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Rebecca Isaacs
Researcher at Microsoft
Publications - 46
Citations - 3749
Rebecca Isaacs is an academic researcher from Microsoft. The author has contributed to research in topics: Network management station & Network management. The author has an hindex of 21, co-authored 45 publications receiving 3548 citations. Previous affiliations of Rebecca Isaacs include Google & Sprint Corporation.
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Proceedings ArticleDOI
The multikernel: a new OS architecture for scalable multicore systems
Andrew Baumann,Paul Barham,Pierre-Évariste Dagand,Tim Harris,Rebecca Isaacs,Simon Peter,Timothy Roscoe,Adrian Schüpbach,Akhilesh Singhania +8 more
TL;DR: This work investigates a new OS structure, the multikernel, that treats the machine as a network of independent cores, assumes no inter-core sharing at the lowest level, and moves traditional OS functionality to a distributed system of processes that communicate via message-passing.
Proceedings ArticleDOI
Naiad: a timely dataflow system
TL;DR: It is shown that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining.
Proceedings Article
Using magpie for request extraction and workload modelling
TL;DR: This paper describes and evaluates the capability of Magpie to accurately extract requests and construct representative models of system behaviour, and constructs concise workload models suitable for performance prediction and change detection.
Proceedings Article
Magpie: online modelling and performance-aware systems
TL;DR: The Magpie modelling service is described which collates detailed traces from multiple machines in an e-commerce site, extracts request-specific audit trails, and constructs probabilistic models of request behaviour.
Differential dataflow
TL;DR: Differential computation as discussed by the authors extends traditional incremental computation to allow arbitrarily nested iteration, and explains how differential computation can be efficiently implemented in the context of a declarative data-parallel dataflow language.