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One important factor is the performance of a Cloud service. Performance can be measured on a technical basis and has an economical impact when contrasted to costs [1]. However, measuring the performance of all existing Cloud providers and all offered services is very costly and time consuming. An advanced approach that can reduce the effort tremendously has been introduced by Haak and Menzel [2]. The complexity of the approach is high what makes it practically unusable in manual application.  
 
One important factor is the performance of a Cloud service. Performance can be measured on a technical basis and has an economical impact when contrasted to costs [1]. However, measuring the performance of all existing Cloud providers and all offered services is very costly and time consuming. An advanced approach that can reduce the effort tremendously has been introduced by Haak and Menzel [2]. The complexity of the approach is high what makes it practically unusable in manual application.  
 
Therefore, an automated and extensible benchmarking tool must implement the approach. The tool must support the measurement of Cloud service performances on multiple metrics and for different service types, and employ the stopping theory to reduce the overall cost and time effort.
 
Therefore, an automated and extensible benchmarking tool must implement the approach. The tool must support the measurement of Cloud service performances on multiple metrics and for different service types, and employ the stopping theory to reduce the overall cost and time effort.
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References:
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[1] Alexander Lenk, Michael Menzel, Johannes Lipsky, Philipp Offermann, Stefan Tai. “What are you paying for? Performance benchmarking for Infrastructure-as-a-Service offerings”. Proceedings of the 4th IEEE International Conference on Cloud Computing (CLOUD 2011), Washington, D.C., July, 2011.
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[2] Steffen Haak, Michael Menzel. “Autonomic Benchmarking for Cloud Infrastructures – An Economic Optimization Model”. Proceedings of the 1st IEEE/ACM Workshop on Autonomic Computing for Economics, in print, Karlsruhe, June, 2011.
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[3] http://www.phoronix-test-suite.com
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[4] http://code.google.com/p/aotearoadecisions
 
|Beschreibung EN=Cloud Computing has become a major trend for Web applications since Cloud technologies provide many benefits such as elasticity and pay-per-use billing. Over time many Cloud provider have entered the stage offering services to run a Web application in the Cloud. Choosing a Cloud provider is complex and requires to consider multiple factors, such as service costs, service levels, switching costs or technological requirements.  
 
|Beschreibung EN=Cloud Computing has become a major trend for Web applications since Cloud technologies provide many benefits such as elasticity and pay-per-use billing. Over time many Cloud provider have entered the stage offering services to run a Web application in the Cloud. Choosing a Cloud provider is complex and requires to consider multiple factors, such as service costs, service levels, switching costs or technological requirements.  
 
One important factor is the performance of a Cloud service. Performance can be measured on a technical basis and has an economical impact when contrasted to costs [1]. However, measuring the performance of all existing Cloud providers and all offered services is very costly and time consuming. An advanced approach that can reduce the effort tremendously has been introduced by Haak and Menzel [2]. The complexity of the approach is high what makes it practically unusable in manual application.  
 
One important factor is the performance of a Cloud service. Performance can be measured on a technical basis and has an economical impact when contrasted to costs [1]. However, measuring the performance of all existing Cloud providers and all offered services is very costly and time consuming. An advanced approach that can reduce the effort tremendously has been introduced by Haak and Menzel [2]. The complexity of the approach is high what makes it practically unusable in manual application.  
 
Therefore, an automated and extensible benchmarking tool must implement the approach. The tool must support the measurement of Cloud service performances on multiple metrics and for different service types, and employ the stopping theory to reduce the overall cost and time effort.
 
Therefore, an automated and extensible benchmarking tool must implement the approach. The tool must support the measurement of Cloud service performances on multiple metrics and for different service types, and employ the stopping theory to reduce the overall cost and time effort.
 +
 +
References:
 +
 +
[1] Alexander Lenk, Michael Menzel, Johannes Lipsky, Philipp Offermann, Stefan Tai. “What are you paying for? Performance benchmarking for Infrastructure-as-a-Service offerings”. Proceedings of the 4th IEEE International Conference on Cloud Computing (CLOUD 2011), Washington, D.C., July, 2011.
 +
 +
[2] Steffen Haak, Michael Menzel. “Autonomic Benchmarking for Cloud Infrastructures – An Economic Optimization Model”. Proceedings of the 1st IEEE/ACM Workshop on Autonomic Computing for Economics, in print, Karlsruhe, June, 2011.
 +
 +
[3] http://www.phoronix-test-suite.com
 +
 +
[4] http://code.google.com/p/aotearoadecisions
 
|Betreuer=Michael Menzel;
 
|Betreuer=Michael Menzel;
 
|Ausschreibung=Ausschreibung intelligent Benchmarking.pdf,
 
|Ausschreibung=Ausschreibung intelligent Benchmarking.pdf,
 
|Forschungsgruppe=Ökonomie und Technologie der eOrganisation
 
|Forschungsgruppe=Ökonomie und Technologie der eOrganisation
 
}}
 
}}
[[Datei:Ausschreibung intelligent Benchmarking.pdf]]
 
  
 
Bewerbungen an [mailto:michael.menzel@kit.edu Michael Menzel].
 
Bewerbungen an [mailto:michael.menzel@kit.edu Michael Menzel].

Version vom 8. August 2011, 16:15 Uhr



Intelligent Cloud Benchmarking




Informationen zur Arbeit

Abschlussarbeitstyp: Bachelor, Master, Diplom
Betreuer: Michael Menzel
Forschungsgruppe: Ökonomie und Technologie der eOrganisation

Archivierungsnummer: 3321
Abschlussarbeitsstatus: Offen
Beginn: unbekannt
Abgabe: unbekannt

Weitere Informationen

Cloud Computing has become a major trend for Web applications since Cloud technologies provide many benefits such as elasticity and pay-per-use billing. Over time many Cloud provider have entered the stage offering services to run a Web application in the Cloud. Choosing a Cloud provider is complex and requires to consider multiple factors, such as service costs, service levels, switching costs or technological requirements. One important factor is the performance of a Cloud service. Performance can be measured on a technical basis and has an economical impact when contrasted to costs [1]. However, measuring the performance of all existing Cloud providers and all offered services is very costly and time consuming. An advanced approach that can reduce the effort tremendously has been introduced by Haak and Menzel [2]. The complexity of the approach is high what makes it practically unusable in manual application. Therefore, an automated and extensible benchmarking tool must implement the approach. The tool must support the measurement of Cloud service performances on multiple metrics and for different service types, and employ the stopping theory to reduce the overall cost and time effort.

References:

[1] Alexander Lenk, Michael Menzel, Johannes Lipsky, Philipp Offermann, Stefan Tai. “What are you paying for? Performance benchmarking for Infrastructure-as-a-Service offerings”. Proceedings of the 4th IEEE International Conference on Cloud Computing (CLOUD 2011), Washington, D.C., July, 2011.

[2] Steffen Haak, Michael Menzel. “Autonomic Benchmarking for Cloud Infrastructures – An Economic Optimization Model”. Proceedings of the 1st IEEE/ACM Workshop on Autonomic Computing for Economics, in print, Karlsruhe, June, 2011.

[3] http://www.phoronix-test-suite.com

[4] http://code.google.com/p/aotearoadecisions


Ausschreibung: Download (pdf)



Bewerbungen an Michael Menzel.