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Michael J. Freedman

Researcher at Massachusetts Institute of Technology

Publications -  155
Citations -  16221

Michael J. Freedman is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Server & Web server. The author has an hindex of 54, co-authored 150 publications receiving 15169 citations. Previous affiliations of Michael J. Freedman include Center for Information Technology & Hebrew University of Jerusalem.

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Proceedings ArticleDOI

DONAR: decentralized server selection for cloud services

TL;DR: This paper presents DONAR, a distributed system that can offload the burden of replica selection, while providing these services with a sufficiently expressive interface for specifying mapping policies, and demonstrates that the distributed algorithm is stable and effective.
Journal ArticleDOI

Languages for software-defined networks

TL;DR: The Frenetic project is designing simple and intuitive abstractions for programming the three main stages of network management: monitoring network traffic, specifying and composing packet forwarding policies, and updating policies in a consistent way to reach SDNs full potential.
Journal ArticleDOI

Rethinking enterprise network control

TL;DR: Ethane allows managers to define a single network-wide fine-grain policy and then enforces it directly, and is compatible with existing high-fanout switches by porting it to popular commodity switching chipsets.
Proceedings ArticleDOI

Shark: scaling file servers via cooperative caching

TL;DR: Shark is a distributed file system designed for large-scale, wide-area deployment, while also providing a drop-in replacement for local-area file systems that enables modestly-provisioned file servers to scale to hundreds of read-mostly clients while retaining traditional usability, consistency, security, and accountability.
Proceedings ArticleDOI

Algorithmic improvements for fast concurrent Cuckoo hashing

TL;DR: The design, implementation, and evaluation of a high-throughput and memory-efficient concurrent hash table that supports multiple readers and writers is presented, and performance results demonstrate that the new hash table design, based around optimistic cuckoo hashing, outperforms other optimized concurrent hash tables by up to 2.5x for write-heavy workloads, even while using substantially less memory for small key-value items.