372:. These compute a network-wide aggregate by sampling information at the nodes in the network and combining the values to arrive at a system-wide value – the largest value for some measurement nodes are making, smallest, etc. The key requirement is that the aggregate must be computable by fixed-size pairwise information exchanges; these typically terminate after a number of rounds of information exchange logarithmic in the system size, by which time an all-to-all information flow pattern will have been established. As a side effect of aggregation, it is possible to solve other kinds of problems using gossip; for example, there are gossip protocols that can arrange the nodes in a gossip overlay into a list sorted by node-id (or some other attribute) in logarithmic time using aggregation-style exchanges of information. Similarly, there are gossip algorithms that arrange nodes into a tree and compute aggregates such as "sum" or "count" by gossiping in a pattern biased to match the tree structure.
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that he believes that
Charlie dyes his mustache. At the next meeting, Bob tells Alice, while Dave repeats the idea to Eve. After each water cooler rendezvous, the number of individuals who have heard the rumor roughly doubles (though this doesn't account for gossiping twice to the same person; perhaps Dave tries to tell the story to Frank, only to find that Frank already heard it from Alice). Computer systems typically implement this type of protocol with a form of random "peer selection": with a given frequency, each machine picks another machine at random and shares any rumors.
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support any classic protocol or implement any classical distributed service. However, such a broadly inclusive interpretation is rarely intended. More typically gossip protocols are those that specifically run in a regular, periodic, relatively lazy, symmetric and decentralized manner; the high degree of symmetry among nodes is particularly characteristic. Thus, while one could run a
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It follows that within logarithmic time in the size of the network (the number of agents), any new search string will have reached all agents. Within an additional delay of the same approximate length, every agent will learn where the best match can be found. In particular, the agent that started the
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Periodically, at some rate (let's say ten times per second, for simplicity), each agent picks some other agent at random, and gossips with it. Search strings known to A will now also be known to B, and vice versa. In the next "round" of gossip A and B will pick additional random peers, maybe C and D.
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often use gossip-like information exchanges. A gossip substrate can be used to implement a standard routed network: nodes "gossip" about traditional point-to-point messages, effectively pushing traffic through the gossip layer. Bandwidth permitting, this implies that a gossip system can potentially
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can be illustrated by the analogy of office workers spreading rumors. Let's say each hour the office workers congregate around the water cooler. Each employee pairs off with another, chosen at random, and shares the latest gossip. At the start of the day, Dave starts a new rumor: he comments to Bob
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For example, in a network with 25,000 machines, we can find the best match after about 30 rounds of gossip: 15 to spread the search string and 15 more to discover the best match. A gossip exchange could occur as often as once every tenth of a second without imposing undue load, hence this form of
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is sometimes used to describe protocols that achieve exponentially rapid spread of information. For this purpose, a protocol must propagate any new information to all nodes that will be affected by the information within time logarithmic in the size of the system (the "mixing time" must be
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Gossip protocols can be used to propagate information in a manner rather similar to the way that a viral infection spreads in a biological population. Indeed, the mathematics of epidemics are often used to model the mathematics of gossip communication. The term
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continuously gossip about information associated with the participating nodes. Typically, propagation latency isn't a concern, perhaps because the information in question changes slowly or there is no significant penalty for acting upon slightly stale
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algorithm, instead of talking to random nodes, information is spread by talking only to neighbouring nodes. There are a number of algorithms that use similar ideas. A key requirement when designing such protocols is that the neighbor set trace out an
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To start the search, a user would ask the local agent to begin to gossip about the search string. (We're assuming that agents either start with a known list of peers, or retrieve this information from some kind of a shared
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Suppose that we want to find the object that most closely matches some search pattern, within a network of unknown size, but where the computers are linked to one another and where each machine is running a small
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or with other types of data in consistent states, counting the number of nodes in a network of unknown size, spreading news robustly, organizing nodes according to some structuring policy, building so-called
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Agents also gossip about the best match, to date. Thus, if A gossips with B, after the interaction, A will know of the best matches known to B, and vice versa. Best matches will "spread" through the network.
357:. They report events, but the gossip occurs periodically and events don't actually trigger the gossip. One concern here is the potentially high latency from when the event occurs until it is delivered.
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Active and
Passive Techniques for Group Size Estimation in Large-Scale and Dynamic Distributed Systems. Dionysios Kostoulas, Dimitrios Psaltoulis, Indranil Gupta, Ken Birman, Al Demers. Elsevier
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In this scenario, searches might automatically age out of the network after, say, 10 seconds. By then, the initiator knows the answer and there is no point in further gossip about that search.
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Gupta, Indranil; Birman, Ken; Linga, Prakash; Demers, Al; Van
Renesse, Robbert (2003). "Kelips: Building an Efficient and Stable P2P DHT through Increased Memory and Background Overhead".
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This round-by-round doubling phenomenon makes the protocol very robust, even if some messages get lost, or some of the selected peers are the same or already know about the search string.
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Proximity-aware superpeer overlay topologies. Gian Paolo Jesi, Alberto
Montresor, and Ozalp Babaoglu. IEEE Transactions on Network and Service Management, 4(2):74–83, September 2007.
347:(or rumor-mongering protocols). These use gossip to spread information; they basically work by flooding agents in the network, but in a manner that produces bounded worst-case loads:
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If the messages might get large (for example, if many searches are active all at the same time), a size limit should be introduced. Also, searches should "age out" of the network.
1052:
Van
Renesse, Robbert; Birman, Kenneth P.; Vogels, Werner (May 2003). "Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining".
489:. Each class contains tens or even hundreds of protocols, differing in their details and performance properties but similar at the level of the guarantees offered to users.
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Systematic Design of P2P Technologies for
Distributed Systems. Indranil Gupta, Global Data Management, eds: R. Baldoni, G. Cortese, F. Davide and A. Melpignano, 2006.
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There are probably hundreds of variants of specific gossip-like protocols because each use-scenario is likely to be customized to the organization's specific needs.
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Build One, Get One Free: Leveraging the
Coexistence of Multiple P2P Overlay Networks. Balasubramaneyam Maniymaran, Marin Bertier and Anne-Marie Kermarrec. Proc.
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Peer counting and sampling in overlay networks: random walk methods. Laurent
Massoulié, Erwan Le Merrer, Anne-Marie Kermarrec, Ayalvadi Ganesh. Proc. 25th ACM
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Chord on Demand. Alberto
Montresor, Márk Jelasity, and Ozalp Babaoglu. Proc. 5th Conference on Peer-to-Peer Computing (P2P), Konstanz, Germany, August 2005.
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X-BOT: A Protocol for
Resilient Optimization of Unstructured Overlays. João Leitão, João Marques, José Pereira, Luís Rodrigues. Proc. 28th IEEE
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Gupta, I.; Kermarrec, A.-M.; Ganesh, A.J. (July 2006). "Efficient and adaptive epidemic-style protocols for reliable and scalable multicast".
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Voulgaris, S.; Kermarrec, A.-M.; Massoulie, L.; Van Steen, M. (2004). "Exploiting semantic proximity in peer-to-peer content searching".
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Many protocols that predate the earliest use of the term "gossip" fall within this rather inclusive definition. For example, Internet
530:
Demers, Alan; Greene, Dan; Hauser, Carl; Irish, Wes; Larson, John (1987). "Epidemic algorithms for replicated database maintenance".
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There is some form of randomness in the peer selection. Peers might be selected from the full set of nodes or from a smaller set of
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Gupta, Ruchir; Singh, Yatindra Nath (2015). "Reputation Aggregation in Peer-to-Peer Network Using Differential Gossip Algorithm".
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is sometimes employed when describing a software system in which this kind of gossip-based information propagation is employed.
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have no central registry and the only way to spread common data is to rely on each member to pass it along to their neighbors.
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The frequency of the interactions is low compared to typical message latencies so that the protocol costs are negligible.
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Some gossip protocols replace the random peer selection mechanism with a more deterministic scheme. For example, in the
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Proceedings of the twenty-fourth annual ACM SIGACT-SIGOPS symposium on Principles of distributed computing - PODC '05
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Gossip-Based Computation of Aggregate Information. David Kempe, Alin Dobra, Johannes Gehrke. Proc. 44th Annual IEEE
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Proceedings. 10th IEEE International Workshop on Future Trends of Distributed Computing Systems, 2004. FTDCS 2004
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Allavena, André; Demers, Alan; Hopcroft, John E. (2005). "Correctness of a gossip based membership protocol".
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On receipt of a search string for the first time, each agent checks its local machine for matching documents.
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over a gossip substrate, doing so would be at odds with the spirit, if not the wording, of the definition.
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Ordered slicing of very large overlay networks. Márk Jelasity and Anne-Marie Kermarrec. IEEE P2P, 2006.
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Eugster, P. Th.; Guerraoui, R.; Handurukande, S. B.; Kouznetsov, P.; Kermarrec, A.-M. (November 2003).
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Birman, Kenneth P.; Hayden, Mark; Ozkasap, Oznur; Xiao, Zhen; Budiu, Mihai; Minsky, Yaron (May 1999).
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Proceedings of the sixth annual ACM Symposium on Principles of distributed computing - PODC '87
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use peer-to-peer gossip to ensure that data is disseminated to all members of a group. Some
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37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07)
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is a procedure or process of computer peer-to-peer communication that is based on the way
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Spatial gossip and resource location protocols. David Kempe, Jon Kleinberg, Alan Demers.
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Gossip protocols are just one class among many classes of networking protocols. See also
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586:. In Serugendo, Giovanna Di Marzo; Gleizes, Marie-Pierre; Karageorgos, Anthony (eds.).
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interact, the state of at least one agent changes to reflect the state of the other.
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2007 26th IEEE International Symposium on Reliable Distributed Systems (SRDS 2007)
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Leitao, Joao; Pereira, Jose; Rodrigues, Luis (2007). "Epidemic Broadcast Trees".
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The core of the protocol involves periodic, pairwise, inter-process interactions.
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The references used may be made clearer with a different or consistent style of
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590:. Natural Computing Series. Springer Berlin Heidelberg. pp. 139–162.
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750:. Lecture Notes in Computer Science. Vol. 2735. pp. 160–169.
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The information exchanged during these interactions is of bounded size.
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network search could search a big data center in about three seconds.
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It is useful to distinguish two prevailing styles of gossip protocol:
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Jelasity, Márk; Montresor, Alberto; Babaoglu, Ozalp (August 2005).
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Jelasity, Márk; Montresor, Alberto; Babaoglu, Ozalp (August 2009).
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Gossip protocols have also been used for achieving and maintaining
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Building low-diameter P2P networks. G. Pandurangan, P. Raghavan,
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For example, a gossip protocol might employ some of these ideas:
780:: A Membership Protocol for Reliable Gossip-Based Broadcast".
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59:
18:
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Leitao, Joao; Pereira, Jose; Rodrigues, Luis (2007). "HyPar
854:"T-Man: Gossip-based fast overlay topology construction"
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International Symposium on Reliable Distributed Systems
512:, BitTorrent peer-to-peer client using gossip protocol.
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IEEE Transactions on Parallel and Distributed Systems
925:"Gossip-based aggregation in large dynamic networks"
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IEEE Transactions on Knowledge and Data Engineering
87:but its sources remain unclear because it lacks
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1046:Symposium on Foundations of Computer Science
980:Symposium on Foundations of Computer Science
327:Due to the replication there is an implicit
406:program that implements a gossip protocol.
53:Learn how and when to remove these messages
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241:Learn how and when to remove this message
223:Learn how and when to remove this message
118:Learn how and when to remove this message
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429:search will have found the best match.
361:Background data dissemination protocols
314:Reliable communication is not assumed.
712:"Lightweight probabilistic broadcast"
7:
1167:The Mathematical Theory of Epidemics
1054:ACM Transactions on Computer Systems
932:ACM Transactions on Computer Systems
719:ACM Transactions on Computer Systems
681:ACM Transactions on Computer Systems
14:
1012:"Introduction to expander graphs"
370:Protocols that compute aggregates
147:to comply with Knowledge (XXG)'s
34:This article has multiple issues.
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987:Journal of Systems and Software
42:or discuss these issues on the
1:
1165:Bailey, Norman T. J. (1957).
1044:. In Proceedings of the 42nd
579:Jelasity, Márk (2011-01-01).
393:logarithmic in system size).
351:Event dissemination protocols
331:of the delivered information.
1010:Nielsen, Michael A. (2005).
873:10.1016/j.comnet.2009.03.013
756:10.1007/978-3-540-45172-3_15
596:10.1007/978-3-642-17348-6_7
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1093:10.1109/FTDCS.2004.1316622
1150:10.1109/TKDE.2015.2427793
975:(JACM) 51: 6 (Nov 2004).
588:Self-organising Software
353:use gossip to carry out
160:may contain suggestions.
145:may need to be rewritten
73:This article includes a
944:10.1145/1082469.1082470
748:Peer-to-Peer Systems II
642:10.1145/1073814.1073871
390:convergently consistent
383:2-phase commit protocol
345:Dissemination protocols
102:more precise citations.
1200:Distributed computing
1066:10.1145/762483.762485
731:10.1145/945506.945507
694:10.1145/312203.312207
1195:Network architecture
1087:. pp. 238–243.
890:10.1109/SRDS.2007.27
884:. pp. 301–310.
837:10.1109/TPDS.2006.85
784:. pp. 419–429.
441:distributed database
283:gossip communication
16:Concept in computing
790:10.1109/DSN.2007.56
677:"Bimodal multicast"
540:10.1145/41840.41841
455:Epidemic algorithms
290:Variants and styles
267:distributed systems
973:Journal of the ACM
462:epidemic algorithm
75:list of references
1176:978-0-85264-113-2
1134:(10): 2812–2823.
907:978-0-7695-2995-0
867:(13): 2321–2339.
861:Computer Networks
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765:978-3-540-40724-9
651:978-1-58113-994-5
605:978-3-642-17347-9
549:978-0-89791-239-6
534:. pp. 1–12.
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166:October 2021
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36:Please help
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485:, database
444:consistency
100:introducing
1189:Categories
1169:. Hafner.
1101:1871/12832
968:(SRDS'09).
898:1822/38894
798:1822/38895
660:8876665695
558:8876960204
517:References
355:multicasts
329:redundancy
205:footnoting
108:March 2009
39:improve it
1141:1210.4301
1042:Eli Upfal
703:207744063
614:214970849
388:The term
322:neighbors
263:epidemics
158:talk page
45:talk page
581:"Gossip"
468:See also
397:Examples
201:citation
1119:9464168
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