on improved particle swarm optimization algorithm in cloud environment
    Zhang Li
    Wu Yuchen
    Deng Kai
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 35 (03) : 2793 - 2803