StarShip: Mitigating I/O Bottlenecks in Serverless Computing for Scientific Workflows
This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows face in serverless environments - an issue that has been largely overlooked by prior works. We propose StarShip, a framework that leverages different storage options and multi-tier functions to reduce I/O over...
Gespeichert in:
| Veröffentlicht in: | Performance evaluation review 2024-06, Vol.52 (1), p.79-80 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 80 |
|---|---|
| container_issue | 1 |
| container_start_page | 79 |
| container_title | Performance evaluation review |
| container_volume | 52 |
| creator | Basu Roy, Rohan Tiwari, Devesh |
| description | This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows face in serverless environments - an issue that has been largely overlooked by prior works. We propose StarShip, a framework that leverages different storage options and multi-tier functions to reduce I/O overhead by co-optimizing for service time and service cost. StarShip leverages the Levenberg-Marquardt optimization to find an effective solution in a large, complex search space. It outperforms existing methods with a 45% improvement in service time and a 37.6% reduction in service cost. |
| doi_str_mv | 10.1145/3673660.3655082 |
| format | Article |
| fullrecord | <record><control><sourceid>acm_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1145_3673660_3655082</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3655082</sourcerecordid><originalsourceid>FETCH-LOGICAL-a592-4eac201f466f7cb5c49aba9bcb6f4a93d4a1d6093d9f5f64a3d1bf17c5c33f823</originalsourceid><addsrcrecordid>eNo9kLtOwzAARS0EEqEwIzH5B9LY8SMxG1Q8KhV1SBFj5Dh2MU3iyDYg_p5CA9O90n0MB4BLjOYYU5YRXhDO0ZxwxlCZH4EEM1akgpb0GCQIc5IyIcQpOAvhDSFc5LhMwHMVpa9e7XgNn2y0WxntsIXLbA1vXYydHrTaBWgHWGn_oX2nQ4AL14_vvz3jPKyU1UO0xir44vzOdO4znIMTI7ugLyadgc393WbxmK7WD8vFzSqVTOQp1VLlCBvKuSlUwxQVspGiUQ03VArSUolbjvZGGGY4laTFjcGFYooQU-ZkBrLDrfIuBK9NPXrbS_9VY1T_QKknKPUEZb-4Oiyk6v_Lf-E3I59eCQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>StarShip: Mitigating I/O Bottlenecks in Serverless Computing for Scientific Workflows</title><source>ACM Digital Library Complete</source><creator>Basu Roy, Rohan ; Tiwari, Devesh</creator><creatorcontrib>Basu Roy, Rohan ; Tiwari, Devesh</creatorcontrib><description>This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows face in serverless environments - an issue that has been largely overlooked by prior works. We propose StarShip, a framework that leverages different storage options and multi-tier functions to reduce I/O overhead by co-optimizing for service time and service cost. StarShip leverages the Levenberg-Marquardt optimization to find an effective solution in a large, complex search space. It outperforms existing methods with a 45% improvement in service time and a 37.6% reduction in service cost.</description><identifier>ISSN: 0163-5999</identifier><identifier>EISSN: 1557-9484</identifier><identifier>DOI: 10.1145/3673660.3655082</identifier><language>eng</language><publisher>New York, NY, USA: ACM</publisher><subject>Architectures ; Cloud computing ; Computer systems organization ; Distributed architectures</subject><ispartof>Performance evaluation review, 2024-06, Vol.52 (1), p.79-80</ispartof><rights>Owner/Author</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-a592-4eac201f466f7cb5c49aba9bcb6f4a93d4a1d6093d9f5f64a3d1bf17c5c33f823</cites><orcidid>0000-0002-1082-9846 ; 0000-0002-7253-2458</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://dl.acm.org/doi/pdf/10.1145/3673660.3655082$$EPDF$$P50$$Gacm$$H</linktopdf><link.rule.ids>314,780,784,2282,27924,27925,40196,76228</link.rule.ids></links><search><creatorcontrib>Basu Roy, Rohan</creatorcontrib><creatorcontrib>Tiwari, Devesh</creatorcontrib><title>StarShip: Mitigating I/O Bottlenecks in Serverless Computing for Scientific Workflows</title><title>Performance evaluation review</title><addtitle>ACM SIGMETRICS</addtitle><description>This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows face in serverless environments - an issue that has been largely overlooked by prior works. We propose StarShip, a framework that leverages different storage options and multi-tier functions to reduce I/O overhead by co-optimizing for service time and service cost. StarShip leverages the Levenberg-Marquardt optimization to find an effective solution in a large, complex search space. It outperforms existing methods with a 45% improvement in service time and a 37.6% reduction in service cost.</description><subject>Architectures</subject><subject>Cloud computing</subject><subject>Computer systems organization</subject><subject>Distributed architectures</subject><issn>0163-5999</issn><issn>1557-9484</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNo9kLtOwzAARS0EEqEwIzH5B9LY8SMxG1Q8KhV1SBFj5Dh2MU3iyDYg_p5CA9O90n0MB4BLjOYYU5YRXhDO0ZxwxlCZH4EEM1akgpb0GCQIc5IyIcQpOAvhDSFc5LhMwHMVpa9e7XgNn2y0WxntsIXLbA1vXYydHrTaBWgHWGn_oX2nQ4AL14_vvz3jPKyU1UO0xir44vzOdO4znIMTI7ugLyadgc393WbxmK7WD8vFzSqVTOQp1VLlCBvKuSlUwxQVspGiUQ03VArSUolbjvZGGGY4laTFjcGFYooQU-ZkBrLDrfIuBK9NPXrbS_9VY1T_QKknKPUEZb-4Oiyk6v_Lf-E3I59eCQ</recordid><startdate>20240611</startdate><enddate>20240611</enddate><creator>Basu Roy, Rohan</creator><creator>Tiwari, Devesh</creator><general>ACM</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-1082-9846</orcidid><orcidid>https://orcid.org/0000-0002-7253-2458</orcidid></search><sort><creationdate>20240611</creationdate><title>StarShip: Mitigating I/O Bottlenecks in Serverless Computing for Scientific Workflows</title><author>Basu Roy, Rohan ; Tiwari, Devesh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a592-4eac201f466f7cb5c49aba9bcb6f4a93d4a1d6093d9f5f64a3d1bf17c5c33f823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Architectures</topic><topic>Cloud computing</topic><topic>Computer systems organization</topic><topic>Distributed architectures</topic><toplevel>online_resources</toplevel><creatorcontrib>Basu Roy, Rohan</creatorcontrib><creatorcontrib>Tiwari, Devesh</creatorcontrib><collection>CrossRef</collection><jtitle>Performance evaluation review</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Basu Roy, Rohan</au><au>Tiwari, Devesh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>StarShip: Mitigating I/O Bottlenecks in Serverless Computing for Scientific Workflows</atitle><jtitle>Performance evaluation review</jtitle><stitle>ACM SIGMETRICS</stitle><date>2024-06-11</date><risdate>2024</risdate><volume>52</volume><issue>1</issue><spage>79</spage><epage>80</epage><pages>79-80</pages><issn>0163-5999</issn><eissn>1557-9484</eissn><abstract>This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows face in serverless environments - an issue that has been largely overlooked by prior works. We propose StarShip, a framework that leverages different storage options and multi-tier functions to reduce I/O overhead by co-optimizing for service time and service cost. StarShip leverages the Levenberg-Marquardt optimization to find an effective solution in a large, complex search space. It outperforms existing methods with a 45% improvement in service time and a 37.6% reduction in service cost.</abstract><cop>New York, NY, USA</cop><pub>ACM</pub><doi>10.1145/3673660.3655082</doi><tpages>2</tpages><orcidid>https://orcid.org/0000-0002-1082-9846</orcidid><orcidid>https://orcid.org/0000-0002-7253-2458</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0163-5999 |
| ispartof | Performance evaluation review, 2024-06, Vol.52 (1), p.79-80 |
| issn | 0163-5999 1557-9484 |
| language | eng |
| recordid | cdi_crossref_primary_10_1145_3673660_3655082 |
| source | ACM Digital Library Complete |
| subjects | Architectures Cloud computing Computer systems organization Distributed architectures |
| title | StarShip: Mitigating I/O Bottlenecks in Serverless Computing for Scientific Workflows |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T12%3A03%3A19IST&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=StarShip:%20Mitigating%20I/O%20Bottlenecks%20in%20Serverless%20Computing%20for%20Scientific%20Workflows&rft.jtitle=Performance%20evaluation%20review&rft.au=Basu%20Roy,%20Rohan&rft.date=2024-06-11&rft.volume=52&rft.issue=1&rft.spage=79&rft.epage=80&rft.pages=79-80&rft.issn=0163-5999&rft.eissn=1557-9484&rft_id=info:doi/10.1145/3673660.3655082&rft_dat=%3Cacm_cross%3E3655082%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 |