The Online Knapsack Problem with Departures
Abstract
The online knapsack problem is a classic online resource allocation problem in networking and operations research. Its basic version studies how to pack online arriving items of different sizes and values into a capacity-limited knapsack. In this paper, we study a general version that includes item departures, while also considering multiple knapsacks and multi-dimensional item sizes. We design a threshold-based online algorithm and prove that the algorithm can achieve order-optimal competitive ratios. Beyond worst-case performance guarantees, we also aim to achieve near-optimal average performance under typical instances. Towards this goal, we propose a data-driven online algorithm that learns within a policy-class that guarantees a worst-case performance bound. In trace-driven experiments, we show that our data-driven algorithm outperforms other benchmark algorithms in an application of online knapsack to job scheduling for cloud computing.
Cite
@article{arxiv.2209.11934,
title = {The Online Knapsack Problem with Departures},
author = {Bo Sun and Lin Yang and Mohammad Hajiesmaili and Adam Wierman and John C. S. Lui and Don Towsley and Danny H. K. Tsang},
journal= {arXiv preprint arXiv:2209.11934},
year = {2023}
}