Edge Cloud Offloading Algorithms: Issues, Methods, and Perspectives
Jianyu Wang, Jianli Pan, Flavio Esposito, Prasad Calyam, Zhicheng, Yang, Prasant Mohapatra

TL;DR
This paper surveys edge cloud offloading algorithms, addressing key issues, methods, and future perspectives to enhance resource management for IoT devices supporting demanding applications.
Contribution
It introduces a new model for analyzing offloading processes and provides a comprehensive overview of current efforts and future research directions in edge cloud offloading.
Findings
Offloading algorithms show strong potential for future IoT applications.
A new characterizing model helps understand the offloading process.
The survey highlights key issues and research gaps in edge cloud offloading.
Abstract
Mobile devices supporting the "Internet of Things" (IoT), often have limited capabilities in computation, battery energy, and storage space, especially to support resource-intensive applications involving virtual reality (VR), augmented reality (AR), multimedia delivery and artificial intelligence (AI), which could require broad bandwidth, low response latency and large computational power. Edge cloud or edge computing is an emerging topic and technology that can tackle the deficiency of the currently centralized-only cloud computing model and move the computation and storage resource closer to the devices in support of the above-mentioned applications. To make this happen, efficient coordination mechanisms and "offloading" algorithms are needed to allow the mobile devices and the edge cloud to work together smoothly. In this survey paper, we investigate the key issues, methods, and…
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Taxonomy
TopicsIoT and Edge/Fog Computing · IoT Networks and Protocols · Caching and Content Delivery
