A Weighted Scoring Model of Heuristic-Based Workload Scheduling Approaches in Edge-Cloud Environments

Hybrid edge–cloud computing has emerged as a promising paradigm to meet the demands of latency-sensitive and resource-aware applications by combining the low-latency benefits of edge nodes with the scalability of cloud infrastructure. Efficient workload scheduling in such environments remains a critical challenge due to the heterogeneity of resources, dynamic network conditions, and diverse application requirements. This paper presents a comprehensive survey and comparative analysis of heuristic and metaheuristic scheduling algorithms tailored for edge–cloud systems. Seven representative algorithms, including Greedy Resource-Aware Heuristics (GRAH), Heterogeneous Earliest Finish Time (HEFT), Min-Min/Max-Min, Genetic Algorithm, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Tabu Search, are evaluated against seven key criteria: latency awareness, energy efficiency, scalability, scheduling accuracy, implementation complexity, resource utilization, and adaptability. The evaluation is literature-driven and structured through a Weighted Scoring Model (WSM), which synthesizes findings from prior simulation-based and experiment-based studies into a comparative framework. Results indicate that Greedy Resource-Aware Heuristics offer the best trade-off for real-time, dynamic scenarios, while optimization-based methods, like GA and Tabu Search, provide superior accuracy and resource balance at the cost of increased complexity. The findings highlight critical trade-offs and offer guidance on selecting appropriate scheduling strategies based on application-specific goals and system constraints.