Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems

Yuyi Mao, Jun Zhang, S. H. Song, Khaled Letaief

Research output: Contribution to journalArticle

110 Citations (Scopus)

Abstract

Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective as minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an [O (1/V ), O (V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.

Original languageEnglish
JournalIEEE Transactions on Wireless Communications
DOIs
Publication statusAccepted/In press - 23 Jun 2017

Fingerprint

Resource Management
Servers
Mobile devices
Computing
Server
Mobile Devices
Program processors
Electric power utilization
Weighted Sums
Power Consumption
Frequency allocation
Closed-form
Fading channels
Gauss-Seidel Method
Bandwidth Allocation
Scheduling
Power Allocation
Fading Channels
Control Parameter
Performance Analysis

Keywords

  • Cloud computing
  • dynamic voltage and frequency scaling
  • Lyapunov optimization
  • Mobile communication
  • Mobile handsets
  • Mobile-edge computing
  • Power demand
  • radio and computational resource management
  • Resource management
  • Servers
  • Wireless communication

ASJC Scopus subject areas

  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

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abstract = "Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective as minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an [O (1/V ), O (V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.",
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