The Online Knapsack Problem with Departures-Reference-Cited by-同舟云学术

The Online Knapsack Problem with Departures

Published:2022-12 Issue:3 Volume:6 Page:1-32
ISSN:2476-1249
Container-title:Proceedings of the ACM on Measurement and Analysis of Computing Systems
language:en
Short-container-title:Proc. ACM Meas. Anal. Comput. Syst.

Author:

Sun Bo¹^ORCID,Yang Lin²^ORCID,Hajiesmaili Mohammad³^ORCID,Wierman Adam⁴^ORCID,Lui John C. S.¹^ORCID,Towsley Don³^ORCID,Tsang Danny H.K.⁵^ORCID

Affiliation:

1. The Chinese University of Hong Kong, Hong Kong, Hong Kong

2. Nanjing University, Nanjing, China

3. University of Massachusetts Amherst, Amherst, MA, USA

4. California Institute of Technology, Pasadena, CA, USA

5. The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Hong Kong, Hong Kong

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.

Funder

Hong Kong Research Grant Council (RGC) General Research Fund

NSF

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Safety, Risk, Reliability and Quality,Computer Science (miscellaneous)

Link

https://dl.acm.org/doi/pdf/10.1145/3570618

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