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Peter Key

Researcher at Microsoft

Publications -  142
Citations -  5171

Peter Key is an academic researcher from Microsoft. The author has contributed to research in topics: Network congestion & Network packet. The author has an hindex of 39, co-authored 142 publications receiving 5042 citations. Previous affiliations of Peter Key include University of Cambridge & BT Group.

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Patent

Network routing of endpoints to content based on content swarms

TL;DR: In this paper, the authors propose a service for mapping endpoint requests to requested content using location-independent names to identify content, where content names are mapped to a dynamically generated content swarm, presenting a current set of hosts, which can provide the requested content.
Proceedings ArticleDOI

Who's hogging the bandwidth: the consequences of revealing the invisible in the home

TL;DR: The consequences of showing real time resource usage in a home are described, and how this varies depending on the social make up of the household is described.
Proceedings ArticleDOI

Performance Analysis of Contention Based Medium Access Control Protocols

TL;DR: A simple and accurate technique for estimating the throughput of the IEEE 802.11 DCF protocol is developed, based on a rigorous analysis of the Markov chain that corresponds to the time evolution of the back-off processes at the contending nodes.
Proceedings Article

Dwelling on the Negative: Incentivizing Effort in Peer Prediction

TL;DR: It is shown that negative payments can be used to make agents with quality lower than the quality threshold choose to not to participate, while those above continue to participate and invest effort, and when using the self-selection mechanism, perfect screening comes for free.
Posted Content

Budget Optimization for Sponsored Search: Censored Learning in MDPs

TL;DR: In this article, the authors consider the problem of budget optimization by an advertiser participating in repeated sponsored search auctions, seeking to maximize the number of clicks attained under that budget, and propose a learning algorithm based on the wellknown Kaplan-Meier or product limit estimator.