Michael Faber and Jens Happe.
Systematic adoption of genetic programming for deriving software
performance curves.
In Proceedings of 3rd ACM/SPEC Internatioanl Conference on
Performance Engineering (ICPE 2012), Boston, USA, April 22-25, 2012, pages
33-44. ACM, New York, NY, USA.
April 2012.
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Daniel Funke, Fabian Brosig, and Michael Faber.
Towards Truthful Resource Reservation in Cloud Computing.
In Proceedings of the 6th International ICST Conference on
Performance Evaluation Methodologies and Tools (ValueTools 2012),
Cargèse, France, 2012.
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.pdf | Abstract ]
Prudent capacity planning to meet their clients future computational needs is one of the major issues cloud computing providers face today. By offering resource reservations in advance, providers gain insight into the projected demand of their customers and can act accordingly. However, customers need to be given an incentive, e.g. discounts granted, to commit early to a provider and to honestly, i.e., truthfully reserve their predicted future resource requirements. Customers may reserve capacity deviating from their truly predicted demand, in order to exploit the mechanism for their own benefit, thereby causing futile costs for the provider. In this paper we prove, using a game theoretic approach, that truthful reservation is the best, i.e., dominant strategy for customers if they are capable to make precise forecasts of their demands and that deviations from truth-telling can be profitable for customers if their demand forecasts are uncertain.
Michael Faber.
Software Performance Analysis using Machine Learning Techniques.
Master's thesis, Karlsruhe Institute of Technology (KIT), Karlsruhe,
Germany, March 2011.
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