scispace - formally typeset
J

Jon Crowcroft

Researcher at University of Cambridge

Publications -  692
Citations -  40720

Jon Crowcroft is an academic researcher from University of Cambridge. The author has contributed to research in topics: The Internet & Multicast. The author has an hindex of 87, co-authored 672 publications receiving 38848 citations. Previous affiliations of Jon Crowcroft include Memorial University of Newfoundland & Information Technology University.

Papers
More filters
Proceedings ArticleDOI

Unikernels: library operating systems for the cloud

TL;DR: The Mirage prototype compiles OCaml code into unikernels that run on commodity clouds and offer an order of magnitude reduction in code size without significant performance penalty, and demonstrates that the hypervisor is a platform that overcomes the hardware compatibility issues that have made past library operating systems impractical to deploy in the real-world.
Proceedings ArticleDOI

Our Twitter Profiles, Our Selves: Predicting Personality with Twitter

TL;DR: It is argued that being able to predict user personality goes well beyond the initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media.
Proceedings ArticleDOI

Distributed community detection in delay tolerant networks

TL;DR: This work proposes and evaluates three novel distributed community detection approaches with great potential to detect both static and temporal communities and finds that with suitable configuration of the threshold values, the distributedcommunity detection can approximate their corresponding centralised methods up to 90% accuracy.
Journal ArticleDOI

Multipoint communication: a survey of protocols, functions, and mechanisms

TL;DR: In this article, a survey of protocol functions and mechanisms for data transmission within a group, from multicast routing problems up to end-to-end multipoint transmission control is presented.
Journal ArticleDOI

Towards real-time community detection in large networks.

TL;DR: Experiments and benchmarks reveal that the extended algorithm is not only faster but its community detection accuracy compares favorably over popular modularity-gain optimization algorithms known to suffer from their resolution limits.