Optimally Efficient Prefix Search and Multicast in Structured P2P Networks

Searching in P2P networks is fundamental to all overlay networks. P2P networks based on Distributed Hash Tables (DHT) are optimized for single key lookups, whereas unstructured networks offer more complex queries at the cost of increased traffic and uncertain success rates. Our Distributed Tree Construction (DTC) approach enables structured P2P networks to perform prefix search, range queries, and multicast in an optimal way. It achieves this by creating a spanning tree over the peers in the search area, using only information available locally on each peer. Because DTC creates a spanning tree, it can query all the peers in the search area with a minimal number of messages. Furthermore, we show that the tree depth has the same upper bound as a regular DHT lookup which in turn guarantees fast and responsive runtime behavior. By placing objects with a region quadtree, we can perform a prefix search or a range query in a freely selectable area of the DHT. Our DTC algorithm is DHT-agnostic and works with most existing DHTs. We evaluate the performance of DTC over several DHTs by comparing the performance to existing application-level multicast solutions, we show that DTC sends 30-250% fewer messages than common solutions

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Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network

Content-based addressing in hierarchical distributed hash tables

Peer-to-peer networks have drawn their strength from their ability to operate functionally without the use of a central agent. In recent years the development of the structured peer-to-peer network has further increased the distributed nature of p2p systems. These networks take advantage of an underlying distributed data structure, a common one is the distributed hash table (DHT). These peers use this structure to act as equals in a network, sharing the same responsibilities of maintaining and contributing. But herein lays the problem, not all peers are equal in terms of resources and power. And with no central agent to monitor and balance load , the heterogeneous nature of peers can cause many distribution or bottleneck issues on the network and peer levels. This is due to the way in which addresses are allocated in these DHTs. Often this function is carried out by a consistent hashing function. These functions although powerful in their simplicity and effectiveness are the stem of a crucial flaw. This flaw causes the random nature in which addresses are assigned both when considering peer identification and allocating resource ownership. This work proposes a solution to mitigate the random nature of address assignment in DHTs, leveraging two methodologies called hierarchical DHTs and content based addressing. Combining these methods would enable peers to work in cooperative groups of like interested peers in order to dynamically share the load between group members. Group formation and utilization relies on the actual resources a peer willingly shares and is able to contribute rather than a function of the random hash employed by traditional DHT p2p structures