Gossiping personalized queries

Bai, Xiao; Bertier, Marin; Guerraoui, Rachid; Kermarrec, Anne-Marie; Leroy, Vincent

doi:10.1145/1739041.1739055

Cited by 29 publications

(32 citation statements)

References 19 publications

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“…Each node also stores a profile (e.g. a user's browsing history), and uses a peer-to-peer epidemic protocol [1,4,30,17] to converge towards an optimal neighborhood, i.e. a neighborhood containing the k most-similar other nodes in the system according to some similarity metric on profiles (e.g.…”

Section: Background: Decentralized K-nn Graph Constructionmentioning

confidence: 99%

“…cosine similarity, or Jaccard's coefficient). The principle of a typical P2P protocol for KNN graph construction [9,30] is shown in Algorithm 1, in its push-pull variant 1 . Starting from a random neighborhood, individual nodes repeatedly select a random neighbor q (line 2), exchange their current neighborhood with that of q (noted Γ (q), line 4), and use the gained information to select more similar neighbors (line 5)…”

Section: Background: Decentralized K-nn Graph Constructionmentioning

confidence: 99%

“…Unfortunately, with this modification, one of the key premises of Algorithm 1 disappears: the far neighbors of a far neighbor are not so likely to be interesting 1 The presented model is close to the Vicinity algorithm [30], but variations exist, most notably the T-Man algorithm [17], which buffers and selects nodes differently. 2 argtop k returns a k-tuple of nodes that maximizes the similarity function sim(p, −).…”

Section: Moving To Decentralized K-furthest-neighbor Graph Constructionmentioning

confidence: 99%

See 2 more Smart Citations

Scalable Anti-KNN: Decentralized Computation of k-Furthest-Neighbor Graphs with HyFN

Bouget

Bromberg

Taı̈ani

et al. 2017

Distributed Applications and Interoperable Systems

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International audienceThe decentralized construction of k-Furthest-Neighbor graphs has been little studied, although such structures can play a very useful role, for instance in a number of distributed resource allocation problems. In this paper we define KFN graphs; we propose HyFN, a generic peer-to-peer KFN construction algorithm, and thoroughly evaluate its behavior on a number of logical networks of varying sizes. 1 Motivation k-Nearest-Neighbor (KNN) graphs have found usage in a number of domains, including machine learning, recommenders, and search. Some applications do not however require the k closest nodes, but the k most dissimilar nodes, what we term the k-Furthest-Neighbor (KFN) graph. Virtual Machines (VMs) placement —i.e. the (re-)assignment of workloads in virtualised IT environments— is a good example of where KFN can be applied. The problem consists in finding an assignment of VMs on physical machines (PMs) that minimises some cost function(s) [27]. The problem has been described as one of the most complex and important for the IT industry [3], with large potential savings [20]. An important challenge is that a solution does not only consist in packing VMs onto PMs — it also requires to limit the amount of interferences between VMs hosted on the same PM [31]. Whatever technique is used (e.g. clustering [21]), interference aware VM placement algorithms need to identify complementary workloads — i.e. workloads that are dissimilar enough that the interferences between them are minimised. This is why the application of KFN graphs would make a lot of sense: identifying quickly complementary workloads (using KFN) to help placement algorithms would decrease the risks of interferences. The construction of KNN graphs in decentralized systems has been widely studied in the past [17, 30, 4, 14]. However, existing approaches typically assume a form of " likely transitivity " of similarity between nodes: if A is close to B, and B to C, then A is likely to be close to C. Unfortunately this property no longer holds when constructing KFN graphs. As a result, these approaches, as demonstrated in the remainder of the paper, are not working anymore when applied to this new problem

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Section: Background: Decentralized K-nn Graph Constructionmentioning

confidence: 99%

Section: Background: Decentralized K-nn Graph Constructionmentioning

confidence: 99%

Section: Moving To Decentralized K-furthest-neighbor Graph Constructionmentioning

confidence: 99%

See 1 more Smart Citation

Scalable Anti-KNN: Decentralized Computation of k-Furthest-Neighbor Graphs with HyFN

Bouget

Bromberg

Taı̈ani

et al. 2017

Distributed Applications and Interoperable Systems

View full text Add to dashboard Cite

show abstract

“…Instead of selecting a static metrics at design time, as most decentralised recommenders do [5,3,4], we propose to investigate whether each node can identify an optimal metric dynamically, during the recommendation process. Adapting a node's similarity metric is, however, difficult for at least three reasons.…”

Section: Self-adaptive Implicit Overlaysmentioning

confidence: 99%

Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

Frey

Kermarrec

Maddock

et al. 2015

Distributed Applications and Interoperable Systems

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Abstract. Decentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. In this paper, we propose a framework to develop dynamically adaptive decentralised recommendation systems. Our proposal supports a decentralised form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall system's mission.

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“…Among these, gossip topology construction protocols have received a large amount of attention [1], [2], [10], [11] due to their inherent ability to scale, survive, and adapt. These gossip protocols exploit epidemic interactions to progressively organize nodes along a predefined topology (e.g.…”

Section: Introductionmentioning

confidence: 99%

Polystyrene: the Decentralized Data Shape That Never Dies

Bouget

Kervadec

Kermarrec

et al. 2014

2014 IEEE 34th International Conference on Distributed Computing Systems

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Abstract-Decentralized topology construction protocols organize nodes along a predefined topology (e.g. a torus, ring, or hypercube). Such topologies have been used in many contexts ranging from routing and storage systems, to publish-subscribe and event dissemination. Since most topologies assume no correlation between the physical location of nodes and their positions in the topology, they do not handle catastrophic failures well, in which a whole region of the topology disappears. When this occurs, the overall shape of the system typically gets lost. This is highly problematic in applications in which overlay nodes are used to map a virtual data space, be it for routing, indexing or storage. In this paper, we propose a novel decentralized approach that maintains the initial shape of the topology even if a large (consecutive) portion of the topology fails. Our approach relies on the dynamic decoupling between physical nodes and virtual ones enabling a fast reshaping. For instance, our results show that a 51,200-node torus converges back to a full torus in only 10 rounds after 50% of the nodes have crashed. Our protocol is both simple and flexible and provides a novel form of collective survivability that goes beyond the current state of the art.

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Gossiping personalized queries

Cited by 29 publications

References 19 publications

Scalable Anti-KNN: Decentralized Computation of k-Furthest-Neighbor Graphs with HyFN

Scalable Anti-KNN: Decentralized Computation of k-Furthest-Neighbor Graphs with HyFN

Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

Polystyrene: the Decentralized Data Shape That Never Dies

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