Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

Abstract

To overcome devices' limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation.However, current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users' power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are re-associated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user-server association policy is proposed, taking into account the channel quality as well as the servers' computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user-server association is solved in the long timescale while a dynamic task offloading and resource ). 2 allocation policy is executed in the short timescale. Simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly-reliable task computation and lower delay performance, compared to baselines. Index Terms 5G and beyond, mobile edge computing (MEC), fog networking and computing, ultra-reliable low latency communications (URLLC), extreme value theory. I. INTRODUCTION Motivated by the surging traffic demands spurred by online video and Internet-of-things (IoT) applications, including machine type and mission-critical communication (e.g., augmented/virtual reality (AR/VR) and drones), mobile edge computing (MEC)/fog computing are emerging technologies that distribute computations, communication, control, and storage at the network edge [2]-[6]. When executing the computation-intensive applications at mobile devices, the performance and user's quality of experience are significantly affected by the device's limited computation capability. Additionally, intensive computations are energy-consuming which severely shortens the lifetime of battery-limited devices. To address the computation and energy issues, mobile devices can wirelessly offload their tasks to proximal MEC servers. On the other hand, offloading tasks incurs additional latency which cannot be overlooked and should be taken into account in the system design. Hence, the energy-delay tradeoff has received significant attention and has been studied in various MEC systems [7]-[22]. A. Related WorkIn [7], Kwak et al. focused on an energy minimization problem for local computation and task offloading in a single-user MEC system. The authors further studied a multi-user system, which takes into account both the energy cost and monetary cost of task offloading [8]. Therein, the cost-delay tradeoff was investigated in terms of competition and coo...

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.,…”

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

“…In [16], an energy-efficient solution is presented to enhance the optimal allocation of resources by cloud providers. An efficient solution presented in [17] proposes ways to utilize the resources in a system that have been freed by preceding tasks. The multi-objective approach presented in the paper uses an approximation approach to perform this process.…”

Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT

“…Therefore, for the sake of simplicity, we can neglect the downlink transmission delay. Some previous works, e.g., [25]- [27], also ignore the downlink transmission time. On the other hand, our work can be easily extended to the scenario where the downlink transmission time is considered.…”