A new multi-agent group-buying auction for automated VM-to-Customer mapping. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Rad Jahanbani, Puria; Adabi, Sepideh; Rezaee, Ali
• Is Part Of: Journal of organizational computing and electronic commerce. Volume 31:Issue 1(2021)
• Page Start: 35
• Page End: 58
• Publication Date: 2021-01-02
• Subjects: Cloud computing -- dynamic pricing -- auction -- multi-agent system -- group-buying -- clustering -- resource allocation; Management -- Data processing -- Periodicals; Electronic commerce -- Periodicals; Electronic data interchange -- Periodicals; Business enterprises -- Computer networks -- Periodicals; 658.0505
• DOI: 10.1080/10919392.2020.1838847
• Languages: English
• ISSNs: 1091-9392

Abstract

ABSTRACT: This paper aims to allocate cloud resources (i.e., Virtual Machines: VM) to customers by using an auction-based multi-agent system. Adding the concept of group-buying to auction makes the possibility of having more winners. Group-buying offers resources at a reduced price on the condition that the number of purchases reaches a minimum number. Subsequently, the rate of successful deals, the speed of reaching agreements and monetary utility of the participants (i.e., providers and customers) will be increased. Due to having a massive number of clients with different characteristics and providers with various VM types in the cloud environment, it is obligatory to make the process of group-buying auction fully automated. By doing this automation, customers will be relieved of serious troubles such as coalition for group-buying, comparing the resources, bidding at auctions and so on. Three main requirements of this automation are as follows: (1) clustering similar customers in rational groups, (2) selecting the most suitable resource for each customer/group of customers, and (3) bidding during auctions on behalf of each customer/group of customers. A particular algorithm for each one of these requirements is presented in this paper. Through simulation experiments, the results of evaluations showed how significantly the proposed cloud market system improved the success rate of customers, the average time taken in resource selection, the average of providers' financial utility, and the average of customers' financial utility.