Epidemic algorithms for replicated database maintenance

Demers, Alan; Greene, Dan; Hauser, Carl; Irish, Wes; Larson, John L.; Shenker, Scott; Sturgis, Howard; Swinehart, Dan; Terry, Douglas B.

doi:10.1145/41840.41841

Cited by 1,172 publications

(428 citation statements)

References 13 publications

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“…Our basic model is a slightly modified version of the Daley and Kendall (DK) model [12][13][14] and it can be considered as the simplest epidemic algorithm for the updating of distributed databases [4,15]. We study the main relevant features of the model, such as the reliability of the dissemination process and the amount of traffic generated by the dynamics.…”

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confidence: 99%

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Efficiency and reliability of epidemic data dissemination in complex networks

Nekovee²,

2004

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We study the dynamics of epidemic spreading processes aimed at spontaneous dissemination of information updates in populations with complex connectivity patterns. The influence of the topological structure of the network in these processes is studied by analyzing the behavior of several global parameters, such as reliability, efficiency, and load. Large-scale numerical simulations of update-spreading processes show that while networks with homogeneous connectivity patterns permit a higher reliability, scale-free topologies allow for a better efficiency. Modern society increasingly relies on large-scale computer and communication networks, such as the Internet. A major challenge in these networks is the development of reliable algorithms for the dissemination of information from a given source to thousands, or even millions, of users, such as for news and stock exchange updates, mass file transfers, and Internet broadcasts [1,2]. In epidemic-inspired communication, this is achieved by exploring a mechanism analogous to the spreading of infectious diseases in populations [3,4]. The information spreads like a benign epidemic through local interaction between nodes which forward the message they receive to a random selection of their peers in the network, until the whole system becomes "infected" with information. The great advantages of epidemic-style communication is that dissemination proceeds on a local basis, without any coordination from a central organizing body [3,4]. These protocols are also highly resilient to sudden failure of communication links and nodes.A relevant result in the mathematical theory of epidemics is that the spreading of infection in a population is strongly affected by the patterns of connectivity in the underlying contact networks. In particular, in scale-free topologies, characterized by degree distributions with power-law behavior, the statistical relevance of hubs makes the network highly permeable to attacks [6][7][8] and the spreading of infections [9] and highlights the need for special immunization strategies. This result suggests that the topology of the underlying computer and communication network might heavily affect the performance of epidemic-style data dissemination protocols. Surprisingly, however, the impact of network topology on such protocols has not been thoroughly explored, although the results could have an important technological value. Indeed, these protocols can potentially find a large spectrum of application, such as mobile communication networks and, more recently, resource discovery in the socalled peer-to-peer systems built on top of the Internet [10], and finally in grid computing [11].In this paper, we define a simple epidemic data dissemination model and perform a detailed numerical study of the dynamics of the information propagation in networks with diverse topological properties. Our basic model is a slightly modified version of the Daley and Kendall (DK) model [12][13][14] and it can be considered as the simplest epidemic algorithm for the updating o...

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“…In general, one does not only want to have high reliability levels, but also the lowest cost in terms of network load [4,15]. This is generally achieved by imposing the minimum possible load to the network.…”

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Efficiency and reliability of epidemic data dissemination in complex networks

Nekovee²,

2004

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“…The position of each node on the ring is advertised in the cluster using a family of peer-to-peer (P2P) protocols: Gossip [34]. Each node periodically disseminates its status information to a number of randomlyselected nodes and relays status information received from other nodes.…”

Section: Background: the Systems We Targetmentioning

confidence: 99%

Keeping up with storage: Decentralized, write-enabled dynamic geo-replication

Matri

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Costan

et al. 2018

Future Generation Computer Systems

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Large-scale applications are ever-increasingly geo-distributed. Maintaining the highest possible data locality is crucial to ensure high performance of such applications. Dynamic replication addresses this problem by dynamically creating replicas of frequently accessed data close to the clients. This data is often stored in decentralized storage systems such as Dynamo or Voldemort, which offer support for mutable data. However, existing approaches to dynamic replication for such mutable data remain centralized, thus incompatible with these systems. In this paper we introduce a writeenabled dynamic replication scheme that leverages the decentralized architecture of such storage systems. We propose an algorithm enabling clients to locate tentatively the closest data replica without prior request to any metadata node. Large-scale experiments on various workloads show a read latency decrease of up to 42% compared to other state-ofthe-art, caching-based solutions.

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“…Inspired by the SIS and SIR models, they designed epidemic algorithms as a method to disseminate updates throughout a distributed, replicated database [87]. Yet, it was not until 2000, once mobile, ad-hoc networks (MANETs) had began to draw the attention of the research community, that Demers et al's epidemic algorithms were applied to computer network research.…”

Section: Epidemic Routingmentioning

confidence: 99%

A taxonomy of biologically inspired research in computer networking

2010

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a b s t r a c tThe natural world is enormous, dynamic, incredibly diverse, and highly complex. Despite the inherent challenges of surviving in such a world, biological organisms evolve, self-organize, self-repair, navigate, and flourish. Generally, they do so with only local knowledge and without any centralized control. Our computer networks are increasingly facing similar challenges as they grow larger in size, but are yet to be able to achieve the same level of robustness and adaptability. Many research efforts have recognized these parallels, and wondered if there are some lessons to be learned from biological systems. As a result, biologically inspired research in computer networking is a quickly growing field. This article begins by exploring why biology and computer network research are such a natural match. We then present a broad overview of biologically inspired research, grouped by topic, and classified in two ways: by the biological field that inspired each topic, and by the area of networking in which the topic lies. In each case, we elucidate how biological concepts have been most successfully applied. In aggregate, we conclude that research efforts are most successful when they separate biological design from biological implementation -that is to say, when they extract the pertinent principles from the former without imposing the limitations of the latter.

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Epidemic algorithms for replicated database maintenance

Cited by 1,172 publications

References 13 publications

Efficiency and reliability of epidemic data dissemination in complex networks

Efficiency and reliability of epidemic data dissemination in complex networks

Keeping up with storage: Decentralized, write-enabled dynamic geo-replication

A taxonomy of biologically inspired research in computer networking

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