Approximate Matching for Peer-to-Peer Overlays with Cubit

Keyword search is a critical component in most content retrieval systems. Despite the emergence of completely decentralized and efficient peer-to-peer techniques for content distribution, there have not been similarly efficient, accurate, and decentralized mechanisms for content discovery based on approximate search keys. In this paper, we present a scalable and efficient peer-to-peer system called Cubit with a new search primitive that can efficiently find the k data items with keys most similar to a given search key. The system works by creating a keyword metric space that encompasses both the nodes and the objects in the system, where the distance between two points is a measure of the similarity between the strings that the points represent. It provides a loosely-structured overlay that can efficiently navigate this space. We evaluate Cubit through both a real deployment as a search plugin for a popular BitTorrent client and a large-scale simulation and show that it provides an efficient, accurate and robust method to handle imprecise string search in filesharing applications.This work was supported in part by NSF-TRUST 0424422 and NSF-CAREER 0546568 grants

An Overlay Architecture for Personalized Object Access and Sharing in a Peer-to-Peer Environment

Due to its exponential growth and decentralized nature, the Internet has evolved into a chaotic repository, making it difficult for users to discover and access resources of interest to them. As a result, users have to deal with the problem of information overload. The Semantic Web's emergence provides Internet users with the ability to associate explicit, self-described semantics with resources. This ability will facilitate in turn the development of ontology-based resource discovery tools to help users retrieve information in an efficient manner. However, it is widely believed that the Semantic Web of the future will be a complex web of smaller ontologies, mostly created by various groups of web users who share a similar interest, referred to as a Community of Interest. This thesis proposes a solution to the information overload problem using a user driven framework, referred to as a Personalized Web, that allows individual users to organize themselves into Communities of Interests based on ontologies agreed upon by all community members. Within this framework, users can define and augment their personalized views of the Internet by associating specific properties and attributes to resources and defining constraint-functions and rules that govern the interpretation of the semantics associated with the resources. Such views can then be used to capture the user's interests and integrate these views into a user-defined Personalized Web. As a proof of concept, a Personalized Web architecture that employs ontology-based semantics and a structured Peer-to-Peer overlay network to provide a foundation of semantically-based resource indexing and advertising is developed. In order to investigate mechanisms that support the resource advertising and retrieval of the Personalized Web architecture, three agent-driven advertising and retrieval schemes, the Aggressive scheme, the Crawler-based scheme, and the Minimum-Cover-Rule scheme, were implemented and evaluated in both stable and churn environments. In addition to the development of a Personalized Web architecture that deals with typical web resources, this thesis used a case study to explore the potential of the Personalized Web architecture to support future web service workflow applications. The results of this investigation demonstrated that the architecture can support the automation of service discovery, negotiation, and invocation, allowing service consumers to actualize a personalized web service workflow. Further investigation will be required to improve the performance of the automation and allow it to be performed in a secure and robust manner. In order to support the next generation Internet, further exploration will be needed for the development of a Personalized Web that includes ubiquitous and pervasive resources

Octopus: A Secure and Anonymous DHT Lookup

Distributed Hash Table (DHT) lookup is a core technique in structured peer-to-peer (P2P) networks. Its decentralized nature introduces security and privacy vulnerabilities for applications built on top of them; we thus set out to design a lookup mechanism achieving both security and anonymity, heretofore an open problem. We present Octopus, a novel DHT lookup which provides strong guarantees for both security and anonymity. Octopus uses attacker identification mechanisms to discover and remove malicious nodes, severely limiting an adversary's ability to carry out active attacks, and splits lookup queries over separate anonymous paths and introduces dummy queries to achieve high levels of anonymity. We analyze the security of Octopus by developing an event-based simulator to show that the attacker discovery mechanisms can rapidly identify malicious nodes with low error rate. We calculate the anonymity of Octopus using probabilistic modeling and show that Octopus can achieve near-optimal anonymity. We evaluate Octopus's efficiency on Planetlab with 207 nodes and show that Octopus has reasonable lookup latency and manageable communication overhead

A scalable approach for content based image retrieval in cloud datacenter

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops

Structured P2P Technologies for Distributed Command and Control

The utility of Peer-to-Peer (P2P) systems extends far beyond traditional file sharing. This paper provides an overview of how P2P systems are capable of providing robust command and control for Distributed Multi-Agent Systems (DMASs). Specifically, this article presents the evolution of P2P architectures to date by discussing supporting technologies and applicability of each generation of P2P systems. It provides a detailed survey of fundamental design approaches found in modern large-scale P2P systems highlighting design considerations for building and deploying scalable P2P applications. The survey includes unstructured P2P systems, content retrieval systems, communications structured P2P systems, flat structured P2P systems and finally Hierarchical Peer-to-Peer (HP2P) overlays. It concludes with a presentation of design tradeoffs and opportunities for future research into P2P overlay systems