Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems

Abstract

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability.In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependencies and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP), and approximate it to a virtual continuous time system (VCTS) with reflections. Different to most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closedform approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). A extension is provided from single MT single MEC server scenarios to multiple MTs multiple MEC servers scenarios and several insights are derived. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.Technology, Hong Kong. 2 I. INTRODUCTION Smart mobile terminals (MTs) with advanced communication and computation capabilities facilitate us with a pervasive and powerful platform to realize many emerging computationintensive mobile applications, e.g., interactive gaming, character recognition, and natural language processing [2], [3]. These pose exigent requirements on the quality of computation experience, especially for the delay-sensitive applications. Computation offloading [4], which offloads the computation tasks to the offloading destination, is one of the fundamental services to improve the computation performance, i.e., delay performance. In computation offloading services, both the communication capability of the MT and the computation capability of the offloading destination will influence the delay performance. Specifically, • The communication capability of the MT: The offloading rate varies according to the time-varying wireless channel quality between the MT and the offloading destination. Poor communication capabilities will result in the starvation of the computation of the offloading destination, which induces a large queuing delay at the MT.• The computation capability of the offloading destination: In practical scenarios, the offloaded tasks cannot be executed immediately because the computation capability of the offloading destination is not infinity. Both the computation time and the waiting time at ...

Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes

“…To make the system realistic, we set the scaling factor . The noise power is dBM/Hz [ 20 ] and the bandwidth is 5 MHz. The channel gain is and the transmission power is 4 mW.…”

“… Back-pressure Algorithm-based Computational Offloading (BPCO) [ 45 ]: This method considers a queuing system, and determines the task offloading decision by using the back-pressure algorithm to calculate the task backlog difference between the local queue and the edge queue. Closed-form Delay-optimal Computational Offloading (CDCO) [ 20 ]: This strategy takes into account the queue model and determines the offloading decision of tasks by minimizing the average energy cost. …”

“…So far, there has been some research works [ 38 , 39 , 40 ] about the stochastic optimization technique and the Lyapunov method. In order to minimize the computation cost of tasks, Meng et al [ 20 ] have shown an offloading method based on the Markov decision process (MDP). This method used the queues but only considered the cascading relationships between queues.…”

“…Geng et al [ 19 ] studied a computational offloading strategy, which also took into account the waiting delay. Meng et al [ 20 ] shown an offloading method based on Markov decision process (MDP). But none of them specifically optimize the delay from the perspective of optimizing the task backlog.…”

Multi‐tier MEC offloading strategy based on dynamic channel characteristics

“…Mobile edge computing, which provides users computation services using servers at the network edge, is considered to be an effective solution to the above challenges and has been conducted in the academia and industry [4]. By offloading computation‐intensive tasks from mobile devices to the proximate edge servers, the quality of computation experience, e.g.…”