# Exponentially Weighted Algorithm for Online Network Resource Allocation with Long-Term Constraints

arXiv: https://arxiv.org/abs/2405.02373 · HTML: https://mlsanity.com/paper/2405.02373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 3 of 3)

- [Online Convex Optimization with Time-Varying Constraints](https://mlsanity.com/paper/1702.04783/) (2017)
- [An Online Convex Optimization Approach to Dynamic Network Resource Allocation](https://mlsanity.com/paper/1701.03974/) (2017)
- [Online Optimization for Network Resource Allocation and Comparison with Reinforcement Learning Techniques](https://mlsanity.com/paper/2311.10023/) (2023)

## Abstract (arXiv)

This paper studies an online optimal resource reservation problem in communication networks with job transfers where the goal is to minimize the reservation cost while maintaining the blocking cost under a certain budget limit. To tackle this problem, we propose a novel algorithm based on a randomized exponentially weighted method that encompasses long-term constraints. We then analyze the performance of our algorithm by establishing an upper bound for the associated regret and the cumulative constraint violations. Finally, we present numerical experiments where we compare the performance of our algorithm with those of reinforcement learning where we show that our algorithm surpasses it.