Technical gazette, Vol. 30 No. 1, 2023.
Original scientific paper
https://doi.org/10.17559/TV-20220712164019
An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment
Haoyang Du
; School of Economics and Management Beijing Jiaotong University, Beijing 100044, China; Henan University of Economics and Law, Zhengzhou 450046, China
Junhui Chen
; School of Economics and Management Beijing Jiaotong University, Beijing 100044, China; Henan University of Economics and Law, Zhengzhou 450046, China
Abstract
The new energy industry development is affected by many factors. Among them, the resources utilization ratio is a major reason for the low productivity of enterprises. As the core problem of cloud computing, the resource allocation problem has been widely concerned by the people, and the resource allocation problem of the new energy industry as the key to energy innovation and transformation should be more paid attention to. In multi-resource cloud computing scenarios, requests made by users often involve multiple types of resources. Traditional resource allocation algorithms have a single optimization object, typically time efficiency. In order to achieve cluster load balancing, utilization of system resources and improvement of system work efficiency, this paper proposes a new cloud computing allocation algorithm based on improved ant colony algorithm. According to the limit conditions of cloud computing environment and computing resources, this paper finds the shortest response time of all resource nodes and gets a set of best available nodes. This method can meet the quality requirements of cloud computing, and the task completion time of the improved algorithm is shorter, the number of algorithm iterations is less, and the load balancing effect is better. Through MATLAB simulation experiments, the effectiveness of the proposed method is verified.
Keywords
cloud computing; improved ant colony optimization; new energy industry; resource allocation
Hrčak ID:
288407
URI
Publication date:
15.12.2022.
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