M
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.
Papers
More filters
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
Nate Foster,Arjun Guha,Mark Reitblatt,Alec Story,Michael J. Freedman,Naga Praveen Kumar Katta,Christopher Monsanto,Joshua Reich,Jennifer Rexford,Cole Schlesinger,David Walker,Rob Harrison +11 more
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
Martin Casado,Michael J. Freedman,Justin Pettit,Jianying Luo,Natasha Gude,Nick McKeown,Scott Shenker +6 more
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.