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https://hdl.handle.net/2440/138645
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Type: | Journal article |
Title: | Dynamic User Allocation in Stochastic Mobile Edge Computing Systems |
Author: | Lai, P. He, Q. Xia, X. Chen, F. Abdelrazek, M. Grundy, J. Hosking, J.G. Yang, Y. |
Citation: | IEEE Transactions on Services Computing, 2022; 15(5):2699-2712 |
Publisher: | Institute of Electrical and Electronics Engineers |
Issue Date: | 2022 |
ISSN: | 1939-1374 1939-1374 |
Statement of Responsibility: | Phu Lai, Qiang He, Xiaoyu Xia, Feifei Chen, Mohamed Abdelrazek, John Grundy, John Hosking, and Yun Yang |
Abstract: | Mobile Edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider's perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This study aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) -- an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable. |
Keywords: | Mobile edge computing; user allocation; Lyapunov optimization; resource allocation |
Rights: | © 2021 IEEE. |
DOI: | 10.1109/TSC.2021.3063148 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170101932 http://purl.org/au-research/grants/arc/DP180100212 http://purl.org/au-research/grants/arc/DP200102491 http://purl.org/au-research/grants/arc/FL190100035 |
Published version: | http://dx.doi.org/10.1109/tsc.2021.3063148 |
Appears in Collections: | Computer Science publications |
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