A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

TL;DR

This survey reviews various scheduling techniques in edge cloud computing, highlighting their advantages, challenges, and future research directions to optimize performance and QoS in resource-limited environments.

Contribution

It classifies and analyzes existing scheduling algorithms in edge cloud computing, providing a comprehensive framework for future development and research.

Findings

Heuristic and meta-heuristic algorithms are the main categories of scheduling strategies.

Different algorithms have varying advantages and limitations based on QoS requirements.

The survey identifies key issues and challenges in current scheduling approaches.

Abstract

After the advent of the Internet of Things and 5G networks, edge computing became the center of attraction. The tasks demanding high computation are generally offloaded to the cloud since the edge is resource-limited. The Edge Cloud is a promising platform where the devices can offload delay-sensitive workloads. In this regard, scheduling holds great importance in offloading decisions in the Edge Cloud collaboration. The ultimate objectives of scheduling are the quality of experience, minimizing latency, and increasing performance. An abundance of efforts on scheduling has been done in the past. In this paper, we have surveyed proposed scheduling strategies in the context of edge cloud computing in various aspects such as advantages and demerits, QoS parameters, and fault tolerance. We have also surveyed such scheduling approaches to evaluate which one is feasible under what circumstances. We first classify all the algorithms into heuristic algorithms and meta-heuristics, and we subcategorize algorithms in each class further based on extracted attributes of algorithms. We hope that this survey will be very thoughtful in the development of new scheduling techniques. Issues, challenges, and future directions have also been examined.