OPTIMIZED TASK SCHEDULING IN FOG-CLOUD ENVIRONMENTS USING A COST-AWARE GENETIC ALGORITHM

Cloud technology offers flexible computing and storage solutions over the internet. However, for latency-sensitive applications such as smart healthcare and smart cities, the reliance on centralized cloud data centers leads to significant performance issues, particularly in terms of delay. Fog and edge computing paradigms aim to address these issues by bringing resources closer to end-users, thus reducing latency and enhancing energy efficiency. In this paper, we propose a cost-aware, genetic-based (CAG) task scheduling algorithm tailored for fog-cloud environments, which seeks to improve cost efficiency for real-time applications with strict deadlines. Our approach is implemented and evaluated using the PureEdgeSim simulator, focusing on key performance metrics such as latency, network congestion, and cost. The results demonstrate that the proposed algorithm surpasses existing techniques like Round-Robin and Trade-off algorithms, achieving superior performance in terms of cost and throughput efficiency. These results demonstrate that the CAG algorithm is an effective solution for task scheduling in fog-cloud environments, offering better cost and deadline management compared to existing methods. This research has significant implications for latency-sensitive IoT applications, such as smart healthcare and smart cities, where efficient resource allocation and cost optimization are critical.