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Elisha J. Rosensweig
Researcher at University of Massachusetts Amherst
Publications - 19
Citations - 756
Elisha J. Rosensweig is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Cache & Cloud computing. The author has an hindex of 11, co-authored 19 publications receiving 728 citations. Previous affiliations of Elisha J. Rosensweig include Alcatel-Lucent & Bell Labs.
Papers
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Proceedings ArticleDOI
Approximate Models for General Cache Networks
TL;DR: A new algorithm is proposed, termed a-Net, that approximates the behavior of multi-cache networks by leveraging existing approximation algorithms for isolated LRU caches and it is demonstrated the utility of a- net using both per- cache and network-wide performance measures.
Proceedings ArticleDOI
Breadcrumbs: Efficient, Best-Effort Content Location in Cache Networks
Elisha J. Rosensweig,Jim Kurose +1 more
TL;DR: This paper considers a network in which each router has a local cache that caches files passing through it and develops a simple content caching, location, and routing systems that adopts an implicit, transparent, and best-effort approach towards caching.
Proceedings ArticleDOI
On the steady-state of cache networks
TL;DR: This work demonstrates that certain cache networks are non-ergodic in that their steady-state characterization depends on the initial state of the system, and establishes several important properties of cache networks, in the form of three independently-sufficient conditions for a cache network to comprise a single ergodic component.
Patent
Method and apparatus for automated deployment of geographically distributed applications within a cloud
Elisha J. Rosensweig,Etti Shalev,Sharon Mendel,Amir Rosenfeld,Sivan Brazilay,Ranny Haiby,Itamar Eshet +6 more
Proceedings ArticleDOI
Can machine learning aid in delivering new use cases and scenarios in 5G
Teodora Sandra Buda,Haytham Assem,Lei Xu,Danny Raz,Udi Margolin,Elisha J. Rosensweig,Diego R. Lopez,Marius-Iulian Corici,Mikhail Smirnov,Robert Mullins,Olga Uryupina,Alberto Mozo,Bruno Ordozgoiti,Angel Martin,Alaa Alloush,Patrick J. O'Sullivan,Imen Grida Ben Yahia +16 more
TL;DR: It is expected that machine learning can provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network.