journal article·2021Open access

Negotiation Based Combinatorial Double Auction Mechanism in Cloud Computing

Zakir Ullah, Asif Umer, Mahdi Zaree, Jamil Ahmad, Faisal Alanazi, Noor Ul Amin, Arif Iqbal Umar, Ali Imran Jehangiri, Muhammad Adnan

Computers, Materials & Continua· Volume 69· Issue 2

Abstract

Cloud computing is a demanding business platform for services related to the field of IT. The goal of cloud customers is to access resources at a sustainable price, while the goal of cloud suppliers is to maximize their services utilization. Previously, the customers would bid for every single resource type, which was a limitation of cloud resources allocation. To solve these issues, researchers have focused on a combinatorial auction in which the resources are offered by the providers in bundles so that the user bids for their required bundle. Still, in this allocation mechanism, some drawbacks need to be tackled, such as due to the lower average bid price the users are dropped from the auction process. To solve this problem, we proposed a “Negotiation based Combinatorial Double Auction Mechanism for Resource Allocation (N-CDARA) in cloud computing”. The proposed method negotiates with dropped users. Lower average bid price users are asked by our proposed mechanism to increase their bids, as by the quoted bids they will be dropped by the auctioneer. Most of the users that are close to winning accept the proposal and increase their bid prices. The proposed mechanism is implemented in a CloudSim simulation toolkit. Results are compared with the latest model and performance study shows that in our proposed scheme more users win and get their requested services and the utilization of offered services is increased up to 18.4% than the existing schemes.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Cloud Computing and Resource Management · Blockchain Technology Applications and Security · Auction Theory and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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