Multi-Tenant Cloud Service Composition using Evolutionary Optimization

被引:0
作者
Kumar, Satish [1 ]
Bahsoon, Rami [1 ]
Chen, Tao [2 ]
Li, Ke [3 ]
Buyya, Rajkumar [4 ]
机构
[1] Univ Birmingham, Sch Comp Sci, Birmingham, W Midlands, England
[2] Nottingham Trend Univ, Dept Comp & Technol, Nottingham, England
[3] Univ Exeter, Dept Comp Sci, Exeter, Devon, England
[4] Univ Melbourne, Sch Comp & Informat Syst, Melbourne, Vic, Australia
来源
2018 IEEE 24TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS 2018) | 2018年
关键词
Multi-Tenant SaaS; Service Composition; Quality of Service; Evolutionary Optimization; QOS; ALGORITHM;
D O I
10.1109/ICPADS.2018.00129
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In Software as a Service (SaaS) cloud marketplace, several functionally equivalent services tend to be available with different Quality of Service (QoS) values. For processing end users multi-dimensional QoS and functional requirements, the application engineers are required to choose suitable services and optimize the service composition plans for each category of users. However, existing approaches for dynamic services composition tend to support execution plans that search for service provisions of equivalent functionalities with varying QoS or cost constraints to meet the tenants' QoS requirements or to dynamically respond to changes in QoS. These approaches tend to ignore the fact that multi-tenant execution plans need to provide variant execution plans, each offering a customized plan for a given tenant with its functionality, QoS and cost requirements. Henceforth, the dynamic selection and composition of multi-tenant service composition is a NP-hard dynamic multi objective optimization problem. To address these challenges, we propose a novel multi-tenant middleware for dynamic service composition in the SaaS cloud. In particular, we present new encoding representation and fitness functions that model the service selection and composition as an evolutionary search. We incorporate our approach with two Multi-Objective Evolutionary Algorithms (MOEA), i.e., MOEA/D-STM and NSGA-II, to perform a comparative study. The experiment results show that the MOEA/D-STM outperforms NSGA-II in terms of quality of solutions and computation time.
引用
收藏
页码:972 / 979
页数:8
相关论文
共 26 条
  • [1] Approximation quality of the hypervolume indicator
    Bringmann, Karl
    Friedrich, Tobias
    [J]. ARTIFICIAL INTELLIGENCE, 2013, 195 : 265 - 290
  • [2] A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing
    Chen, Fuzan
    Dou, Runliang
    Li, Minqiang
    Wu, Harris
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2016, 99 : 423 - 431
  • [3] On the Effects of Seeding Strategies: A Case for Search-based Multi-Objective Service Composition
    Chen, Tao
    Li, Miqing
    Yao, Xin
    [J]. GECCO'18: PROCEEDINGS OF THE 2018 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2018, : 1419 - 1426
  • [4] Chen Y., 2016, IEEE T CLOUD COMPUTI
  • [5] Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition
    Cremene, Marcel
    Suciu, Mihai
    Pallez, Denis
    Dumitrescu, D.
    [J]. APPLIED SOFT COMPUTING, 2016, 39 : 124 - 139
  • [6] A fast and elitist multiobjective genetic algorithm: NSGA-II
    Deb, K
    Pratap, A
    Agarwal, S
    Meyarivan, T
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) : 182 - 197
  • [7] Gartner Inc, 2018, GARTN FOR WORLDW PUB
  • [8] He Q., 2012, 2012 IEEE 5 INT C CL, P566
  • [9] Jatosh C., 2018, FUTURE GENEATION COM, P1008
  • [10] Computational Intelligence Based QoS-Aware Web Service Composition: A Systematic Literature Review
    Jatoth, Chandrashekar
    Gangadharan, G. R.
    Buyya, Rajkumar
    [J]. IEEE TRANSACTIONS ON SERVICES COMPUTING, 2017, 10 (03) : 475 - 492