The Online Knapsack Problem with Departures
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...
Gespeichert in:
| Veröffentlicht in: | Proceedings of the ACM on measurement and analysis of computing systems 2022-12, Vol.6 (3), p.1-32, Article 57 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 32 |
|---|---|
| container_issue | 3 |
| container_start_page | 1 |
| container_title | Proceedings of the ACM on measurement and analysis of computing systems |
| container_volume | 6 |
| creator | Sun, Bo Yang, Lin Hajiesmaili, Mohammad Wierman, Adam Lui, John C. S. Towsley, Don Tsang, Danny H.K. |
| description | 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. |
| doi_str_mv | 10.1145/3570618 |
| format | Article |
| fullrecord | <record><control><sourceid>acm_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1145_3570618</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3570618</sourcerecordid><originalsourceid>FETCH-LOGICAL-a244t-c02d7cbd63828b2bb8999dc591a3346c79751b2e645a73e51cd01d5374555d813</originalsourceid><addsrcrecordid>eNpNj0tLAzEUhYMoWGpx7yo7FzKam-TmsZT6xEJd1PWQV-nozHRIRsR_r9Iqrs6B83HgI-QU2CWAxCuBmikwB2TCpVYVcGkP__VjMivllTEGBhlaMSEXq02iy75t-kSfejcUF97oc976NnX0oxk39CYNLo_vOZUTcrR2bUmzfU7Jy93tav5QLZb3j_PrReW4lGMVGI86-KiE4cZz7421Nga04ISQKmirETxPSqLTIiGEyCCi0BIRowExJee735C3peS0rofcdC5_1sDqH816r_lNnu1IF7o_6Hf8AkCtShg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>The Online Knapsack Problem with Departures</title><source>ACM Digital Library</source><creator>Sun, Bo ; Yang, Lin ; Hajiesmaili, Mohammad ; Wierman, Adam ; Lui, John C. S. ; Towsley, Don ; Tsang, Danny H.K.</creator><creatorcontrib>Sun, Bo ; Yang, Lin ; Hajiesmaili, Mohammad ; Wierman, Adam ; Lui, John C. S. ; Towsley, Don ; Tsang, Danny H.K.</creatorcontrib><description>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.</description><identifier>ISSN: 2476-1249</identifier><identifier>EISSN: 2476-1249</identifier><identifier>DOI: 10.1145/3570618</identifier><language>eng</language><publisher>New York, NY, USA: ACM</publisher><subject>Applied computing ; Decision analysis ; Design and analysis of algorithms ; Network algorithms ; Network economics ; Networks ; Online algorithms ; Operations research ; Theory of computation</subject><ispartof>Proceedings of the ACM on measurement and analysis of computing systems, 2022-12, Vol.6 (3), p.1-32, Article 57</ispartof><rights>ACM</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a244t-c02d7cbd63828b2bb8999dc591a3346c79751b2e645a73e51cd01d5374555d813</citedby><cites>FETCH-LOGICAL-a244t-c02d7cbd63828b2bb8999dc591a3346c79751b2e645a73e51cd01d5374555d813</cites><orcidid>0000-0001-9056-0500 ; 0000-0002-7808-7375 ; 0000-0003-3172-7811 ; 0000-0003-0135-7098 ; 0000-0002-5923-0199 ; 0000-0001-7466-0384 ; 0000-0001-9278-2254</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://dl.acm.org/doi/pdf/10.1145/3570618$$EPDF$$P50$$Gacm$$Hfree_for_read</linktopdf><link.rule.ids>314,776,780,2276,27901,27902,40172,76197</link.rule.ids></links><search><creatorcontrib>Sun, Bo</creatorcontrib><creatorcontrib>Yang, Lin</creatorcontrib><creatorcontrib>Hajiesmaili, Mohammad</creatorcontrib><creatorcontrib>Wierman, Adam</creatorcontrib><creatorcontrib>Lui, John C. S.</creatorcontrib><creatorcontrib>Towsley, Don</creatorcontrib><creatorcontrib>Tsang, Danny H.K.</creatorcontrib><title>The Online Knapsack Problem with Departures</title><title>Proceedings of the ACM on measurement and analysis of computing systems</title><addtitle>ACM POMACS</addtitle><description>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.</description><subject>Applied computing</subject><subject>Decision analysis</subject><subject>Design and analysis of algorithms</subject><subject>Network algorithms</subject><subject>Network economics</subject><subject>Networks</subject><subject>Online algorithms</subject><subject>Operations research</subject><subject>Theory of computation</subject><issn>2476-1249</issn><issn>2476-1249</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNpNj0tLAzEUhYMoWGpx7yo7FzKam-TmsZT6xEJd1PWQV-nozHRIRsR_r9Iqrs6B83HgI-QU2CWAxCuBmikwB2TCpVYVcGkP__VjMivllTEGBhlaMSEXq02iy75t-kSfejcUF97oc976NnX0oxk39CYNLo_vOZUTcrR2bUmzfU7Jy93tav5QLZb3j_PrReW4lGMVGI86-KiE4cZz7421Nga04ISQKmirETxPSqLTIiGEyCCi0BIRowExJee735C3peS0rofcdC5_1sDqH816r_lNnu1IF7o_6Hf8AkCtShg</recordid><startdate>20221208</startdate><enddate>20221208</enddate><creator>Sun, Bo</creator><creator>Yang, Lin</creator><creator>Hajiesmaili, Mohammad</creator><creator>Wierman, Adam</creator><creator>Lui, John C. S.</creator><creator>Towsley, Don</creator><creator>Tsang, Danny H.K.</creator><general>ACM</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-9056-0500</orcidid><orcidid>https://orcid.org/0000-0002-7808-7375</orcidid><orcidid>https://orcid.org/0000-0003-3172-7811</orcidid><orcidid>https://orcid.org/0000-0003-0135-7098</orcidid><orcidid>https://orcid.org/0000-0002-5923-0199</orcidid><orcidid>https://orcid.org/0000-0001-7466-0384</orcidid><orcidid>https://orcid.org/0000-0001-9278-2254</orcidid></search><sort><creationdate>20221208</creationdate><title>The Online Knapsack Problem with Departures</title><author>Sun, Bo ; Yang, Lin ; Hajiesmaili, Mohammad ; Wierman, Adam ; Lui, John C. S. ; Towsley, Don ; Tsang, Danny H.K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a244t-c02d7cbd63828b2bb8999dc591a3346c79751b2e645a73e51cd01d5374555d813</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Applied computing</topic><topic>Decision analysis</topic><topic>Design and analysis of algorithms</topic><topic>Network algorithms</topic><topic>Network economics</topic><topic>Networks</topic><topic>Online algorithms</topic><topic>Operations research</topic><topic>Theory of computation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Bo</creatorcontrib><creatorcontrib>Yang, Lin</creatorcontrib><creatorcontrib>Hajiesmaili, Mohammad</creatorcontrib><creatorcontrib>Wierman, Adam</creatorcontrib><creatorcontrib>Lui, John C. S.</creatorcontrib><creatorcontrib>Towsley, Don</creatorcontrib><creatorcontrib>Tsang, Danny H.K.</creatorcontrib><collection>CrossRef</collection><jtitle>Proceedings of the ACM on measurement and analysis of computing systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sun, Bo</au><au>Yang, Lin</au><au>Hajiesmaili, Mohammad</au><au>Wierman, Adam</au><au>Lui, John C. S.</au><au>Towsley, Don</au><au>Tsang, Danny H.K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Online Knapsack Problem with Departures</atitle><jtitle>Proceedings of the ACM on measurement and analysis of computing systems</jtitle><stitle>ACM POMACS</stitle><date>2022-12-08</date><risdate>2022</risdate><volume>6</volume><issue>3</issue><spage>1</spage><epage>32</epage><pages>1-32</pages><artnum>57</artnum><issn>2476-1249</issn><eissn>2476-1249</eissn><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.</abstract><cop>New York, NY, USA</cop><pub>ACM</pub><doi>10.1145/3570618</doi><tpages>32</tpages><orcidid>https://orcid.org/0000-0001-9056-0500</orcidid><orcidid>https://orcid.org/0000-0002-7808-7375</orcidid><orcidid>https://orcid.org/0000-0003-3172-7811</orcidid><orcidid>https://orcid.org/0000-0003-0135-7098</orcidid><orcidid>https://orcid.org/0000-0002-5923-0199</orcidid><orcidid>https://orcid.org/0000-0001-7466-0384</orcidid><orcidid>https://orcid.org/0000-0001-9278-2254</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2476-1249 |
| ispartof | Proceedings of the ACM on measurement and analysis of computing systems, 2022-12, Vol.6 (3), p.1-32, Article 57 |
| issn | 2476-1249 2476-1249 |
| language | eng |
| recordid | cdi_crossref_primary_10_1145_3570618 |
| source | ACM Digital Library |
| subjects | Applied computing Decision analysis Design and analysis of algorithms Network algorithms Network economics Networks Online algorithms Operations research Theory of computation |
| title | The Online Knapsack Problem with Departures |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-15T01%3A56%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-acm_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Online%20Knapsack%20Problem%20with%20Departures&rft.jtitle=Proceedings%20of%20the%20ACM%20on%20measurement%20and%20analysis%20of%20computing%20systems&rft.au=Sun,%20Bo&rft.date=2022-12-08&rft.volume=6&rft.issue=3&rft.spage=1&rft.epage=32&rft.pages=1-32&rft.artnum=57&rft.issn=2476-1249&rft.eissn=2476-1249&rft_id=info:doi/10.1145/3570618&rft_dat=%3Cacm_cross%3E3570618%3C/acm_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |