{"type":"ScholarlyArticle","title":"Online Revenue Maximization for Server Pricing","authors":[{"name":"Shant Boodaghians","slug":"shant-boodaghians","given":"Shant","family":"Boodaghians","affiliation":"University of Illinois at Urbana-Champaign , Urbana IL 61801 , USA"},{"name":"Federico Fusco","slug":"federico-fusco","given":"Federico","family":"Fusco","affiliation":"Sapienza University of Rome , 00185 Rome , Italy"},{"name":"Stefano Leonardi","slug":"stefano-leonardi","given":"Stefano","family":"Leonardi","affiliation":"Sapienza University of Rome , 00185 Rome , Italy"},{"name":"Yishay Mansour","slug":"yishay-mansour","given":"Yishay","family":"Mansour","affiliation":"Tel Aviv University , P.O. Box 39040 , Tel Aviv 6997801 , Israel"},{"name":"Ruta Mehta","slug":"ruta-mehta","given":"Ruta","family":"Mehta","affiliation":"University of Illinois at Urbana-Champaign , Urbana IL 61801 , USA"}],"abstract":"Efficient and truthful mechanisms to price time on remote servers/machines have been the subject of much work in recent years due to the importance of the cloud market. This paper considers online revenue maximization for a unit capacity server, when jobs are non preemptive, in the Bayesian setting: at each time step, one job arrives, with parameters drawn from an underlying distribution.\r\nWe design an efficiently computable truthful posted price mechanism, which maximizes revenue in expectation and in retrospect, up to additive error.  The prices are posted prior to learning the agent's type, and the computed pricing scheme is deterministic.\r\nWe also show the pricing mechanism is robust to learning the job distribution from samples, where polynomially many samples suffice to obtain near optimal prices.","doi":"10.24963/ijcai.2020/568","url":"https://ijcai.org/proceedings/2020/568","pdf":"https://ijcai.org/proceedings/2020/0568.pdf","markdown":"https://ijcai.org/proceedings/2020/568.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/568","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Planning and Scheduling","track":"Main track","number":568,"pages":{"first":4106,"last":4112},"keywords":["Planning and Scheduling: Markov Decisions Processes","Planning and Scheduling: Planning under Uncertainty","Agent-based and Multi-agent Systems: Algorithmic Game Theory"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24353/file_24353.m3u8","length_sec":282,"provider":"hls"}],"references":[{"position":1,"raw":"[Azar et al., 2015] Yossi Azar, Inna Kalp-Shaltiel, Brendan Lucier, Ishai Menache, Joseph Naor, and Jonathan Yaniv. 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