A Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique-Based Hybrid Optimisation Algorithm for Efficient Virtual Machine Migration and Task Scheduling
Keywords:
Deep Deterministic Policy Gradient (DDPG), Iterative Concept of War and Rat Swarm (ICWRS), Rat Swarm Optimizer (RSO), Multi-Agent Deep Learning, Adaptive Multi-Agent System, War Strategy Optimization (WSO).Abstract
TIn cloud computing settings, virtual machine (VM) migration and task scheduling are essential components that are meant to improve system efficiency and resource use. In this work, we present a unique method for effective task scheduling and virtual machine migration: the Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient (AMS-DDPG). To improve resource allocation in cloud settings, the AMS-DDPG strategy combines the advantages of Adaptive Multi-Agent and Deep Deterministic Policy Gradient (DDPG). In order to further improve the AMS-DDPG technique's performance, we provide an iterative idea of War and Rat Swarm (ICWRS). This idea does parameter optimization inside the AMS-DDPG approach using War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO). In order to effectively adjust the settings of the AMS-DDPG approach and enhance its convergence and performance, WSO and RSO simulate war and swarm tactics, respectively. Through the use of simulated cloud computing scenarios, different workload patterns and system configurations are taken into consideration while evaluating the suggested technique. Our test findings show that, in comparison to conventional techniques, the AMS-DDPG methodology with the ICWRS approach performs better. By optimizing VM migration and task scheduling, the AMS-DDPG approach increases overall system efficiency, lowers energy consumption, and improves resource usage. Furthermore, by presenting a thorough hybrid optimization approach that makes use of deep reinforcement learning and optimization strategies inspired by nature, our study adds to the rapidly expanding area of cloud computing. The possibility of merging numerous advanced approaches for tackling complicated resource allocation challenges in cloud systems is shown by the combination of DDPG, AMS-DDPG, ICWRS, WSO, and RSO. Future research aiming at using intelligent optimization techniques to improve the scalability and efficiency of cloud computing systems has a potential direction thanks to this study.
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