Tighter price of anarchy for selfish task allocation on selfish machines
Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish cl...
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| Autor*in: |
Cheng, Xiayan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of combinatorial optimization - Springer US, 1997, 44(2020), 3 vom: 06. März, Seite 1848-1879 |
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| Übergeordnetes Werk: |
volume:44 ; year:2020 ; number:3 ; day:06 ; month:03 ; pages:1848-1879 |
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| DOI / URN: |
10.1007/s10878-020-00556-6 |
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| 520 | |a Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish client of task allocated to $$M_i$$ suffers from a cost equal to the load of $$M_i$$. Our aim is to allocate the tasks on the m machines so as to minimize the maximum completion times of the tasks on each machine. A stable allocation is referred to as a dual equilibrium (DE). We firstly show that 4/3 is tight upper bound of the Price of Anarchy(PoA) with respect to dual equilibrium for $$m\in \{3,\ldots ,9\}$$. And secondly $$(7m-6)/(5m-3)$$ is an upper bound for $$m\ge 10$$. The result is better than the existing bound of 7/5. | ||
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10.1007/s10878-020-00556-6 doi (DE-627)OLC2079634593 (DE-He213)s10878-020-00556-6-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Cheng, Xiayan verfasserin aut Tighter price of anarchy for selfish task allocation on selfish machines 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish client of task allocated to $$M_i$$ suffers from a cost equal to the load of $$M_i$$. Our aim is to allocate the tasks on the m machines so as to minimize the maximum completion times of the tasks on each machine. A stable allocation is referred to as a dual equilibrium (DE). We firstly show that 4/3 is tight upper bound of the Price of Anarchy(PoA) with respect to dual equilibrium for $$m\in \{3,\ldots ,9\}$$. And secondly $$(7m-6)/(5m-3)$$ is an upper bound for $$m\ge 10$$. The result is better than the existing bound of 7/5. Price of anarchy Dual equilibrium Schedule Game theory Li, Rongheng (orcid)0000-0003-2821-891X aut Zhou, Yunxia aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2020), 3 vom: 06. März, Seite 1848-1879 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2020 number:3 day:06 month:03 pages:1848-1879 https://doi.org/10.1007/s10878-020-00556-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2020 3 06 03 1848-1879 |
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10.1007/s10878-020-00556-6 doi (DE-627)OLC2079634593 (DE-He213)s10878-020-00556-6-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Cheng, Xiayan verfasserin aut Tighter price of anarchy for selfish task allocation on selfish machines 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish client of task allocated to $$M_i$$ suffers from a cost equal to the load of $$M_i$$. Our aim is to allocate the tasks on the m machines so as to minimize the maximum completion times of the tasks on each machine. A stable allocation is referred to as a dual equilibrium (DE). We firstly show that 4/3 is tight upper bound of the Price of Anarchy(PoA) with respect to dual equilibrium for $$m\in \{3,\ldots ,9\}$$. And secondly $$(7m-6)/(5m-3)$$ is an upper bound for $$m\ge 10$$. The result is better than the existing bound of 7/5. Price of anarchy Dual equilibrium Schedule Game theory Li, Rongheng (orcid)0000-0003-2821-891X aut Zhou, Yunxia aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2020), 3 vom: 06. März, Seite 1848-1879 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2020 number:3 day:06 month:03 pages:1848-1879 https://doi.org/10.1007/s10878-020-00556-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2020 3 06 03 1848-1879 |
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10.1007/s10878-020-00556-6 doi (DE-627)OLC2079634593 (DE-He213)s10878-020-00556-6-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Cheng, Xiayan verfasserin aut Tighter price of anarchy for selfish task allocation on selfish machines 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish client of task allocated to $$M_i$$ suffers from a cost equal to the load of $$M_i$$. Our aim is to allocate the tasks on the m machines so as to minimize the maximum completion times of the tasks on each machine. A stable allocation is referred to as a dual equilibrium (DE). We firstly show that 4/3 is tight upper bound of the Price of Anarchy(PoA) with respect to dual equilibrium for $$m\in \{3,\ldots ,9\}$$. And secondly $$(7m-6)/(5m-3)$$ is an upper bound for $$m\ge 10$$. The result is better than the existing bound of 7/5. Price of anarchy Dual equilibrium Schedule Game theory Li, Rongheng (orcid)0000-0003-2821-891X aut Zhou, Yunxia aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2020), 3 vom: 06. März, Seite 1848-1879 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2020 number:3 day:06 month:03 pages:1848-1879 https://doi.org/10.1007/s10878-020-00556-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2020 3 06 03 1848-1879 |
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10.1007/s10878-020-00556-6 doi (DE-627)OLC2079634593 (DE-He213)s10878-020-00556-6-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Cheng, Xiayan verfasserin aut Tighter price of anarchy for selfish task allocation on selfish machines 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish client of task allocated to $$M_i$$ suffers from a cost equal to the load of $$M_i$$. Our aim is to allocate the tasks on the m machines so as to minimize the maximum completion times of the tasks on each machine. A stable allocation is referred to as a dual equilibrium (DE). We firstly show that 4/3 is tight upper bound of the Price of Anarchy(PoA) with respect to dual equilibrium for $$m\in \{3,\ldots ,9\}$$. And secondly $$(7m-6)/(5m-3)$$ is an upper bound for $$m\ge 10$$. The result is better than the existing bound of 7/5. Price of anarchy Dual equilibrium Schedule Game theory Li, Rongheng (orcid)0000-0003-2821-891X aut Zhou, Yunxia aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2020), 3 vom: 06. März, Seite 1848-1879 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2020 number:3 day:06 month:03 pages:1848-1879 https://doi.org/10.1007/s10878-020-00556-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2020 3 06 03 1848-1879 |
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10.1007/s10878-020-00556-6 doi (DE-627)OLC2079634593 (DE-He213)s10878-020-00556-6-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Cheng, Xiayan verfasserin aut Tighter price of anarchy for selfish task allocation on selfish machines 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish client of task allocated to $$M_i$$ suffers from a cost equal to the load of $$M_i$$. Our aim is to allocate the tasks on the m machines so as to minimize the maximum completion times of the tasks on each machine. A stable allocation is referred to as a dual equilibrium (DE). We firstly show that 4/3 is tight upper bound of the Price of Anarchy(PoA) with respect to dual equilibrium for $$m\in \{3,\ldots ,9\}$$. And secondly $$(7m-6)/(5m-3)$$ is an upper bound for $$m\ge 10$$. The result is better than the existing bound of 7/5. Price of anarchy Dual equilibrium Schedule Game theory Li, Rongheng (orcid)0000-0003-2821-891X aut Zhou, Yunxia aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2020), 3 vom: 06. März, Seite 1848-1879 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2020 number:3 day:06 month:03 pages:1848-1879 https://doi.org/10.1007/s10878-020-00556-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2020 3 06 03 1848-1879 |
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Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish client of task allocated to $$M_i$$ suffers from a cost equal to the load of $$M_i$$. Our aim is to allocate the tasks on the m machines so as to minimize the maximum completion times of the tasks on each machine. A stable allocation is referred to as a dual equilibrium (DE). We firstly show that 4/3 is tight upper bound of the Price of Anarchy(PoA) with respect to dual equilibrium for $$m\in \{3,\ldots ,9\}$$. And secondly $$(7m-6)/(5m-3)$$ is an upper bound for $$m\ge 10$$. The result is better than the existing bound of 7/5. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish client of task allocated to $$M_i$$ suffers from a cost equal to the load of $$M_i$$. Our aim is to allocate the tasks on the m machines so as to minimize the maximum completion times of the tasks on each machine. A stable allocation is referred to as a dual equilibrium (DE). We firstly show that 4/3 is tight upper bound of the Price of Anarchy(PoA) with respect to dual equilibrium for $$m\in \{3,\ldots ,9\}$$. And secondly $$(7m-6)/(5m-3)$$ is an upper bound for $$m\ge 10$$. The result is better than the existing bound of 7/5. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Given a set $$L = \{J_1,J_2,\ldots ,J_n\}$$ of n tasks and a set $$M = \{M_1,M_2, \ldots ,M_m\}$$ of m identical machines, in which tasks and machines are possessed by different selfish clients. Each selfish client of machine $$M_i \in M$$ gets a profit equal to its load and each selfish client of task allocated to $$M_i$$ suffers from a cost equal to the load of $$M_i$$. Our aim is to allocate the tasks on the m machines so as to minimize the maximum completion times of the tasks on each machine. A stable allocation is referred to as a dual equilibrium (DE). We firstly show that 4/3 is tight upper bound of the Price of Anarchy(PoA) with respect to dual equilibrium for $$m\in \{3,\ldots ,9\}$$. And secondly $$(7m-6)/(5m-3)$$ is an upper bound for $$m\ge 10$$. The result is better than the existing bound of 7/5. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Tighter price of anarchy for selfish task allocation on selfish machines |
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Li, Rongheng Zhou, Yunxia |
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