# Delay-Optimal Computation Task Scheduling for Mobile-Edge Computing Systems

arXiv: https://arxiv.org/abs/1604.07525 · HTML: https://mlsanity.com/paper/1604.07525/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2016.

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

- [Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design](https://mlsanity.com/paper/2103.14272/) (2021)
- [Offloading Algorithms for Maximizing Inference Accuracy on Edge Device Under a Time Constraint](https://mlsanity.com/paper/2112.11413/) (2021)
- [Edge-centric Optimization of Multi-modal ML-driven eHealth Applications](https://mlsanity.com/paper/2208.02597/) (2022)
- [Structure-aware reinforcement learning for node-overload protection in mobile edge computing](https://mlsanity.com/paper/2107.01025/) (2021)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation](https://mlsanity.com/paper/2402.16866/) (2024)
- [Multi-objective Optimal Roadside Units Deployment in Urban Vehicular Networks](https://mlsanity.com/paper/2402.18581/) (2024)
- [Monitoring and Proactive Management of QoS Levels in Pervasive Applications](https://mlsanity.com/paper/2206.05478/) (2022)
- [Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach](https://mlsanity.com/paper/2307.01366/) (2023)
- [A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management](https://mlsanity.com/paper/2511.16075/) (2025)

## Abstract (arXiv)

Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a challenging two-timescale stochastic optimization problem. Specifically, in the larger timescale, whether to execute a task locally at the mobile device or to offload a task to the MEC server for cloud computing should be decided, while in the smaller timescale, the transmission policy for the task input data should adapt to the channel side information. In this paper, we adopt a Markov decision process approach to handle this problem, where the computation tasks are scheduled based on the queueing state of the task buffer, the execution state of the local processing unit, as well as the state of the transmission unit. By analyzing the average delay of each task and the average power consumption at the mobile device, we formulate a power-constrained delay minimization problem, and propose an efficient one-dimensional search algorithm to find the optimal task scheduling policy. Simulation results are provided to demonstrate the capability of the proposed optimal stochastic task scheduling policy in achieving a shorter average execution delay compared to the baseline policies.