Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing

Abstract

While mobile edge computing (MEC) alleviates the computation and power limitations of mobile devices, additional latency is incurred when offloading tasks to remote MEC servers. In this work, the power-delay tradeoff in the context of task offloading is studied in a multi-user MEC scenario. In contrast with current system designs relying on average metrics (e.g., the average queue length and average latency), a novel network design is proposed in which latency and reliability constraints are taken into account. This is done by imposing a probabilistic constraint on users' task queue lengths and invoking results from extreme value theory to characterize the occurrence of lowprobability events in terms of queue length (or queuing delay) violation. The problem is formulated as a computation and transmit power minimization subject to latency and reliability constraints, and solved using tools from Lyapunov stochastic optimization. Simulation results demonstrate the effectiveness of the proposed approach, while examining the power-delay tradeoff and required computational resources for various computation intensities.Index Terms-5G, mobile edge computing, fog networking and computing, ultra-reliable low latency communications (URLLC), extreme value theory.

Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networks

“…i.e., σ k ≤ σ th k and ξ k ≤ ξ th k 8 [37], [38]. Subsequently, applying both parameter thresholds and (9) and (10), we impose constraints for the time-averaged mean and second moment of the conditional excess queue value, i.e.,…”

Multi-access Edge Computing: A Survey

“…For them, it is important to design a MEC system not only relying on average metrics (e.g., average workload and average latency) but also considering statistical distribution of them, in other words, the upper bound of metrics or the delay bound violation probability. Liu et al [75] investigate this problem employing extreme value theory [76] and Lyapunov stochastic optimization technique [77]. As the result, the method for task computation and offloading decision at the user devices, and the resource allocation at the server side are presented.…”

Cross-Layer Design for Mission-Critical IoT in Mobile Edge Computing Systems

“…Although these works did not consider MC-IoT, they developed useful methodologies for optimizing computing offloading in MEC systems. How to improve the QoS in MEC systems by optimizing task offloading has been addressed in [36][37][38][39]. In [36], the average delay was minimized by optimizing task offloading/scheduling in MEC systems.…”

“…The offloading schemes for URLLC in MEC systems were optimized in [38], where a weighted sum of E2E delay and the offloading failure probability was minimized in a single-user scenario. How to analyze latency in largescale MEC networks was studied in [39], where the average communication and computing latencies were derived.Most of the existing studies on resource management in MEC systems only analyzed UL transmission and processing delay, and assumed DL transmission can be finished with high transmit power at the APs [36][37][38][39]. Besides, they did not take the decoding errors in the short blocklength regime into account, which is crucial for MC-IoT.…”

“…Most of the existing studies on resource management in MEC systems only analyzed UL transmission and processing delay, and assumed DL transmission can be finished with high transmit power at the APs [36][37][38][39]. Besides, they did not take the decoding errors in the short blocklength regime into account, which is crucial for MC-IoT.…”