Stochastic Resource Allocation and Delay Analysis for Mobile Edge Computing Systems

To alleviate the local computation demands from the ever-increasing computation-intensive mobile applications, Mobile Edge Computing (MEC) has proved promising. Especially, by opportunistically offloading these computation tasks to the MEC server, the delay of computing could be significantly improv...

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Bibliographic Details
Published in:IEEE transactions on communications 2023-07, Vol.71 (7), p.4018-4033
Main Authors: Wang, Yitu, Wang, Wei, Lau, Vincent K. N., Nakachi, Takayuki, Zhang, Zhaoyang
Format: Article
Language:English
Subjects:
Algorithms
Applications programs
Buffers
Communication
Delay
delay analysis
Delays
Differential equations
Edge computing
lyapunov optimization
Mathematical analysis
Mobile computing
Mobile edge computing (MEC)
Optimization
Queuing theory
Random walk
reflection process
Resource allocation
Resource management
Servers
Steady state
stochastic differential equations (SDEs)
Stochastic processes
Task analysis
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Summary:To alleviate the local computation demands from the ever-increasing computation-intensive mobile applications, Mobile Edge Computing (MEC) has proved promising. Especially, by opportunistically offloading these computation tasks to the MEC server, the delay of computing could be significantly improved through communication. In this paper, we develop an analytical framework for joint communication and computation resources allocation for multi-user MEC systems. Specifically, to retrieve the combined effect of communication and computation capabilities, we establish a dual queue system, including a data queue sub-system and a computation queue sub-system. To address the associated stochastic resource optimization problem, we propose a low-complexity resource allocation algorithm by Lyapunov optimization to stabilize all the sub-queue systems. As the practical buffers are finite, the conventional delay analysis of Lyapunov optimization becomes inaccurate. Alternatively, we model the stochastic queue lengthes as discrete time controlled random walk processes, which are transformed to continuous time Stochastic Differential Equations (SDEs) with reflections by strong approximation. According to the steady state analysis on the SDEs, we derive closed-form steady state distributions of the queue lengths, and then obtain the average delay performance with finite buffers. Finally, the accuracy of the proposed delay analysis is verified through simulation.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2023.3266353