QoS-driven metaheuristic service composition schemes: a comprehensive overview

被引:16
作者
Masdari, Mohammad [1 ]
Nouzad, Mehdi [2 ]
Ozdemir, Suat [3 ]
机构
[1] Islamic Azad Univ, Dept Comp Engn, Urmia Branch, Orumiyeh, Iran
[2] Islamic Azad Univ, Dept Comp Engn, Marand Branch, Marand, Iran
[3] Hacettepe Univ, Dept Comp Engn, Ankara, Turkey
关键词
Metaheuristic; Service composition; Optimization; SOA; PSO; GA; Evolutionary algorithms; PARTICLE SWARM OPTIMIZATION; ANT COLONY OPTIMIZATION; GENETIC ALGORITHM; OPTIMAL-SELECTION; BEES ALGORITHM; SYSTEM; MODEL;
D O I
10.1007/s10462-020-09940-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Services Oriented Architecture provides Web Services (WSs) as reusable software components that can be applied to create more complicate composite services for users according to the specified QoS limitations. However, considering many WSs that may be appropriate for each task of a user-submitted workflow, finding the optimal WSs for a composite WS to maximize the overall QoS is an NP-hard problem. As a result, numerous composition schemes have been suggested in the literature to untangle this problem by using various metaheuristic algorithms. This paper presents a comprehensive survey and taxonomy of such QoS-oriented metaheuristic WS composition schemes provided in the literature. It investigates how metaheuristic algorithms are adapted for the WS composition problem and highlight their main features, advantages, and limitations. Also, in each category of the studied composition schemes, a comparison of their applied QoS factors, evaluated metrics, exploited simulators, and properties of the applied metaheuristic algorithms are explained. Finally, the concluding remarks and future research directions are summarized to help researchers in working in this area.
引用
收藏
页码:3749 / 3816
页数:68
相关论文
共 156 条
[1]   A Penalty-based Genetic Algorithm for QoS-Aware Web Service Composition with Inter-Service Dependencies and Conflicts [J].
Ai, Lifeng ;
Tang, Maolin .
2008 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING CONTROL & AUTOMATION, VOLS 1 AND 2, 2008, :738-743
[2]  
Akbaripour H, 2017, IMPERIALIST COMPETIT
[3]   Enhancement of Ant Colony Optimization for QoS-Aware Web Service Selection [J].
Alayed, Hashem ;
Dahan, Fadl ;
Alfakih, Taha ;
Mathkour, Hassan ;
Arafah, Mohammed .
IEEE ACCESS, 2019, 7 :97041-97051
[4]  
AllamehAmiri M., 2013, J AI DATA MINING, V1, P63, DOI DOI 10.22044/jadm.2013.97
[5]   QoS aware web service composition based on genetic algorithm [J].
Allameh Amiri M. ;
Serajzadeh H. .
2010 5th International Symposium on Telecommunications, IST 2010, 2010, :502-507
[6]   A review on simulation-based optimization methods applied to building performance analysis [J].
Anh-Tuan Nguyen ;
Reiter, Sigrid ;
Rigo, Philippe .
APPLIED ENERGY, 2014, 113 :1043-1058
[7]  
[Anonymous], 2018, INT J ADV MANUF TECH
[8]  
[Anonymous], 2010, P 12 INT C INF INT W
[9]  
[Anonymous], 2012, Int J Comput Appl
[10]   A View of Cloud Computing [J].
Armbrust, Michael ;
Fox, Armando ;
Griffith, Rean ;
Joseph, Anthony D. ;
Katz, Randy ;
Konwinski, Andy ;
Lee, Gunho ;
Patterson, David ;
Rabkin, Ariel ;
Stoica, Ion ;
Zaharia, Matei .
COMMUNICATIONS OF THE ACM, 2010, 53 (04) :50-58