Efficient Resource Allocation in Cloud Computing using Adaptive Harris Hawks Approach

It is simple to keep an eye on the cloud service provider's memory, power, network bandwidth, and storage. Complex and large-scale computations in a cloud environment are successfully overcome by an effective technology called cloud computing. Commercial clients can increase or contract their resource utilization based on their needs thanks to cloud computing. However, because resources must be made accessible when an internet-based cloud application needs them, resource allocation is highly demanded in the cloud computing industry. Therefore, to solve such problems, an optimal resource allocation based on a multi-agent system is needed. When compared to other techniques like Reinforcement Learning Pro-Active Scalar (RLPAS) and (SFLA-FFA), the simulation results of the Multi-Agent Deep reinforcement learning-based Adaptive Harris Hawks Algorithm (MAD-HHA) demonstrate better outcomes. As the number of nodes increases, it is discovered that the Meta heuristic algorithm system produces superior outcomes. While FFA produces poor reliability of roughly 64%, the reliability analysis performed for 10 nodes is 76%. As the number of nodes increases, it is discovered that the Meta heuristic algorithm system produces superior outcomes. The examination of completed requests demonstrates that, in response to rising request counts, the Meta heuristic algorithm system outperforms FFA. During a 1000 request count, 200 unsuccessful requests were received, but FFA only manages a low reliability of roughly 210.