Summary cache: a scalable wide-area web cache sharing protocol

By Li Fan, Pei Cao, Jussara M. Almeida, Andrei Z. Broder. 2000

2,397 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture

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In Computer Networks and Communications

In Hardware and Architecture

Citations in the corpus

Secure Federated Submodel Learning (2019, methodology): “We ﬁnally present a generalized version of Bloom ﬁlter, called counting Bloom ﬁlter [71].”
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning (2016, methodology): “Counting Bloom ﬁlters (Fan et al., 2000) maintain a counter n ( · ) for each number between 1 and p .”
Ad click prediction: a view from the trenches (2013, methodology): “We use a rolling set of counting Bloom filters [4, 12] to detect the first n times a feature is encountered in training.”
World Wide Web caching: trends and techniques (2000, methodology): “In that sense, it is very similar to the CRP protocol used by the adaptive caching project, as well as the cache digest approach championed by both Summary Cache and Squid-2.”
Cuckoo Filter: Practically Better Than Bloom (2014, methodology): “Counting Bloom ﬁlters [12] have been suggested for multiple applications [24, 25, 9], but they generally use 3–4 × space to retain the same false positive rate as a space-optimized Bloom ﬁlter.”
Secure Indexes (2003, methodology): “Therefore, we use “counting Bloom ﬁlters” [12] at all non-leaf nodes.”
Democratizing Content Publication with Coral (2004, background): “Prior to the recent interest in peer-to-peer systems, several projects proposed cooperative Web caching [2, 7, 8, 16].”
Single-packet IP traceback (2002, background): “rate of such false positives can be controlled by only allowing an individual Bloom filter to store a limited number of digests [18].”
Efficient Peer-to-Peer Keyword Searching (2003, background): “A Bloom filter [2, 7, 12] is a hash-based data structure that efficiently represents membership in a set.”
Network Applications of Bloom Filters: A Survey (2004, methodology): “Fan, Cao, Almeida, and Broder describe Summary Cache, which uses Bloom ﬁ lters for Web cache sharing [Fan et al. 00].”
Giggle: A Framework for Constructing Scalable Replica Location Services (2002, methodology): “Summary Cache [12] uses Bloom filters (dubbed “cache summaries”) as compact representations for the local set of cached files.”
Virtualizing transactional memory (2005, neutral): “Analysis of the trade-offs among filter size, counter size, and the number of hash functions can be found in the literature (see, for example, [6]), Experimental work [21] suggests that a family of linear congruences work well for hash functions.”
Distributed Caching Algorithms for Content Distribution Networks (2010, background): “We assume that demand estimates are given, and that suitable mechanisms for on-line content look-up and request routing are available, e.g. the distributed directory services in [2], [9], [15], [18].”
Epidemic Routing for Partially-Connected Ad Hoc Networks (2009, background): “While not explored here, a “Bloom filter” [4, 12] would substantially reduce the space overhead associated with the summary vector.”
OceanStore: an architecture for global-scale persistent storage (2000, background): “The Summary Cache [16] pushes Bloom .lters between cooperating web caches, although their method does not scale well in the number of caches.”
Identifying Dynamic Replication Strategies for a High-Performance Data Grid (2001, methodology): “Strategy 6: Fast Spread- In this method a replica of the file is stored at each node along its path to the client [3].”