Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (1812.08076v2)

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 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 several baselines.

• The paper proposes a novel framework using extreme value theory for dynamic task offloading and resource allocation to meet URLLC requirements in MEC systems.
• It implements a two-timescale mechanism with a user-server matching policy to balance computation loads and minimize power consumption.
• Simulation results demonstrate improved reliability and reduced latency compared to baseline methods, confirming its effectiveness for mission-critical applications.

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

This paper addresses the challenges associated with enabling ultra-reliable low-latency communication (URLLC) in mobile edge computing (MEC) systems. It specifically focuses on overcoming computation and energy constraints encountered by devices performing intensive tasks by employing a novel approach that integrates dynamic task offloading and resource allocation.

Context and Motivation

In the context of contemporary wireless networks, particularly those within the 5G and beyond landscape, ensuring URLLC is becoming increasingly essential due to the growing demand for mission-critical applications such as augmented reality (AR), virtual reality (VR), and IoT deployments. Existing MEC systems often prioritize average-based performance metrics, which do not adequately cater to the stringent latency and reliability requirements of such applications.

Methodological Approach

The authors propose a new framework that centers on addressing reliability and latency via extreme value theory. The framework introduces probabilistic constraints on task queue lengths, aiming to minimize power consumption while balancing the resources assigned to local computation and task offloading. The framework is characterized by the following core components:

1. User-Server Association Policy: The policy considers both channel quality and servers’ computational capacity and workloads. It utilizes matching theory to smartly pair user equipment (UE) with MEC servers, achieving efficient resource distribution.
2. Two-Timescale Mechanism: This mechanism differentiates between long timescale decision-making, where user-server associations are determined, and short timescale actions, which involve dynamic task offloading and resource allocation.
3. Lyapunov Optimization Framework: By leveraging Lyapunov optimization, the proposed framework dynamically adjusts resource allocation based on queue states and channel conditions, thus adapting to real-time network dynamics.

Key Findings

The simulation results underscore the effectiveness of the proposed approach. One prominent observation is that the framework ensures more reliable task execution and reduced latencies compared to several baseline methods. It succeeds in accommodating high order statistics of queue length deviations, thereby emphasizing the handling of extreme cases which are vital for mission-critical applications.

Implications and Future Directions

The implications of this research are significant for both the academic community and the industry. Practically, this framework can play a pivotal role in the deployment of MEC systems within URLLC scenarios, enhancing the quality of experience for end-users and improving operational efficiencies. Theoretically, it presents a robust foundation for further exploration into the integration of extreme value theory within network optimization paradigms.

Moving forward, it would be intriguing to explore how this framework can be extended or adapted for other types of edge computing architectures, such as those involving fog computing. Additionally, integrating machine learning techniques for predictive resource management could further enhance the system's adaptability and efficiency.

In conclusion, the paper provides a comprehensive analysis and innovative solution to the challenges of resource allocation in MEC systems facing URLLC demands. Its contributions to both the theoretical aspects of queue management and the practical deployment of edge computing resources are noteworthy.