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

arXiv: https://arxiv.org/abs/1812.08076 · HTML: https://mlsanity.com/paper/1812.08076/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 10 of 10)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications](https://mlsanity.com/paper/2212.14749/) (2022)
- [A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission](https://mlsanity.com/paper/2402.02043/) (2024)
- [Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks](https://mlsanity.com/paper/2503.03391/) (2025)
- [Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT](https://mlsanity.com/paper/2108.07504/) (2021)
- [FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations](https://mlsanity.com/paper/2209.14399/) (2022)
- [Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network](https://mlsanity.com/paper/2408.14298/) (2024)
- [Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning](https://mlsanity.com/paper/2507.19712/) (2025)
- [Adversarial Water-Filling: Theory, Algorithms, and a Domain-Specific Wireless Foundation Model](https://mlsanity.com/paper/2605.26163/) (2026)
- [Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks](https://mlsanity.com/paper/2607.18288/) (2026)

## Abstract (arXiv)

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.