Resource allocation in cloud auction-based market by hybrid optimization algorithm

Title

Resource allocation in cloud auction-based market by hybrid optimization algorithm

Creator

Geetha, P.; Padmavathy, T.; Vinodha, D.; Abirami, G.

Description

Effective resource allocation is essential in the rapidly changing cloud computing landscape to maximize provider revenue and user satisfaction. Through competitive bidding procedures, the auction-based market model has become a potent tool for allocating cloud resources among users. In this paper, a new method for cloud computing environments is presented: Double Auction-based Resource Allocation (DARA). The auction model and optimal resource allocation are the two main parts of the DARA methodology. The Double Auction mechanism is used as the auction model in the suggested DARA framework. In this model, resource prices and allocations are decided through a competitive auction process that involves both buyers and sellers.The highest price that buyers are willing to pay for resources is expressed in bids, and the lowest price that sellers are willing to accept is expressed in asks. There are many intricate tasks involved in this two-way auction process, including matching bids and asks, determining market prices, and handling transactions. Finding the equilibrium price requires the method to solve complex optimization problems in order to balance supply and demand. In order to overcome these obstacles, the study suggests the Hippopotamus Updated Pufferfish Optimization (HUPO) algorithm for the best possible resource distribution. The HUPO algorithm is made to handle limitations like truthfulness, resource density, execution time, and operating expenses. In order to ensure that users pay fair prices and service providers make the most money, it is crucial to implement effective resource allocation strategies that balance the cost of resources with their availability. According to the mean statistical metric, the resource density for the HUPO model is 17.862, which is greater than the values of all other traditional approaches, including BES at 14.960, AOA at 12.546, ACO at 14.274, COA at 13.693, SMO at 13.452, HOA at 13.686, and POA at 13.907. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

Source

Cluster Computing;Volume;29;Issue;1;Article No.;34;

Date

01-01-2026

Publisher

Springer

Subject

Bid-Ask matching; Cloud computing; Double auction; HUPO algorithm; Resource allocation

Coverage

Geetha P., Department of Computational Intelligence (CINTEL), School of Computing, SRM Institute of Science and Technology (KTR Campus), Tamil Nadu, Chennai, India; Padmavathy T., Department of Database Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, India; Vinodha D., Department of AIML and DS, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Abirami G., Department of Computer Science and Engineering, B.S.Abdur Rahman Crescent Institute of Science & Technology, GST Road, Tamil Nadu, Vandalur, Chennai, 600 048, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 13867857;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s10586-025-05767-2

https://www.scopus.com/pages/publications/105021945715?origin=resultslist