Developing a Methodology for IOT Load Distribution within Edge Computing

Abstract

Optimizing task distribution and resource allocation becomes crucial with the exponential growth of IoT devices and the proliferation of edge computing. On the other hand, building such a flexible model about resources inside a heterogeneous climate is difficult. Also, the increasing demand for IoT services necessitated working to reduce the time delay by accomplishing successful load balancing. The objective of this study is to enhance load balancing by ensuring equitable allocation of resources among workloads, thereby enhancing Quality of Service (QOS) in cloud computing and minimizing processing time (PT), hence decreasing response time (RT). Our methodology presents a decentralized system with multiple agents that utilize the nodes in the edge and the cloud to distribute the workload caused by incoming tasks and the cost of performing those tasks. A collaborative model is followed to allocate the tasks to the resources to increase the utilization of available resources.