Performance modeling of representative load sharing schemes for clustered servers in multiaccess edge computing
Author | Liu L. |
Author | Chan S. |
Author | Han G. |
Author | Guizani M. |
Author | Bandai M. |
Available date | 2020-03-18T10:47:15Z |
Publication Date | 2019 |
Publication Name | IEEE Internet of Things Journal |
Resource | Scopus |
ISSN | 23274662 |
Abstract | Due to their limited functionality, ubiquitous connected devices in the Internet of Things rely heavily on the computational and storage resources of the cloud. However, mainstream cloud systems always require high network bandwidth and cannot satisfy the delay requirement of real-time applications. Therefore, a new paradigm called multiaccess edge computing has emerged to offload the computation and storage needs of end user devices to the edge cloud servers located in the radio access networks of 5G mobile networks. In this paper, we study and compare three load sharing schemes, namely, no sharing, random sharing, and least loaded sharing, which exploit the collaboration between clustered servers in different degrees. We develop computationally efficient analytical models to evaluate the performance of these schemes. These models are validated by simulation, and then used to compare the performances of the three load sharing schemes under various system parameters. Comparison results show that the least loaded sharing scheme is most suitable to fully exploit the collaboration between the servers and achieve load balance among them. It contributes to reducing the blocking probability and waiting time experienced by users. |
Sponsor | This work was supported in part by the Innovation and Technology Fund from the Innovation and Technology Commission of Hong Kong under Project 9443004, in part by the National Natural Science Foundation of China under Grant 61572172 and Grant 61872124, in part by the National Natural Science Foundation of China Guangdong Joint Fund under Grant U180120020, in part by the Fundamental Research Funds for the Central Universities under Grant 2016B10714, and in part by the Six Talent Peaks Project in Jiangsu Province under Grant XYDXXJS-007. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Buffer sharing Collaboration servers Load balancing Multiaccess edge computing (MEC) |
Type | Article |
Pagination | 4880-4888 |
Issue Number | 3 |
Volume Number | 6 |
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