Dynamic formation of service communities in the cloud under distribution and incomplete information settings. (4th October 2017)

• Record Type: Journal Article
• Authors: Khosrowshahi‐Asl, Ehsan; Bentahar, Jamal; Estrada, Rebeca; Otrok, Hadi; Mizouni, Rabeb; Khosravifar, Babak
• Other Names: Sangaiah Arun Kumar guestEditor.; Pham Hoang guestEditor.; Qiu Tie guestEditor.; Muhammad Khan guestEditor.; Awan Irfan guestEditor.; Younas Muhammad guestEditor.; Hussain Farookh guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-10-04
• Subjects: cloud services -- community of services -- distributed decision‐making; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4338
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Communities that gather functionally identical or complementary cloud services aim to provide better visibility, efficiency, and market share. This paper investigates the issue of forming these communities in distributed decision‐making settings under incomplete information. By incomplete information, we mean only partial information about the individual performance of cloud services within communities and about how they will behave within these communities is available. Forming communities in these particular settings is still an open problem. Most of the existing models require real‐time global knowledge about the services and high computational complexity, which makes the community formation extremely hard and time‐consuming. In this paper, we propose a strategic Distributed Decision‐making Mechanism (DDM) that regulates the cloud services decision‐making process. DDM first generates an initial set of data based on information obtained from existing cloud services regarding their single and cooperative efficiency. By analyzing this set and on the basis of a distance function, the decision‐making mechanism with regard to which community to form is implemented as a decision profile of strategies and their expected utility computed in terms of computational efficiency. DDM efficiently and systematically helps 1) communities find appropriate cloud services to invite as new members and 2) single services find suitable communities to join. To evaluate the proposed mechanism, we performed experiments using real data including 142 users and 4, 000 cloud services obtained from the CloudArmor, CloudHarmony, and WS‐DREAM datasets. The experimental results show that our algorithms outperform the existing solutions.