A Hybrid Optimized Intelligent Resource-Constrained Service Scheduling for Unified IoT Applications in Smart Cities

As the Internet of Things (IoT) continues to advance as a technology, it has given rise to innovative and cross-domain IoT applications, particularly in smart cities. For IoT applications and services that are sensitive to latency and due resource constraints it affects the Quality of Service (QoS). To address these challenges, context-aware fog computing at the network edge requires an enhanced focus on optimizing resources for intelligent service management. Due to the dynamic change of workload at fog nodes, i.e., sudden rise in demand, an effective load balancing approach among fog nodes becomes essential. However, it's crucial to execute load transfers, such as Virtual Machine (VM) migrations but improper migration can lead to a cascade of migrations and ultimately degrade system performance. In this paper, we introduce a resource-optimized intelligent service model (RoISM) designed to facilitate resource optimization through a forecasting technique. This technique predicts the requisite context instances and resource computation needed for efficient service delivery. The proposed hybrid approach to service management leverages context-sharing, context-migration, and live service migration strategies, all based on the forecast method. This method utilizes both current and predicted resource utilization data, as well as context availability, to fulfil service requests within the specified latency requirements for cross-domain IoT applications. To validate the effectiveness of our proposed service management algorithms, we conducted simulations using a CloudSim simulator. The results obtained from these simulations confirm the superiority of our proposed methods