inproceedings_faber.bib

@inproceedings{FaHa2012-ICPE,
  address = {New York, NY, USA},
  author = {Michael Faber and Jens Happe},
  booktitle = {Proceedings of 3rd ACM/SPEC Internatioanl Conference on Performance Engineering (ICPE 2012)},
  day = {22--25},
  isbn = {978-1-4503-1202-8},
  location = {Boston, USA},
  month = {April},
  pages = {33--44},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/FaHa2012-ICPE.pdf},
  publisher = {ACM},
  title = {Systematic adoption of genetic programming for deriving software performance curves},
  url = {http://doi.acm.org/10.1145/2188286.2188295},
  year = {2012}
}

@inproceedings{FuBrFa2012-VALUETOOLS-TruthfulResourceReservation,
  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.}},
  author = {Funke, Daniel and Brosig, Fabian and Faber, Michael},
  booktitle = {{Proceedings of the 6th International ICST Conference on Performance Evaluation Methodologies and Tools (ValueTools 2012), Carg{\`e}se, France}},
  month = {October},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/FuBrFa2012-VALUETOOLS-TruthfulResourceReservation.pdf},
  title = {{Towards Truthful Resource Reservation in Cloud Computing}},
  year = {2012}
}