Multi-tenant SaaS deployment optimisation algorithm for cloud computing environment

被引:2
|
作者
Cao Ming [1 ]
Yu Bingjie [1 ]
Liu Xiantong [1 ]
机构
[1] State Grid Hebei Informat & Telecommun Branch, Shijiazhuang 050000, Hebei, Peoples R China
关键词
software as a service; SaaS; multi-tenant; SPP; ant colony algorithm; ACO; MapReduce;
D O I
10.1504/IJIPT.2018.10015709
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article adopts MapReduce and multi-targeted ant colony algorithm (ACO) distribution in parallel to solve large-scaled service dynamic selection in SaaS and puts forward a service dynamic selection algorithm based on these technologies. The algorithm integrates cloud calculation technologies such as loading strategy, ACO, MapReduce, and HDFS, which deploys the service to servers as little as possible, to further save the energy target. Meanwhile, it also takes into account the smallest price target deployment and server loading balancing target, which transforms the global optimisation service dynamic selection into a multi-targeted service combination optimisation problem with QoS restriction. The simulation experiments verify and prove the feasibility, effectiveness and convergence of the improved algorithm.
引用
收藏
页码:152 / 158
页数:7
相关论文
共 50 条
  • [41] A Formal Model for Multi-tenant Software-as-a-Service in cloud computing
    Banerjee, Ansuman
    COMPUTE'2012, 2012,
  • [42] Traffic and Failure Aware VM Placement for Multi-tenant Cloud Computing
    Li, Xin
    Qian, Chen
    2015 IEEE 23RD INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS), 2015, : 41 - 50
  • [43] Adaptive Performance Isolation Middleware for Multi-tenant SaaS
    Walraven, Stefan
    De Borger, Wouter
    Vanbrabant, Bart
    Lagaisse, Bert
    Van Landuyt, Dimitri
    Joosen, Wouter
    2015 IEEE/ACM 8TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC), 2015, : 112 - 121
  • [44] Collaborative Network Security in Multi-Tenant Data Center for Cloud Computing
    Zhen Chen
    Wenyu Dong
    Hang Li
    Peng Zhang
    Xinming Chen
    Junwei Cao
    TsinghuaScienceandTechnology, 2014, 19 (01) : 82 - 94
  • [45] Optimized Cloud Deployment of Multi-tenant Software Considering Data Protection Concerns
    Mann, Zoltan Adam
    Metzger, Andreas
    2017 17TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), 2017, : 609 - 618
  • [46] Accommodating Multi-Tenant FPGAs in the Cloud
    Mbongue, Joel Mandebi
    Bobda, Christophe
    28TH IEEE INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES (FCCM), 2020, : 214 - 214
  • [47] Knowledge-Based Resource Allocation for Collaborative Simulation Development in a Multi-Tenant Cloud Computing Environment
    Peng, Gongzhuang
    Wang, Hongwei
    Dong, Jietao
    Zhang, Heming
    IEEE TRANSACTIONS ON SERVICES COMPUTING, 2018, 11 (02) : 306 - 317
  • [48] Policy-Driven Data Management Middleware for Multi-Cloud Storage in Multi-Tenant SaaS
    Rafique, Ansar
    Van Landuyt, Dimitri
    Lagaisse, Bert
    Joosen, Wouter
    2015 IEEE/ACM 2ND INTERNATIONAL SYMPOSIUM ON BIG DATA COMPUTING (BDC), 2015, : 78 - 84
  • [49] A partition model and strategy based on the Stoer–Wagner algorithm for SaaS multi-tenant data
    Xiaona Li
    Junli Zhao
    Yumei Ma
    Pingping Wang
    Hongyi Sun
    Yi Tang
    Soft Computing, 2017, 21 : 6121 - 6132
  • [50] Towards Dynamic Tenant Management for Microservice based Multi-Tenant SaaS Applications
    Kalra, Sumit
    Prabhakar, T. V.
    ISEC'18: PROCEEDINGS OF THE 11TH INNOVATIONS IN SOFTWARE ENGINEERING CONFERENCE, 2018,