TY - JOUR
T1 - Distributed resource scheduling in edge computing
T2 - Problems, solutions, and opportunities
AU - Sahni, Yuvraj
AU - Cao, Jiannong
AU - Yang, Lei
AU - Wang, Shengwei
N1 - Funding Information:
The work described in this paper was supported in part by the Research Institute for Artificial Intelligence of Things (RIAIoT), The Hong Kong Polytechnic University , in part by the Hong Kong (HK) Research Grant Council (RGC) General Research Fund with project code PolyU 15217919 , in part by the HK RGC Research Impact Fund with project code R5060-19 , and in part by the HK RGC Collaborative Research Fund with project code C5018-20GF .
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/12/24
Y1 - 2022/12/24
N2 - Edge computing has become popular in the last decade and will advance in future to support real-time actionable analytics at the devices. One of the fundamental problems for future edge computing is to make distributed resource scheduling (DRS) decisions both at the end devices and edge devices to support requirements including autonomous computation, scalability, low-latency, etc. Several surveys in the literature on edge computing have considered some aspects related to DRS, such as challenges and solution approaches, particularly for computation offloading and data management. However, to the best of our knowledge, there is no comprehensive survey on DRS in edge computing. This paper surveys the challenging issues, motivations, and existing works for enabling DRS in edge computing. We define and identify the unique issues for DRS in edge computing compared to traditional works on parallel and distributed systems. The motivations for DRS in edge computing have been described by pointing out the benefits and emerging application scenarios. This paper also provides a taxonomy to classify the existing works from three perspectives, i.e., systems, problems, and solution approaches. Finally, we have outlined several future directions that can help researchers to advance the state-of-the-art.
AB - Edge computing has become popular in the last decade and will advance in future to support real-time actionable analytics at the devices. One of the fundamental problems for future edge computing is to make distributed resource scheduling (DRS) decisions both at the end devices and edge devices to support requirements including autonomous computation, scalability, low-latency, etc. Several surveys in the literature on edge computing have considered some aspects related to DRS, such as challenges and solution approaches, particularly for computation offloading and data management. However, to the best of our knowledge, there is no comprehensive survey on DRS in edge computing. This paper surveys the challenging issues, motivations, and existing works for enabling DRS in edge computing. We define and identify the unique issues for DRS in edge computing compared to traditional works on parallel and distributed systems. The motivations for DRS in edge computing have been described by pointing out the benefits and emerging application scenarios. This paper also provides a taxonomy to classify the existing works from three perspectives, i.e., systems, problems, and solution approaches. Finally, we have outlined several future directions that can help researchers to advance the state-of-the-art.
KW - Distributed resource scheduling
KW - Edge computing
KW - Internet of Things
KW - Survey
UR - http://www.scopus.com/inward/record.url?scp=85141544814&partnerID=8YFLogxK
U2 - 10.1016/j.comnet.2022.109430
DO - 10.1016/j.comnet.2022.109430
M3 - Short survey
AN - SCOPUS:85141544814
SN - 1389-1286
VL - 219
JO - Computer Networks
JF - Computer Networks
M1 - 109430
ER -