K
Kirk Pruhs
Researcher at University of Pittsburgh
Publications - 223
Citations - 7565
Kirk Pruhs is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Competitive analysis & Scheduling (computing). The author has an hindex of 44, co-authored 219 publications receiving 7259 citations. Previous affiliations of Kirk Pruhs include Carnegie Mellon University & University of Wisconsin-Madison.
Papers
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Proceedings Article
Speed is as Powerful as Clairvoyance.
Bala Kalyanasundaram,Kirk Pruhs +1 more
TL;DR: In this paper, the authors introduce resource augmentation as a method for analyzing online scheduling problems and show that the performance of an on-line scheduler is best-effort real-time scheduling can be significantly improved if the system is designed in such a way that the laxity of every job is proportional to its length.
Journal ArticleDOI
Speed is as powerful as clairvoyance
Bala Kalyanasundaram,Kirk Pruhs +1 more
TL;DR: The performance of an on-line scheduler is best-effort real time scheduling can be significantly improved if the system is designed in such a way that the laxity of every job is proportional to its length.
Journal ArticleDOI
Speed scaling to manage energy and temperature
TL;DR: The study of speed scaling to manage temperature is initiated and it is shown that the optimal temperature schedule can be computed offline in polynomial-time using the Ellipsoid algorithm and that no deterministic online algorithm can have a better competitive ratio.
Journal ArticleDOI
Algorithmic problems in power management
Sandy Irani,Kirk Pruhs +1 more
TL;DR: This survey places more concentration on lines of research of the authors: managing power using the techniques of speed scaling and power-down which are also currently the dominant techniques in practice.
Proceedings ArticleDOI
Dynamic speed scaling to manage energy and temperature
TL;DR: This paper considers online speed scaling algorithms to minimize the energy used subject to the constraint that every job finishes by its deadline, and provides a tight bound on the competitive ratio of the previously proposed optimal available algorithm.