# An Online Algorithm for Computation Offloading in Non-Stationary Environments

arXiv: https://arxiv.org/abs/2006.12032 · HTML: https://mlsanity.com/paper/2006.12032/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits](https://mlsanity.com/paper/2105.14586/) (2021)
- [Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks](https://mlsanity.com/paper/2205.10366/) (2022)
- [Learning to Schedule in Non-Stationary Wireless Networks With Unknown Statistics](https://mlsanity.com/paper/2308.02734/) (2023)

## Abstract (arXiv)

We consider the latency minimization problem in a task-offloading scenario, where multiple servers are available to the user equipment for outsourcing computational tasks. To account for the temporally dynamic nature of the wireless links and the availability of the computing resources, we model the server selection as a multi-armed bandit (MAB) problem. In the considered MAB framework, rewards are characterized in terms of the end-to-end latency. We propose a novel online learning algorithm based on the principle of optimism in the face of uncertainty, which outperforms the state-of-the-art algorithms by up to ~1s. Our results highlight the significance of heavily discounting the past rewards in dynamic environments.