This paper presents the design of Mercury, a scalable protocol for supporting multi-attribute rangebased searches. Mercury differs from previous range-based query systems in that it supports multiple attributes as well as performs explicit load balancing. Efficient routing and load balancing are implemented using novel light-weight sampling mechanisms for uniformly sampling random nodes in a highly dynamic overlay network. Our evaluation shows that Mercury is able to achieve its goals of logarithmic-hop routing and near-uniform load balancing. We also show that a publish-subscribe system based on the Mercury protocol can be used to construct a distributed object repository providing efficient and scalable object lookups and updates. By providing applications a range-based query language to express their subscriptions to object updates, Mercury considerably simplifies distributed state management. Our experience with the design and implementation of a simple distributed multiplayer game built on top of this object management framework shows that indicates that this indeed is a useful building block for distributed applications. Keywords: Range queries, Peer-to-peer systems, Distributed applications, Multiplayer games 1

Overlay networks are used today in a variety of distributed systems ranging from file-sharing and storage systems to communication infrastructures. However, designing, building and adapting these overlays to the intended application and the target environment is a di#cult and time consuming process.

This paper examines graph-theoretic properties of existing peer-to-peer architectures and proposes a new infrastructure based on optimal-diameter de Bruijn graphs. Since generalized de Bruijn graphs possess very short average routing distances and high resilience to node failure, they are well suited for structured peer-to-peer networks. Using the example of Chord, CAN, and de Bruijn, we first study routing performance, graph expansion, and clustering properties of each graph. We then examine bisection width, path overlap, and several other properties that affect routing and resilience of peer-to-peer networks. Having confirmed that de Bruijn graphs offer the best diameter and highest connectivity among the existing peer-to-peer structures, we offer a very simple incremental building process that preserves optimal properties of de Bruijn graphs under uniform user joins/departures. We call the combined peer-to-peer architecture

Several peer-to-peer networks are based upon randomized graph topologies that permit e#cient greedy routing, e.g., randomized hypercubes, randomized Chord, skip-graphs and constructions based upon small-world percolation networks. In each of these networks, a node has out-degree #(log n), where n denotes the total number of nodes, and greedy routing is known to take O(log n) hops on average. We establish lower-bounds for greedy routing for these networks, and analyze Neighbor-of-Neighbor (NoN)-greedy routing. The idea behind NoN, as the name suggests, is to take a neighbor's neighbors into account for making better routing decisions.

We present SETS, an architecture for building topic-segmented networks for efficient search. The key idea is to arrange participants in a topic-segmented topology where most of the links are short-distance links joining pairs of sites with similar content. The resulting topically focused regions are joined together into a single network by long-distance links. Queries are then matched and routed to only the topically closest regions. We draw on ideas from machine learning and social network theory to build an efficient search network. We discuss a variety of design issues and tradeoffs that an implementor of SETS would face. We show that SETS is ecient in network traffic and query processing load.

As Peer-to-Peer (P2P) networks become popular, there is an emerging need to collect a variety of statistical summary information about the participating nodes. The P2P networks of today lack mechanisms to compute even such basic aggregates as MIN, MAX, SUM, COUNT or AVG. In this paper, we define and study the NODEAGGREGATION problem that is concerned with aggregating data stored at nodes in the network. We present generic schemes that can be used to compute any of the basic aggregation functions accurately and robustly. Our schemes can be used as building blocks for tools to collect statistics on network topology, user behavior and other node characteristics. This is a STUDENT paper intended as a REGULAR presentation. I.

We consider four problems on distance estimation and object location which share the common flavor of capturing global information via informative node labels: low-stretch routing schemes [47], distance labeling [24], searchable small worlds [30], and triangulation-based distance estimation [33]. Focusing on metrics of low doubling dimension, we approach these problems with a common technique called rings of neighbors, which refers to a sparse distributed data structure that underlies all our constructions. Apart from improving the previously known bounds for these problems, our contributions include extending Kleinberg’s small world model to doubling metrics, and a short proof of the main result in Chan et al. [14]. Doubling dimension is a notion of dimensionality for general metrics that has recently become a useful algorithmic concept in the theoretical computer science literature. 1

The study of complex networks has emerged over the past several years as a theme spanning many disciplines, ranging from mathematics and computer science to the social and biological sciences. A significant amount of recent work in this area has focused on the development of random graph models that capture some of the qualitative properties observed in large-scale network data; such models have the potential to help us reason, at a general level, about the ways in which real-world networks are organized. We survey one particular line of network research, concerned with small-world phenomena and decentralized search algorithms, that illustrates this style of analysis. We begin by describing a well-known experiment that provided the first empirical basis for the "six degrees of separation" phenomenon in social networks; we then discuss some probabilistic network models motivated by this work, illustrating how these models lead to novel algorithmic and graph-theoretic questions, and how they are supported by recent empirical studies of large social networks.

A pervasive requirement of distributed systems is to deal with churn -- change in the set of participating nodes due to joins, graceful leaves, and failures. A high churn rate can increase costs or decrease service quality. This paper studies how to reduce churn by selecting which subset of a set of available nodes to use. First,

Today an application developer using a distributed hash table (DHT) with n nodes must choose a DHT protocol from the spectrum between O(1) lookup protocols [9, 18] and O(log n) protocols [20–23,25,26]. O(1) protocols achieve low latency lookups on small or low-churn networks because lookups take only a few hops, but incur high maintenance traffic on large or high-churn networks. O(log n) protocols incur less maintenance traffic on large or highchurn networks but require more lookup hops in small networks. Accordion is a new routing protocol that does not force the developer to make this choice: Accordion adjusts itself to provide the best performance across a range of network sizes and churn rates while staying within a bounded bandwidth budget. The key challenges in the design of Accordion are the algorithms that choose the routing table’s size and content. Each Accordion node learns of new neighbors opportunistically, in a way that causes the density of its neighbors to be inversely proportional to their distance in ID space from the node. This distribution allows Accordion to vary the table size along a continuum while still guaranteeing at most O(log n) lookup hops. The user-specified bandwidth budget controls the rate at which a node learns about new neighbors. Each node limits its routing table size by evicting neighbors that it judges likely to have failed. High churn (i.e., short node lifetimes) leads to a high eviction rate. The equilibrium between the learning and eviction processes determines the table size. Simulations show that Accordion maintains an efficient lookup latency versus bandwidth tradeoff over a wider range of operating conditions than existing DHTs.