Gossip-based protocols provide a simple, scalable, and robust way to disseminate messages in large-scale systems. In such protocols, messages are spread in an epidemic manner. Gossiping may take place between nodes using push, pull, or a combination. Push-based systems achieve reasonable latency and high resilience to failures but may impose an unnecessarily large redundancy and overhead on the system. At the other extreme, pull-based protocols impose a lower overhead on the network at the price of increased latencies. A few hybrid approaches have been proposedtypically pushing control messages and pulling datato avoid the redundancy of high-volume content and single-source streams. Yet, to the best of our knowledge, no other system intermingles push and pull in a multiple-senders scenario, in such a way that data messages of one help in carrying control messages of the other and in adaptively adjusting its rate of operation, further reducing overall cost and improving both on delays and robustness. In this paper, we propose an efficient generic push-pull dissemination protocol, Pulp, which combines the best of both worlds. Pulp exploits the efficiency of push approaches, while limiting redundant messages and therefore imposing a low overhead, as pull protocols do. Pulp leverages the dissemination of multiple messages from diverse sources: by exploiting the push phase of messages to transmit information about other disseminations, Pulp enables an efficient pulling of other messages, which themselves help in turn with the dissemination of pending messages. We deployed Pulp on a cluster and on PlanetLab. Our results demonstrate that Pulp achieves an appealing trade-off between coverage, message redundancy, and propagation delay.
Abstract. Popular search engines essentially rely on information about the structure of the graph of linked elements to find the most relevant results for a given query. While this approach is satisfactory for popular interest domains or when the user expectations follow the main trend, it is very sensitive to the case of ambiguous queries, where queries can have answers over several different domains. Elements pertaining to an implicitly targeted interest domain with low popularity are usually ranked lower than expected by the user. This is a consequence of the poor usage of user-centric information in search engines. Leveraging semantic information can help avoid such situations by proposing complementary results that are carefully tailored to match user interests. This paper proposes a collaborative search companion system, CoFeed, that collects user search queries and accesses feedback to build user-and document-centric profiling information. Over time, the system constructs ranked collections of elements that maintain the required information diversity and enhance the user search experience by presenting additional results tailored to the user interest space. This collaborative search companion requires a supporting architecture adapted to large user populations generating high request loads. To that end, it integrates mechanisms for ensuring scalability and load balancing of the service under varying loads and user interest distributions. Experiments with a deployed prototype highlight the efficiency of the system by analyzing improvement in search relevance, computational cost, scalability and load balance.
SUMMARY Search engines essentially rely on the structure of the graph of hyperlinks. Although accurate for the main trend, this is not effective when some query is ambiguous. Leveraging semantic information by the mean of interest matching allows proposing complementary results that are tailored to the user's expectations. This paper proposes a collaborative search companion system, CoFeed, that collects user search queries and that considers feedback to build user‐centric and document‐centric profiling information. Over time, the system constructs ranked collections of elements that maintain the required information diversity and enhance the user search experience by presenting additional results tailored to the user's interest space. This collaborative search companion requires a supporting architecture adapted to large user populations generating high request loads. To that end, it integrates mechanisms for ensuring scalability and load balancing of the service under varying loads and user interest distributions. Moreover, collecting the recommendation data poses the problem of users’ privacy, and the bias one peer can induce to the system by sending fake recommendations. To that end, CoFeed ensures both publisher anonymity and rate limitation. With the former, the origin of the data is never known by the server that processes it, even if several servers collude to spy on some user. The latter, combined with decoupled authentication, allows to minimize the influence of cheating peers sending fake recommendations. Experiments with a deployed prototype highlight the efficiency of the system by analyzing improvement in search relevance, computational cost, scalability and load balancing. Copyright © 2011 John Wiley & Sons, Ltd.
SPLAY is an integrated system that facilitates the complete chain of distributed systems evaluation, from design and implementation to deployment and experiments control. Algorithms are expressed in a concise, yet very efficient, language based on Lua. Implementations in SPLAY are highly similar to the pseudo-code usually found in research papers. SPLAY eases the use of any kind of testbeds, e.g., PlanetLab, ModelNet clusters, or non-dedicated platforms such as networks of workstations. Using SPLAY and PlanetLab, this demonstration highlights a complete evaluation chain of an epidemic protocol and a churn-driven experiment using the Pastry DHT.
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