An autonomous architecture based on reinforcement deep neural network for resource allocation in cloud computing

• Published in: Computing Vol. 106; no. 2; pp. 371 - 403
• Main Authors: Alizadeh Javaheri, Seyed Danial, Ghaemi, Reza, Monshizadeh Naeen, Hossein
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.02.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Cloud computing; Computer Appl. in Administrative Data Processing; Computer Communication Networks; Computer Science; Energy consumption; Heuristic methods; Information Systems Applications (incl.Internet); Infrastructure; Machine learning; Neural networks; Particle swarm optimization; Regular Paper; Resource allocation; Resource utilization; Software Engineering; Virtual environments; Autonomous system; Cloud computing; 68T07; Deep reinforcement neural network; Resource allocation; 68U01
• ISSN: 0010-485X; 1436-5057
• DOI: 10.1007/s00607-023-01220-7

Today, cloud computing technology has attracted the attention of many researchers. According to the needs of users to quickly execute requests and provide quality services, optimal allocation of resources and timing of task execution between virtual machines in cloud computing are of great importance. One of the important challenges that cloud service providers face is the effective management of resources by physical infrastructure. Therefore, in this paper, an autonomous system based on the Clipped Double Deep Q-Learning (CDDQL) Algorithm and the meta-heuristic Particle Swarm Optimization (PSO) for resource allocation is proposed in the Fog-cloud computing infrastructure. The PSO algorithm is used to prioritize the tasks and CDDQL is used as the core of the autonomous system (Auto-CDDQL) to allocate the desired VM resources to the tasks. The proposed Auto-CDDQL is implemented in the Fog and performs this process autonomously. By evaluating the results, it was observed that the amount of Make Span, response time, task completion, resource utilization, and energy consumption rate in the proposed AutoCDDQL on the c-hilo dataset, compared to the FCFS, RR, and PBTS methods, are significantly improved.