Scheduling methods compared for cloud fog and edge computing
Scheduling in Cloud,Fog, and Edge Computing: A Comparative Analysis and Decision Framework for Technique Selection
Distributed, Parallel, and Cluster Computing
Summary
Scheduling tasks in cloud, fog, and edge computing is complicated because these systems have different types of resources and changing conditions. The authors look at different approaches to scheduling, such as simple rules, complex optimization, machine learning, and combinations of these, comparing their strengths and weaknesses. They explain when each method works best depending on the needs like speed, accuracy, adaptability, and complexity. Finally, they create a guide to help pick the right scheduling method based on the situation and goals.
What this means in practice
- •For cloud infrastructure teams: Select scheduling techniques suited to specific cloud workloads, optimizing resource use and latency under stable conditions or complex demands.
- •For network operations centers: Choose adaptive scheduling methods that handle dynamic, uncertain conditions in fog and edge networks to maintain service quality.
A survey. It maps existing work.
Authors
Rana Ghazali, Douglas G. Down
Abstract
The increasing heterogeneity and dynamism of cloud, fog, and edge computing make scheduling a challenging problem involving diverse resource capabilities, workload characteristics, latency and energy constraints, and competing optimization objectives. Although numerous scheduling methods have been proposed, existing reviews often provide fragmented coverage of computing environments, scheduling granularities, or technique families, offering limited guidance on selecting an appropriate approach for specific requirements. This review presents a comparative analysis of task, job, and workflow scheduling across the cloud, fog, and edge paradigms, covering heuristic, metaheuristic, learning-based, and hybrid techniques. Studies published between 2020 and 2026 are analyzed according to scheduling granularity, computing environment, optimization objectives, constraints, performance metrics, and evaluation tools. The analysis highlights trade-offs among computational efficiency, optimization capability, adaptability, and implementation complexity across technique families. Heuristic methods offer low computational overhead for relatively stable scheduling conditions, whereas metaheuristic methods are suited to complex and multi-objective optimization problems. Learning-based methods provide greater adaptability to dynamic and uncertain environments but introduce data, training, inference, and convergence requirements. Hybrid methods combine complementary capabilities but generally increase computational and implementation complexity. Based on these findings, a decision framework is developed to guide technique selection according to workload characteristics, environmental conditions, optimization objectives, resource constraints, and computational requirements.