QoS-aware cloud service composition using eagle strategy

被引:107
|
作者
Gavvala, Siva Kumar [1 ]
Jatoth, Chandrashekar [2 ]
Gangadharan, G. R. [3 ]
Buyya, Rajkumar [4 ]
机构
[1] Univ Hyderabad, Hyderabad, India
[2] Koneru Lakshmaiah Educ Fdn, Hyderabad, India
[3] Natl Inst Technol, Tiruchirappalli, Tamil Nadu, India
[4] Univ Melbourne, Cloud Comp & Distributed Syst CLOUDS Lab, Melbourne, Vic, Australia
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2019年 / 90卷
基金
澳大利亚研究理事会;
关键词
Quality of Service (QoS); Cloud services; Service composition; Metaheuristic algorithm; Eagle strategy; KRILL HERD ALGORITHM; DIFFERENTIAL EVOLUTION; SEARCH ALGORITHM; CUCKOO SEARCH; OPTIMIZATION; TESTS;
D O I
10.1016/j.future.2018.07.062
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In recent years, several cloud services have proliferated that conspicuously result in providing similar services having same functionality by multiple service providers, but varying in Quality of Service (QoS) properties. Thus, providing a cloud service composition with optimal QoS values that satisfy the requirements of an user becomes complex and challenging in a cloud environment. Several metaheuristics proposed in solving this problem. However, many of them fail to maintain a suitable balance between exploration and exploitation. We propose a novel Eagle Strategy with Whale Optimization Algorithm (ESWOA) that ensures the proper balance between exploration and exploitation. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:273 / 290
页数:18
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