A Survey of State-of-the-art on Edge Computing: Theoretical Models, Technologies, Directions, and Development Paths

Abstract

In order to describe the roadmap of current edge computing research activities, we first gave a brief overview of the most advanced edge computing surveys published in the last six years. We found that edge computing has been adaptively integrated into growing number of applications. Edge computing theory and technology will bring substantial innovation and incentive, as well as a large number of application scenarios in different fields, such as edge computing assisted smart city, Internet of Vehicles(IoV), Industrial Internet and many other different fields. However, we believe that a comprehensive investigation of the essence of edge computing is still missing by using the most advanced theoretical models, technologies, directions and development paths. To fill this gap, we have conducted an in-depth survey by identifying and classifying the latest high-quality research results related to theoretical discoveries in edge computing, as well as edge convergence technologies that intersect with the frontiers of Information and Communication Technology (ICT). Finally, we also summarize several promising open issues as future research directions. We hope that this survey report will attract much more attention, stimulate fruitful discussions, and provide ideas and useful guidance for further research on the theoretical models, technologies, directions and development paths of edge computing.

Opportunities and Pitfalls of IT Architectures for Edge Computing

“…Even the platforms and tools used most were often intended for cloud-native development [7]. Even so, tends show that edge-native solutions are becoming more common, and the framework and tool ecosystem is evolving as well [8] with tools like OpenYurt [9], KubeEdge [10] and EdgeX [11]. Because the area of Edge Computing is still evolving standardisation is becoming an issue, and should be addressed as noted by [12], while [13] found service migration, security and privacy preservation and deployment as the most pressing issues.…”

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

“…The research in [32] is focused on service orchestration and resource management for edge computing, including task offloading, content caching, and virtual network embedding (VNE). Finally, in [33], all recent technological advances in edge computing, especially from the perspective of architectures and models, key technologies, and directions, are presented and discussed.…”

“…Year Contributions [25] Edge computing-driven IoT under various technological aspects [26] Security considerations in edge-based IoT [27] Security and privacy considerations in IEC continuum systems and classification of attacks [28] Edge AI [29] Analysis of different node categories in an edge-cloud-IoT environment [30] Frameworks and simulation tools for the support of distributed intelligence in the edge to cloud environments [31] Integration of edge computing with novel technologies [32] Management and orchestration in edge computing IoT systems [33] Recent advances in edge computing…”

A joint task caching and computation offloading scheme based on Deep Reinforcement Learning

“…Edge computing [8] is a distributed computing model that reduces data transmission delays and network congestion by placing computing and data processing capabilities as close as possible to the data source or end user. This model can process tasks on edge devices, nodes, or servers close to the data source, providing faster response times and a better user experience [9]. Edge computing aims to solve the problem of latency and bandwidth limitations in cloud computing, and it is suitable for application scenarios that require instant response.…”