Abstract — Over the Internet today, computing and communications environments are significantly more complex and chaotic than classical distributed systems, lacking any centralized organization or hierarchical control. There has been much interest in emerging Peer-to-Peer (P2P) network overlays because they provide a good substrate for creating large-scale data sharing, content distribution and application-level multicast applications. These P2P networks try to provide a long list of features such as: selection of nearby peers, redundant storage, efficient search/location of data items, data permanence or guarantees, hierarchical naming, trust and authentication, and, anonymity. P2P networks potentially offer an efficient routing architecture that is self-organizing, massively scalable, and robust in the wide-area, combining fault tolerance, load balancing and explicit notion of locality. In this paper, we present a survey and comparison of various Structured and Unstructured P2P networks. We categorize the various schemes into these two groups in the design spectrum and discuss the application-level network performance of each group.

Structured peer-to-peer (p2p) overlay networks provide a useful substrate for building distributed applications. They assign object keys to overlay nodes and provide a primitive to route a message to the node responsible for a key. Proximity neighbor selection (PNS) can be used to achieve both low delay routes and low bandwidth usage but it introduces high overhead. This paper presents a detailed evaluation of PNS and heuristic approximations. We describe a new heuristic called constrained gossiping (PNS-CG) and show that it achieves performance similar to perfect PNS with low overhead. We also compare constrained gossiping with previous heuristics and show that it achieves better performance with lower overhead.

Hierarchical metric-space clustering methods have been commonly used to organize proteomes into taxonomies. Consequently, it is often anticipated that hierarchical clustering can be leveraged as a basis for scalable database index structures capable of managing the hyper-exponential growth of sequence data. M-tree is one such data structure specialized for the management of large data sets on disk. We explore the application of M-trees to the storage and retrieval of peptide sequence data. Exploiting a technique first suggested by Myers, we organize the database as records of fixed length substrings. Empirical results are promising. However, metric-space indexes are subject to “the curse of dimensionality ” and the ultimate performance of an index is sensitive to the quality of the initial construction of the index. We introduce new hierarchical bulk-load algorithm that alternates between top-down and bottom-up clustering to initialize the index. Using the Yeast Proteomes, the bi-directional bulk load produces a more effective index than the existing M-tree initialization algorithms. 1.

Abstract. We present a review of the literature on fast nearest neighbors using the basic approach from Karger and Ruhl [4] and a recent technique called cover trees. A small error in Insert procedure from the original paper on cover trees is corrected and an examination of how query time actually varies with the size of the problem is shown using a Python implementation of the basic cover tree algorithms. 1.

This paper proposes the first peer-to-peer network and lookup algorithm that has worst case constant distortion. The construction uses a constant expected number of links. The design lends itself to dynamic deployment, and has a simple and easy to verify proof. The construction embraces the two-tier architecture of current peer-to-peer networks, where stronger and stable nodes serve as ultra-peers, and other (e.g., transient home users) are regular peers.