An Adaptive SDN-Based Load Balancing Method for Edge/Fog-Based Real-Time Healthcare Systems

Abstract

Edge/fog computing has gained significant popularity as a computing paradigm that facilitates real-time applications, especially in healthcare systems. However, deploying these systems in real-world healthcare scenarios presents technical challenges, among which load balancing is a key concern. Load balancing aims to distribute workloads evenly across multiple nodes in a network to optimize processing and communication efficiency. This article proposes an adaptive load-balancing method that combines the strengths of static and software-defined networking (SDN)-based load balancing algorithms for edge/fog-based healthcare systems. A new algorithm called load balancing of optimal edge-server placement (LB-OESP) is proposed to balance the workload statically in the systems, followed by the presentation of an SDN-based greedy heuristic (SDN-GH) algorithm to manage the data flow dynamically within the network. The LB-OESP algorithm effectively balances workloads while minimizing the number of edge servers required, thereby improving system performance and saving costs. The SDN-GH algorithm leverages the benefits of SDN to dynamically balance the load and provide a more efficient system. Simulation results demonstrate that the proposed method provides an adaptive load-balancing solution that takes into consideration changing network conditions and ensures improved system performance and reliability. Furthermore, the proposed method offers a 12% reduction in system latency and up to 28% lower deployment costs compared to the previous studies. The proposed method is a promising solution for edge/fog-based healthcare systems, providing an efficient and cost-effective approach to managing workloads.