Task Management for Cooperative Mobile Edge Computing

Published

November 12, 2020

Author(s)

Li-Tse Hsieh, Hang Liu, Yang Guo, Robert Gazda

Abstract

Mobile edge computing (MEC) is an emerging paradigm that integrates computing capabilities in a wireless access network to execute computational tasks near mobile users with low latency. It is challenging to orchestrate the heterogeneous MEC edge nodes and remote cloud data centers to jointly process user tasks. Furthermore, in many mobile scenarios, the task arrivals and network states are unknown and time varying. In this paper, we propose a stochastic framework to enable the horizontal cooperation among geographically distributed MEC edge nodes and the vertical cooperation between edge nodes and cloud data centers, for achieving the optimal performance of user task processing under non-stationary task arrivals and network states. The task assignment optimization problem is formulated as a Markov decision process by considering the dynamic interactions and heterogeneity of the involved entities. The algorithm is then developed based on online reinforcement learning to capture system dynamics and learn the optimal task assignment policy with no requirement for prior knowledge of task arrival and network statistics. A function decomposition technique is also proposed to simplify the problem and reduce the state space. The evaluation results show that the proposed online learning-based scheme significantly outperforms the state-of-the-art baselines.

Conference Dates

November 11-13, 2020

Conference Location

San Jose, CA, US

Conference Title

ACM/IEEE Workshop on Hot Topics on Web of Things 2020

Pub Type

Conferences

Download Paper

Local Download

Keywords

Mobile edge computing (MEC), Reinforcement Learning, task assignment, stochastic optimization, Markov decision process (MDP), reinforcement learning

Citation

Hsieh, L. , Liu, H. , Guo, Y. and Gazda, R. (2020), Task Management for Cooperative Mobile Edge Computing, ACM/IEEE Workshop on Hot Topics on Web of Things 2020, San Jose, CA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928585 (Accessed April 25, 2024)