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Conference Paper: Towards payment-bound analysis in cloud systems with task-prediction errors

TitleTowards payment-bound analysis in cloud systems with task-prediction errors
Authors
Issue Date2013
Citation
The 6th International Conference on Cloud Computing (CLOUD 2013), Santa Clara Marriott, CA., 27 June-2 July 2013. How to Cite?
AbstractIn modern cloud systems, how to optimize user service level based on virtual resources customized on demand is a critical issue. In this paper, we comprehensively analyze the payment bound under a cloud model with virtual machines (VMs), by taking into account that task’s workload may be predicted with errors. The analysis is based on an optimized resource allocation algorithm with polynomial time complexity. We theoretically derive the upper bound of task payment based on a particular margin of workload prediction-error. We also extend the payment-minimization algorithm to adapt to the dynamic changes of host availability over time, and perform the evaluation by a real-cluster environment with 56 VMs deployed. Experiments confirm the correctness of our theoretical inference, and show that our payment-minimization solution can keep 95% of user payments below 1.15 times as large as the theoretical values of the ideal payment with hypothetically accurate information. The ratio for the rest user payments can be limited to about 1.5 at the worst case.
DescriptionConference Theme: Change we are leading
Persistent Identifierhttp://hdl.handle.net/10722/189639

 

DC FieldValueLanguage
dc.contributor.authorDi, Sen_US
dc.contributor.authorWang, CLen_US
dc.contributor.authorKondo, Den_US
dc.contributor.authorHan, G-
dc.date.accessioned2013-09-17T14:50:34Z-
dc.date.available2013-09-17T14:50:34Z-
dc.date.issued2013en_US
dc.identifier.citationThe 6th International Conference on Cloud Computing (CLOUD 2013), Santa Clara Marriott, CA., 27 June-2 July 2013.en_US
dc.identifier.urihttp://hdl.handle.net/10722/189639-
dc.descriptionConference Theme: Change we are leading-
dc.description.abstractIn modern cloud systems, how to optimize user service level based on virtual resources customized on demand is a critical issue. In this paper, we comprehensively analyze the payment bound under a cloud model with virtual machines (VMs), by taking into account that task’s workload may be predicted with errors. The analysis is based on an optimized resource allocation algorithm with polynomial time complexity. We theoretically derive the upper bound of task payment based on a particular margin of workload prediction-error. We also extend the payment-minimization algorithm to adapt to the dynamic changes of host availability over time, and perform the evaluation by a real-cluster environment with 56 VMs deployed. Experiments confirm the correctness of our theoretical inference, and show that our payment-minimization solution can keep 95% of user payments below 1.15 times as large as the theoretical values of the ideal payment with hypothetically accurate information. The ratio for the rest user payments can be limited to about 1.5 at the worst case.-
dc.languageengen_US
dc.relation.ispartofIEEE International Conference on Cloud Computing (CLOUD)en_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleTowards payment-bound analysis in cloud systems with task-prediction errorsen_US
dc.typeConference_Paperen_US
dc.identifier.emailWang, CL: clwang@cs.hku.hken_US
dc.identifier.authorityWang, CL=rp00183en_US
dc.description.naturepostprint-
dc.identifier.hkuros223372en_US
dc.customcontrol.immutablesml 131025-

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