DataFlasks : an epidemic dependable key-value substrate

Recently, tuple-stores have become pivotal struc- tures in many information systems. Their ability to handle large datasets makes them important in an era with unprecedented amounts of data being produced and exchanged. However, these tuple-stores typically rely on structured peer-to-peer protocols which assume moderately stable environments. Such assumption does not always hold for very large scale systems sized in the scale of thousands of machines. In this paper we present a novel approach to the design of a tuple-store. Our approach follows a stratified design based on an unstructured substrate. We focus on this substrate and how the use of epidemic protocols allow reaching high dependability and scalability.(undefined

A Square Root Topologys to Find Unstructured Peer-To-Peer Networks

Unstructured peer-to-peer file sharing networks are very popular in the market Which they introduce Large network traffic The resultant networks may not perform search efficiently and effectively because used overlay topology formation algorithms are creating unstructured P2P networks are not performs guarantees In this paper we choosen the square-root topology and show that this topology Which improves routing performance compared to power-law networks In the square-root topology shows that this topology is optimal for random walk searches A power-law topology for other types of search techniques besides random walks Then we interoduced a decentralized algorithm for forming a square-root topology its effectiveness in constructing efficient networks using both simulations and experiments with our prototype Results show that the square-root topology can provide a good and the best performance and improvement over power-law topologies and other topology type

Gossip Learning with Linear Models on Fully Distributed Data

Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications. In this model we have one data record at each network node, but without the possibility to move raw data due to privacy considerations. For example, user profiles, ratings, history, or sensor readings can represent this case. This problem is difficult, because there is no possibility to learn local models, the system model offers almost no guarantees for reliability, yet the communication cost needs to be kept low. Here we propose gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods. We present an instantiation of this approach for the case of classification with linear models. Our main contribution is an ensemble learning method which---through the continuous combination of the models in the network---implements a virtual weighted voting mechanism over an exponential number of models at practically no extra cost as compared to independent random walks. We prove the convergence of the method theoretically, and perform extensive experiments on benchmark datasets. Our experimental analysis demonstrates the performance and robustness of the proposed approach.Comment: The paper was published in the journal Concurrency and Computation: Practice and Experience http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291532-0634 (DOI: http://dx.doi.org/10.1002/cpe.2858). The modifications are based on the suggestions from the reviewer

NAT-resilient Gossip Peer Sampling

International audienceCet article explique comment réaliser un protocole de diffusion par gossip fonctionnant en présence de NATs