Efficient Resource Allocation in Fog Computing Using Multi Metric AIS Optimization

Abstract

Cloud computing is a new era technology, which is entirely dependent on the internet to maintain large applications, where data is shared over one platform to provide better services to clients belonging to different organizations. It ensures maximum utilization of computational resources by making availability of data, software and infrastructure with at a lower cost in a secure, reliable and flexible manner. Fog computing is emerging as a powerful and popular computing paradigm, which extends cloud computing paradigm to enable the service execution in the edge network. The mobile and IoT (Internet of Things) applications could choose the computing nodes in both fog and cloud for resource provisioning. Generally, load balancing is one of the key factors to achieve resource efficiency and avoid bottleneck, overloaded and low-load resource usage. Load balancing is an important aspect of fog networks that avoids a situation with some under-loaded or overloaded fog nodes. Quality of Service (QoS) parameters such as resource utilization, cost, response time, performance, and energy consumption can be improved with load balancing. Recently, several studies have been conducted on load balancing in fog computing. This paper introduces an Artificial Immune System (AIS)-based load balancer for efficient resource allocation in fog computing, utilizing a multi-metric fitness function incorporating CPU and RAM availability, energy efficiency, and proximity. A mutation mechanism enhances adaptability and prevents allocation bias. the results reveal that the AIS achieves 41.67% better resource allocation efficiency compared to Random Load Balancer and up to 30.77% improvement over Least connection and Round Robin strategies. The AIS model effectively balances CPU utilization, latency, and energy consumption, providing a robust and scalable solution for dynamic fog networks.