DAG-Fluid: A Real-Time Scheduling Algorithm for DAGs
Various scheduling algorithms have been proposed for real-time parallel tasks modeled as a Directed Acyclic Graph (DAG). The capacity augmentation bound is a quantitative metric widely used in this field to compare the algorithms. Among the existing algorithms, the lowest capacity augmentation bound...
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| Veröffentlicht in: | IEEE transactions on computers 2021-03, Vol.70 (3), p.471-482 |
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| creator | Guan, Fei Qiao, Jiaqing Han, Yu |
| description | Various scheduling algorithms have been proposed for real-time parallel tasks modeled as a Directed Acyclic Graph (DAG). The capacity augmentation bound is a quantitative metric widely used in this field to compare the algorithms. Among the existing algorithms, the lowest capacity augmentation bound for DAG tasks with implicit deadlines is 2, which has been achieved by federated scheduling. To improve the schedulability and lower the capacity augmentation bound, this paper proposes DAG-Fluid, an algorithm based on fluid scheduling. We prove that DAG-Fluid has a capacity augmentation bound of 2-\frac{1}{m+1} 2-1m+1 , in which m m is the number of processors in the system. Experiments show that DAG-Fluid performs better than the state of the art scheduling algorithms. |
| doi_str_mv | 10.1109/TC.2020.2990282 |
| format | Article |
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The capacity augmentation bound is a quantitative metric widely used in this field to compare the algorithms. Among the existing algorithms, the lowest capacity augmentation bound for DAG tasks with implicit deadlines is 2, which has been achieved by federated scheduling. To improve the schedulability and lower the capacity augmentation bound, this paper proposes DAG-Fluid, an algorithm based on fluid scheduling. We prove that DAG-Fluid has a capacity augmentation bound of <inline-formula><tex-math notation="LaTeX">2-\frac{1}{m+1}</tex-math> <mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>-</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>m</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:math><inline-graphic xlink:href="guan-ieq1-2990282.gif"/> </inline-formula>, in which <inline-formula><tex-math notation="LaTeX">m</tex-math> <mml:math><mml:mi>m</mml:mi></mml:math><inline-graphic xlink:href="guan-ieq2-2990282.gif"/> </inline-formula> is the number of processors in the system. Experiments show that DAG-Fluid performs better than the state of the art scheduling algorithms.]]></description><identifier>ISSN: 0018-9340</identifier><identifier>EISSN: 1557-9956</identifier><identifier>DOI: 10.1109/TC.2020.2990282</identifier><identifier>CODEN: ITCOB4</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Augmentation ; multi-processor system ; Optimal scheduling ; parallel task ; Program processors ; Real time ; Real-time scheduling ; Real-time systems ; Scheduling ; Scheduling algorithms ; Task analysis ; Task scheduling</subject><ispartof>IEEE transactions on computers, 2021-03, Vol.70 (3), p.471-482</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c289t-d817b45791f8eec0679f038fc0798be20be0b0c391fec4904794a09eddb9a9e23</citedby><cites>FETCH-LOGICAL-c289t-d817b45791f8eec0679f038fc0798be20be0b0c391fec4904794a09eddb9a9e23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9078838$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9078838$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Guan, Fei</creatorcontrib><creatorcontrib>Qiao, Jiaqing</creatorcontrib><creatorcontrib>Han, Yu</creatorcontrib><title>DAG-Fluid: A Real-Time Scheduling Algorithm for DAGs</title><title>IEEE transactions on computers</title><addtitle>TC</addtitle><description><![CDATA[Various scheduling algorithms have been proposed for real-time parallel tasks modeled as a Directed Acyclic Graph (DAG). The capacity augmentation bound is a quantitative metric widely used in this field to compare the algorithms. Among the existing algorithms, the lowest capacity augmentation bound for DAG tasks with implicit deadlines is 2, which has been achieved by federated scheduling. To improve the schedulability and lower the capacity augmentation bound, this paper proposes DAG-Fluid, an algorithm based on fluid scheduling. We prove that DAG-Fluid has a capacity augmentation bound of <inline-formula><tex-math notation="LaTeX">2-\frac{1}{m+1}</tex-math> <mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>-</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>m</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:math><inline-graphic xlink:href="guan-ieq1-2990282.gif"/> </inline-formula>, in which <inline-formula><tex-math notation="LaTeX">m</tex-math> <mml:math><mml:mi>m</mml:mi></mml:math><inline-graphic xlink:href="guan-ieq2-2990282.gif"/> </inline-formula> is the number of processors in the system. Experiments show that DAG-Fluid performs better than the state of the art scheduling algorithms.]]></description><subject>Algorithms</subject><subject>Augmentation</subject><subject>multi-processor system</subject><subject>Optimal scheduling</subject><subject>parallel task</subject><subject>Program processors</subject><subject>Real time</subject><subject>Real-time scheduling</subject><subject>Real-time systems</subject><subject>Scheduling</subject><subject>Scheduling algorithms</subject><subject>Task analysis</subject><subject>Task scheduling</subject><issn>0018-9340</issn><issn>1557-9956</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kDFPwzAQhS0EEqUwM7BEYnZ7dpzaxxYFWpAqIUGYrcS5tKnSptjNwL9vqlZMN7zvvZM-xh4FTIQAnObZRIKEiUQEaeQVG4kk0RwxmV2zEYAwHGMFt-wuhA0AzCTgiKnXdMHnbd9UL1EafVHR8rzZUvTt1lT1bbNbRWm76nxzWG-juvPRwId7dlMXbaCHyx2zn_lbnr3z5efiI0uX3EmDB14ZoUuVaBS1IXIw01hDbGoHGk1JEkqCElw85OQUgtKoCkCqqhILJBmP2fN5d--7357CwW663u-Gl1YqY7RKlFQDNT1TzncheKrt3jfbwv9ZAfakxuaZPamxFzVD4-ncaIjon0bQxsQmPgKgXVw2</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Guan, Fei</creator><creator>Qiao, Jiaqing</creator><creator>Han, Yu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20210301</creationdate><title>DAG-Fluid: A Real-Time Scheduling Algorithm for DAGs</title><author>Guan, Fei ; Qiao, Jiaqing ; Han, Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c289t-d817b45791f8eec0679f038fc0798be20be0b0c391fec4904794a09eddb9a9e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Augmentation</topic><topic>multi-processor system</topic><topic>Optimal scheduling</topic><topic>parallel task</topic><topic>Program processors</topic><topic>Real time</topic><topic>Real-time scheduling</topic><topic>Real-time systems</topic><topic>Scheduling</topic><topic>Scheduling algorithms</topic><topic>Task analysis</topic><topic>Task scheduling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guan, Fei</creatorcontrib><creatorcontrib>Qiao, Jiaqing</creatorcontrib><creatorcontrib>Han, Yu</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on computers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Guan, Fei</au><au>Qiao, Jiaqing</au><au>Han, Yu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>DAG-Fluid: A Real-Time Scheduling Algorithm for DAGs</atitle><jtitle>IEEE transactions on computers</jtitle><stitle>TC</stitle><date>2021-03-01</date><risdate>2021</risdate><volume>70</volume><issue>3</issue><spage>471</spage><epage>482</epage><pages>471-482</pages><issn>0018-9340</issn><eissn>1557-9956</eissn><coden>ITCOB4</coden><abstract><![CDATA[Various scheduling algorithms have been proposed for real-time parallel tasks modeled as a Directed Acyclic Graph (DAG). The capacity augmentation bound is a quantitative metric widely used in this field to compare the algorithms. Among the existing algorithms, the lowest capacity augmentation bound for DAG tasks with implicit deadlines is 2, which has been achieved by federated scheduling. To improve the schedulability and lower the capacity augmentation bound, this paper proposes DAG-Fluid, an algorithm based on fluid scheduling. We prove that DAG-Fluid has a capacity augmentation bound of <inline-formula><tex-math notation="LaTeX">2-\frac{1}{m+1}</tex-math> <mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>-</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>m</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:math><inline-graphic xlink:href="guan-ieq1-2990282.gif"/> </inline-formula>, in which <inline-formula><tex-math notation="LaTeX">m</tex-math> <mml:math><mml:mi>m</mml:mi></mml:math><inline-graphic xlink:href="guan-ieq2-2990282.gif"/> </inline-formula> is the number of processors in the system. Experiments show that DAG-Fluid performs better than the state of the art scheduling algorithms.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TC.2020.2990282</doi><tpages>12</tpages></addata></record> |
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| subjects | Algorithms Augmentation multi-processor system Optimal scheduling parallel task Program processors Real time Real-time scheduling Real-time systems Scheduling Scheduling algorithms Task analysis Task scheduling |
| title | DAG-Fluid: A Real-Time Scheduling Algorithm for DAGs |
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