Adaptive Web QoS controller based on online system identification using quantum-behaved particle swarm optimization
Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Dif...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fang, Wei [verfasserIn] Sun, Jun [verfasserIn] Wu, Xiaojun [verfasserIn] Palade, Vasile [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 19(2014), 6 vom: 03. Juli, Seite 1715-1725 |
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| Übergeordnetes Werk: |
volume:19 ; year:2014 ; number:6 ; day:03 ; month:07 ; pages:1715-1725 |
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| DOI / URN: |
10.1007/s00500-014-1359-9 |
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| 520 | |a Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. | ||
| 650 | 4 | |a Particle swarm optimization |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Sun, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Xiaojun |e verfasserin |4 aut | |
| 700 | 1 | |a Palade, Vasile |e verfasserin |4 aut | |
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10.1007/s00500-014-1359-9 doi (DE-627)SPR006486983 (SPR)s00500-014-1359-9-e DE-627 ger DE-627 rakwb eng Fang, Wei verfasserin aut Adaptive Web QoS controller based on online system identification using quantum-behaved particle swarm optimization 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. Particle swarm optimization (dpeaa)DE-He213 Quantum-behaved particle swarm optimization (dpeaa)DE-He213 Web QoS (dpeaa)DE-He213 Adaptive control (dpeaa)DE-He213 Sun, Jun verfasserin aut Wu, Xiaojun verfasserin aut Palade, Vasile verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 19(2014), 6 vom: 03. Juli, Seite 1715-1725 (DE-627)SPR006469531 nnns volume:19 year:2014 number:6 day:03 month:07 pages:1715-1725 https://dx.doi.org/10.1007/s00500-014-1359-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 19 2014 6 03 07 1715-1725 |
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10.1007/s00500-014-1359-9 doi (DE-627)SPR006486983 (SPR)s00500-014-1359-9-e DE-627 ger DE-627 rakwb eng Fang, Wei verfasserin aut Adaptive Web QoS controller based on online system identification using quantum-behaved particle swarm optimization 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. Particle swarm optimization (dpeaa)DE-He213 Quantum-behaved particle swarm optimization (dpeaa)DE-He213 Web QoS (dpeaa)DE-He213 Adaptive control (dpeaa)DE-He213 Sun, Jun verfasserin aut Wu, Xiaojun verfasserin aut Palade, Vasile verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 19(2014), 6 vom: 03. Juli, Seite 1715-1725 (DE-627)SPR006469531 nnns volume:19 year:2014 number:6 day:03 month:07 pages:1715-1725 https://dx.doi.org/10.1007/s00500-014-1359-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 19 2014 6 03 07 1715-1725 |
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10.1007/s00500-014-1359-9 doi (DE-627)SPR006486983 (SPR)s00500-014-1359-9-e DE-627 ger DE-627 rakwb eng Fang, Wei verfasserin aut Adaptive Web QoS controller based on online system identification using quantum-behaved particle swarm optimization 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. Particle swarm optimization (dpeaa)DE-He213 Quantum-behaved particle swarm optimization (dpeaa)DE-He213 Web QoS (dpeaa)DE-He213 Adaptive control (dpeaa)DE-He213 Sun, Jun verfasserin aut Wu, Xiaojun verfasserin aut Palade, Vasile verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 19(2014), 6 vom: 03. Juli, Seite 1715-1725 (DE-627)SPR006469531 nnns volume:19 year:2014 number:6 day:03 month:07 pages:1715-1725 https://dx.doi.org/10.1007/s00500-014-1359-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 19 2014 6 03 07 1715-1725 |
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10.1007/s00500-014-1359-9 doi (DE-627)SPR006486983 (SPR)s00500-014-1359-9-e DE-627 ger DE-627 rakwb eng Fang, Wei verfasserin aut Adaptive Web QoS controller based on online system identification using quantum-behaved particle swarm optimization 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. Particle swarm optimization (dpeaa)DE-He213 Quantum-behaved particle swarm optimization (dpeaa)DE-He213 Web QoS (dpeaa)DE-He213 Adaptive control (dpeaa)DE-He213 Sun, Jun verfasserin aut Wu, Xiaojun verfasserin aut Palade, Vasile verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 19(2014), 6 vom: 03. Juli, Seite 1715-1725 (DE-627)SPR006469531 nnns volume:19 year:2014 number:6 day:03 month:07 pages:1715-1725 https://dx.doi.org/10.1007/s00500-014-1359-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 19 2014 6 03 07 1715-1725 |
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10.1007/s00500-014-1359-9 doi (DE-627)SPR006486983 (SPR)s00500-014-1359-9-e DE-627 ger DE-627 rakwb eng Fang, Wei verfasserin aut Adaptive Web QoS controller based on online system identification using quantum-behaved particle swarm optimization 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. Particle swarm optimization (dpeaa)DE-He213 Quantum-behaved particle swarm optimization (dpeaa)DE-He213 Web QoS (dpeaa)DE-He213 Adaptive control (dpeaa)DE-He213 Sun, Jun verfasserin aut Wu, Xiaojun verfasserin aut Palade, Vasile verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 19(2014), 6 vom: 03. Juli, Seite 1715-1725 (DE-627)SPR006469531 nnns volume:19 year:2014 number:6 day:03 month:07 pages:1715-1725 https://dx.doi.org/10.1007/s00500-014-1359-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 19 2014 6 03 07 1715-1725 |
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Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. |
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Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. |
| abstract_unstemmed |
Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006486983</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002806.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-014-1359-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006486983</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-014-1359-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fang, Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Adaptive Web QoS controller based on online system identification using quantum-behaved particle swarm optimization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Particle swarm optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quantum-behaved particle swarm optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Web QoS</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive control</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Jun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Xiaojun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Palade, Vasile</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">19(2014), 6 vom: 03. Juli, Seite 1715-1725</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:19</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:6</subfield><subfield code="g">day:03</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:1715-1725</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-014-1359-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">19</subfield><subfield code="j">2014</subfield><subfield code="e">6</subfield><subfield code="b">03</subfield><subfield code="c">07</subfield><subfield code="h">1715-1725</subfield></datafield></record></collection>
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