9,381 research outputs found
Handling Network Partitions and Mergers in Structured Overlay Networks
Structured overlay networks form a major class of peer-to-peer systems, which are touted for their abilities to
scale, tolerate failures, and self-manage. Any long-lived
Internet-scale distributed system is destined to face network partitions. Although the problem of network partitions
and mergers is highly related to fault-tolerance and
self-management in large-scale systems, it has hardly been
studied in the context of structured peer-to-peer systems.
These systems have mainly been studied under churn (frequent
joins/failures), which as a side effect solves the problem
of network partitions, as it is similar to massive node
failures. Yet, the crucial aspect of network mergers has been
ignored. In fact, it has been claimed that ring-based structured
overlay networks, which constitute the majority of the
structured overlays, are intrinsically ill-suited for merging
rings. In this paper, we present an algorithm for merging
multiple similar ring-based overlays when the underlying
network merges. We examine the solution in dynamic conditions,
showing how our solution is resilient to churn during
the merger, something widely believed to be difficult or
impossible. We evaluate the algorithm for various scenarios
and show that even when falsely detecting a merger, the
algorithm quickly terminates and does not clutter the network
with many messages. The algorithm is flexible as the
tradeoff between message complexity and time complexity
can be adjusted by a parameter
The essence of P2P: A reference architecture for overlay networks
The success of the P2P idea has created a huge diversity
of approaches, among which overlay networks, for example,
Gnutella, Kazaa, Chord, Pastry, Tapestry, P-Grid, or DKS,
have received specific attention from both developers and
researchers. A wide variety of algorithms, data structures,
and architectures have been proposed. The terminologies
and abstractions used, however, have become quite inconsistent since the P2P paradigm has attracted people from many different communities, e.g., networking, databases, distributed systems, graph theory, complexity theory, biology, etc. In this paper we propose a reference model for overlay networks which is capable of modeling different approaches in this domain in a generic manner. It is intended to allow researchers and users to assess the properties of concrete systems, to establish a common vocabulary for scientific discussion, to facilitate the qualitative comparison of the systems, and to serve as the basis for defining a standardized API to make overlay networks interoperable
CATS: linearizability and partition tolerance in scalable and self-organizing key-value stores
Distributed key-value stores provide scalable, fault-tolerant, and self-organizing
storage services, but fall short of guaranteeing linearizable consistency
in partially synchronous, lossy, partitionable, and dynamic networks, when data
is distributed and replicated automatically by the principle of consistent hashing.
This paper introduces consistent quorums as a solution for achieving atomic
consistency. We present the design and implementation of CATS, a distributed
key-value store which uses consistent quorums to guarantee linearizability and partition tolerance in such adverse and dynamic network conditions. CATS is
scalable, elastic, and self-organizing; key properties for modern cloud storage
middleware. Our system shows that consistency can be achieved with practical
performance and modest throughput overhead (5%) for read-intensive workloads
An analytical framework for the performance evaluation of proximity-aware structured overlays
In this paper, we present an analytical study of proximity-aware structured peer-to-peer networks under churn. We use a master-equation-based approach, which is used traditionally in non-equilibrium statistical mechanics to describe steady-state or transient phenomena. In earlier work we have demonstrated that this methodology is in fact also well suited to describing structured overlay networks under churn, by showing how we can accurately predict the average number of hops taken by a lookup, for any value of churn, for the Chord system. In this paper, we extend the analysis so as to also be able to predict lookup latency, given an average latency for the links in the network. Our results show that there exists a region in the parameter space of the model, depending on churn, the number of nodes, the maintenance rates and the delays in the network, when the network cannot function as a small world graph anymore, due to the farthest connections of a node always being wrong or dead. We also demonstrate how it is possible to analyse proximity neighbour selection or proximity route selection within this formalism
Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
Many decentralized online social networks (DOSNs) have been proposed due to
an increase in awareness related to privacy and scalability issues in
centralized social networks. Such decentralized networks transfer processing
and storage functionalities from the service providers towards the end users.
DOSNs require individualistic implementation for services, (i.e., search,
information dissemination, storage, and publish/subscribe). However, many of
these services mostly perform social queries, where OSN users are interested in
accessing information of their friends. In our work, we design a socially-aware
distributed hash table (DHTs) for efficient implementation of DOSNs. In
particular, we propose a gossip-based algorithm to place users in a DHT, while
maximizing the social awareness among them. Through a set of experiments, we
show that our approach reduces the lookup latency by almost 30% and improves
the reliability of the communication by nearly 10% via trusted contacts.Comment: 10 pages, p2p 2015 conferenc
Statistical structures for internet-scale data management
Efficient query processing in traditional database management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributed systems, on the other hand, statistics management still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge. To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average-Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation of these tools for distributed statistical synopses, and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability
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