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arXiv · 2209.11934

The Online Knapsack Problem with Departures

Bo Sun·Lin Yang·Mohammad Hajiesmaili·Adam Wierman·John C. S. Lui·Don Towsley·Danny H. K. Tsang
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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.

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Bo Sun, Lin Yang, Mohammad Hajiesmaili, Adam Wierman, John C. S. Lui, Don Towsley, Danny H. K. Tsang. 2022-09-24. The Online Knapsack Problem with Departures. https://arxiv.org/abs/2209.11934

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