Abstract
Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.
Highlights
Smart devices, e.g., smartphones, wearable devices, and Internet of Thing (IoT) devices, are increasingly popular as the development of information technology and the growing need for improving the quality of human life
BACKGROUND we first provide a simple overview of edge-cloud computing environment, and present several representative cases of task offloading in edge-clouds, which are helpful to understand the remainder of the paper
REVIEWED LITERATURE SEARCH The literatures we reviewed in this paper include the followings: (1) the relevant literatures obtained by querying the Engineering Village Compendex database4 and the Web of Science Core Collection5 with the searching conditions, (‘‘edge cloud’’ OR ‘‘fog cloud’’) AND (‘‘task scheduling’’ OR ‘‘task offloading’’ OR ‘‘workload scheduling’’ OR ‘‘workload offloading’’ OR ‘‘workflow scheduling’’ OR ‘‘workflow offloading’’ OR ‘‘computing offloading’’ OR ‘‘computation offloading’’), to cover as many high quality research articles as possible; (2) and the relevant literatures citing the literatures obtained in (1) and (2), which were achieved by Google Scholar6, to cover as many newly published works as possible
Summary
The research was supported in part by the National Natural Science Foundation of China (Grant No 61872043, 61975187, 21904120), the Key Science and Technology Program of Henan Province (Grant No 202102210174, 202102210383, 202102210149), the Key Scientific Research Projects of Henan Higher School (Grant No 19A520043, 20B520039, 21A520050), Qin Xin Talents Cultivation Program, Beijing Information Science and Technology University (No QXTCP B201904), the fund of the Beijing Key Laboratory of Internet Culture and Digital Dissemination Research (Grant No ICDDXN004), the Foundation Training Program for Young Key Teachers of Zhengzhou University of Light Industry, and the Research Fund for the Doctoral Program of Zhengzhou University of Light Industry.
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