Efficient distribution of workloads plays a crucial role in fog computing environments, particularly for health monitoring systems that rely on real-time data processing and analysis. Fog computing expands upon cloud computing by decentralizing computational tasks to fog nodes located at the network edge. This proximity enables faster data processing, decreased latency, and enhanced scalability for time-sensitive applications like health monitoring. Similar to other research domains, fog computing faces various challenges, including the problem of load balancing. Numerous studies have been conducted to tackle this issue. However, there is still a need for further work to develop efficient systems that can operate on large-scale networks. Real-time application scheduling involves multiple complex factors, one of which is the tie-breaking problem. In the benchmark algorithm, the decision for application assignment is based on two critical parameters: communication and computing loads of the Base Stations and Fog nodes, respectively. The scheme deploys more fog nodes to reduce latency, but this may lead to tie situations where multiple Base Stations and fog nodes have the same minimum loads, causing network congestion. To address this problem, an efficient workload distribution approach for health monitoring systems that resolves tie situations and achieve improved workload distribution among fog nodes. Is proposed To evaluate the effectiveness of the approach, extensive simulations using the ifogsim toolkit and compared the results with the benchmark algorithm in terms of latency and network usage. The proposed algorithm significantly reduces latency and achieves better network usage compared to the benchmark algorithm.
Key words: efficient workload distribution - fog computing - health monitoring system, ifogsim
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