The Community for Technology Leaders
RSS Icon
Subscribe
Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 409-412
Lory Al Moakar , Department of Computer Science, University of Pittsburgh, USA
Panos K. Chrysanthis , Department of Computer Science, University of Pittsburgh, USA
Christine Chung , Department of Computer Science, Connecticut College, USA
Shenoda Guirguis , Department of Computer Science, University of Pittsburgh, USA
Alexandros Labrinidis , Department of Computer Science, University of Pittsburgh, USA
Panayiotis Neophytou , Department of Computer Science, University of Pittsburgh, USA
Kirk Pruhs , Department of Computer Science, University of Pittsburgh, USA
ABSTRACT
Amazon, Google, and IBM now sell cloud computing services.We consider the setting of a for-profit business selling data stream monitoring/management services and we investigate auction-based mechanisms for admission control of continuous queries. When submitting a query, each user also submits a bid of how much she is willing to pay for that query to run. The admission control auction mechanism then determines which queries to admit, and how much to charge each user in a way that maximizes system revenue while being strategyproof and sybil immune, incentivizing users to use the system honestly. Specifically, we require that each user maximizes her payoff by bidding her true value of having her query run. We design several payment mechanisms and experimentally evaluate them. We describe the provable game theoretic characteristics of each mechanism alongside its performance with respect to maximizing profit and total user payoff.
CITATION
Lory Al Moakar, Panos K. Chrysanthis, Christine Chung, Shenoda Guirguis, Alexandros Labrinidis, Panayiotis Neophytou, Kirk Pruhs, "Admission control mechanisms for continuous queries in the cloud", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 409-412, doi:10.1109/ICDE.2010.5447822
5 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool