# Online Learning for Function Placement in Serverless Computing

arXiv: https://arxiv.org/abs/2410.13696 · HTML: https://mlsanity.com/paper/2410.13696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding](https://mlsanity.com/paper/2202.13706/) (2022)
- [Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning](https://mlsanity.com/paper/2202.02140/) (2022)
- [On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2205.09925/) (2022)

## Abstract (arXiv)

We study the placement of virtual functions aimed at minimizing the cost. We propose a novel algorithm, using ideas based on multi-armed bandits. We prove that these algorithms learn the optimal placement policy rapidly, and their regret grows at a rate at most O( N M √Tln T ) while respecting the feasibility constraints with high probability, where T is total time slots, M is the number of classes of function and N is the number of computation nodes. We show through numerical experiments that the proposed algorithm both has good practical performance and modest computational complexity. We propose an acceleration technique that allows the algorithm to achieve good performance also in large networks where computational power is limited. Our experiments are fully reproducible, and the code is publicly available.