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

By Yuyi Mao, Jun Zhang, Shenghui Song, Khaled Ben Letaief. IEEE Transactions on Wireless Communications, 2017

608 citations recorded by the source corpus.

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 of 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 $\left [{O\left ({1 / V}\right), O\left ({V}\right) }\right ]$ tradeoff with $V$ as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b6c43c817d3a9be0d5bf393b204673561ccddba2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Mobile Cooperative Computing: Energy-Efficient Peer-to-Peer Computation Offloading (2017, methodology): “The design of multiuser MECO systems involves the new research issue of joint radio-andcomputation resource allocation [9]–[12] for achieving system-level objectives (e.”
Price-Based Distributed Offloading for Mobile-Edge Computing With Computation Capacity Constraints (2017, background): “e allocation for MEC has been recently investigated in the literature, e.g., [5]– [12]. In general, the MEC paradigm can be divided into two categories: binary ofﬂoading [5]–[9] and partial ofﬂoading [10]–[12]. With binary ofﬂoading, the computation tasks at users can not be partitioned but must be ”
Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution (2020, background): “some others considered the comprehensive problem of radio allocation and computation resources management [23]–[25].”
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks (2017, background): “For multiuser MEC systems, both centralized [12] [13] and distributed [14] resource management schemes are studied for joint radio and computational resource management, in order to maximize system-level performance metric.”
6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications (2020, methodology): “A dynamic joint optimization algorithm that schedules the communication and computational resources optimally was suggested in [52], [53].”
Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, background): “Extending the problem of [10], the authors further took into account the server’s power consumption and resource allocation in the system analysis [11].”
Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems (2020, background): “The wireless transmission from a mobile device to an edge node suffers from path loss and small-scale fading [11], [23].”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “First, the stochastic nature of wireless channels and task arrivals should be considered [100], [36]; Second, the limited radio and computing resources are shared by multiple users, both of which will affect the computation latency [101], [102]; Finally, the mobility of vehicles will affect the task”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, methodology): “Considering an MEC system consisting of multiple users and edge servers, Mao et al. [99] minimize the long-term average weighted sum power consumption of the MEC system by adjusting the radio and computation resources at both the MDs and edge servers.”