@article{isci05longterm,
  abstract = {Computer systems increasingly rely on adaptive dynamic management of their operations to balance power and performance goals. Such dynamic adjustments rely heavily on the system's ability to observe and predict workload behavior and system responses. The authors characterize the workload behavior of full benchmarks running on server-class systems using hardware performance counters. Based on these characterizations, they developed a set of long-term value, gradient, and duration prediction techniques that can help systems to provision resources.},
  added-at = {2009-08-27T15:15:05.000+0200},
  address = {Los Alamitos, CA, USA},
  author = {Isci, Canturk and Buyuktosunoglu, Alper and Martonosi, Margaret},
  biburl = {http://www.bibsonomy.org/bibtex/2a05ae650e52a217ce6f18f4b5dc8b7cd/derkling},
  description = {Long-Term Workload Phases: Duration Predictions and Applications to DVFS},
  doi = {http://doi.ieeecomputersociety.org/10.1109/MM.2005.93},
  interhash = {d15853a104f02c34e270384a2b4adbec},
  intrahash = {a05ae650e52a217ce6f18f4b5dc8b7cd},
  issn = {0272-1732},
  journal = {IEEE Micro},
  keywords = {DVFS PhD_Background_Mechanisms PhD_Biblio},
  number = 5,
  pages = {39-51},
  publisher = {IEEE Computer Society},
  timestamp = {2009-08-27T15:15:05.000+0200},
  title = {Long-Term Workload Phases: Duration Predictions and Applications to DVFS},
  url = {http://www2.computer.org/portal/web/csdl/doi/10.1109/MM.2005.93},
  volume = 25,
  year = 2005
}