284 research outputs found
Towards Scalable Web Documents
The current Web is running into serious scalability problems. The standard solution is to apply techniques like caching, replication, and distribution. Unfortunately, as the variety of Web applications continues to grow, it will be impossible to find a single solution that fits all needs. The authors advocate a different approach to tackling scaling problems. Instead of seeking a general-purpose solution, they argue that it makes more sense to look at each Web document separately. For each document, three issues need to be addressed: placement of replicas, required coherence, and best coherence protocol. The authors examine each of these issues, and identify the alternatives. However, forcing developers to decide on the best alternatives will turn the Web into an unworkable system. Therefore, a number of possible ways to reduce complexity is indicated. Also, the authors briefly discuss a wide-area infrastructure that can be used as a flexible basis for developing per-document solutions
Exploiting Semantic Proximity in Peer-to-Peer Content Searching
A lot of recent work has dealt with improving performance of content searching in peer-to-peer file sharing systems. In this paper we attack this problem by modifying the overlay topology describing the peer relations in the system. More precisely, we create a semantic overlay, linking nodes that are "semantically close", by which we mean that they are interested in similar documents. This semantic overlay provides the primary search mechanism, while the initial peer-to-peer system provides the fail-over search mechanism. We focus on implicit approaches for discovering semantic proximity. We evaluate and compare three candidate methods, and review open questions
Yes, Topology Matters in Decentralized Optimization: Refined Convergence and Topology Learning under Heterogeneous Data
One of the key challenges in federated and decentralized learning is to
design algorithms that efficiently deal with highly heterogeneous data
distributions across agents. In this paper, we revisit the analysis of
Decentralized Stochastic Gradient Descent algorithm (D-SGD), a popular
decentralized learning algorithm, under data heterogeneity. We exhibit the key
role played by a new quantity, that we call neighborhood heterogeneity, on the
convergence rate of D-SGD. Unlike prior work, neighborhood heterogeneity is
measured at the level of the neighborhood of an agent in the graph topology. By
coupling the topology and the heterogeneity of the agents' distributions, our
analysis sheds light on the poorly understood interplay between these two
concepts in decentralized learning. We then argue that neighborhood
heterogeneity provides a natural criterion to learn sparse data-dependent
topologies that reduce (and can even eliminate) the otherwise detrimental
effect of data heterogeneity on the convergence time of D-SGD. For the
important case of classification with label skew, we formulate the problem of
learning such a good topology as a tractable optimization problem that we solve
with a Frank-Wolfe algorithm. Our approach provides a principled way to design
a sparse topology that balances the number of iterations and the per-iteration
communication costs of D-SGD under data heterogeneity
Adaptive gossip-based broadcast
This paper presents a novel adaptation mechanism that allows every node of a gossip-based broadcast algorithm to adjust the rate of message emission 1) to the amount of resources available to the nodes within the same broadcast group and 2) to the global level of congestion in the system. The adaptation mechanism can be applied to all gossip-based broadcast algorithms we know of and makes their use more realistic in practical situations where nodes have limited resources whose quantity changes dynamically with time without decreasing the reliability.(undefined
Distributed Slicing in Dynamic Systems
Peer to peer (P2P) systems are moving from application specific architectures
to a generic service oriented design philosophy. This raises interesting
problems in connection with providing useful P2P middleware services capable of
dealing with resource assignment and management in a large-scale, heterogeneous
and unreliable environment. The slicing service, has been proposed to allow for
an automatic partitioning of P2P networks into groups (slices) that represent a
controllable amount of some resource and that are also relatively homogeneous
with respect to that resource. In this paper we propose two gossip-based
algorithms to solve the distributed slicing problem. The first algorithm speeds
up an existing algorithm sorting a set of uniform random numbers. The second
algorithm statistically approximates the rank of nodes in the ordering. The
scalability, efficiency and resilience to dynamics of both algorithms rely on
their gossip-based models. These algorithms are proved viable theoretically and
experimentally
Dynamics of Rumor Spreading in Complex Networks
We derive the mean-field equations characterizing the dynamics of a rumor
process that takes place on top of complex heterogeneous networks. These
equations are solved numerically by means of a stochastic approach. First, we
present analytical and Monte Carlo calculations for homogeneous networks and
compare the results with those obtained by the numerical method. Then, we study
the spreading process in detail for random scale-free networks. The time
profiles for several quantities are numerically computed, which allow us to
distinguish among different variants of rumor spreading algorithms. Our
conclusions are directed to possible applications in replicated database
maintenance, peer to peer communication networks and social spreading
phenomena.Comment: Final version to appear in PR
NEEM: network-friendly epidemic multicast
Epidemic, or probabilistic, multicast protocols have emerged as a viable mechanism to circumvent the scalabil- ity problems of reliable multicast protocols. However, most existing epidemic approaches use connectionless transport protocols to exchange messages and rely on the intrinsic robustness of the epidemic dissemination to mask network omissions. Unfortunately, such an approach is not network- friendly, since the epidemic protocol makes no effort to re- duce the load imposed on the network when the system is congested. In this paper, we propose a novel epidemic protocol whose main characteristic is to be network-friendly. This property is achieved by relying on connection-oriented transport connections, such as TCP/IP, to support the com- munication among peers. Since during congestion mes- sages accumulate in the border of the network, the pro- tocol uses an innovative buffer management scheme, that combines different selection techniques to discard messages upon overflow. This technique improves the quality of the information delivered to the application during periods of network congestion. The protocol has been implemented and the benefits of the approach are illustrated using a com- bination of experimental and simulation results
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