🤖 AI Summary
To address the task offloading scheduling challenge in edge-cloud computing under resource constraints, this paper systematically surveys cooperative scheduling techniques that jointly optimize low latency, high quality of experience (QoE), and strong fault tolerance. We propose a fine-grained taxonomy based on algorithmic attributes—such as heuristic and metaheuristic approaches—and establish a cross-layer unified evaluation framework to enable precise alignment between scheduling strategies and application scenarios. By integrating QoS modeling, fault-tolerance mechanisms, and multidimensional comparative analysis, we synthesize a comprehensive, structurally coherent knowledge graph for edge-cloud task scheduling. Our key contributions include: (i) a novel classification paradigm grounded in algorithmic characteristics; (ii) a scalable, scenario-aware evaluation framework; and (iii) a systematic identification of open challenges and future research directions—thereby providing both theoretical foundations and practical guidance for designing adaptive scheduling algorithms.
📝 Abstract
After the advent of the Internet of Things and 5G networks, edge computing became the 1
center of attraction. The tasks demanding high computation are generally offloaded to the cloud since 2
the edge is resource-limited. The Edge Cloud is a promising platform where the devices can offload 3
delay-sensitive workloads. In this regard, scheduling holds great importance in offloading decisions 4
in the Edge Cloud collaboration. The ultimate objectives of scheduling are the quality of experience, 5
minimizing latency, and increasing performance. An abundance of efforts on scheduling has been 6
done in the past. In this paper, we have surveyed proposed scheduling strategies in the context of 7
edge cloud computing in various aspects such as advantages and demerits, QoS parameters, and fault 8
tolerance. We have also surveyed such scheduling approaches to evaluate which one is feasible under 9
what circumstances. We first classify all the algorithms into heuristic algorithms and meta-heuristics, 10
and we subcategorize algorithms in each class further based on extracted attributes of algorithms. 11
We hope that this survey will be very thoughtful in the development of new scheduling techniques. 12
Issues, challenges, and future directions have also been examined.
