Answering similarity queries in peer-to-peer networks

A variety of peer-to-peer (P2P) systems for sharing digital information are currently available and most of them perform searching by exact key matching. In this paper we focus on similarity searching and describe FuzzyPeer, a generic broadcast-based P2P system which supports a wide range of fuzzy queries. As a case study we present an image retrieval application implemented on top of FuzzyPeer. Users provide sample images whose sets of features are propagated through the peers. The answer consists of the top-k most similar images within the query horizon. In our system the participation of peers is ad hoc and dynamic, their functionality is symmetric and there is no centralized index.     

Reducing communication costs in robust peer-to-peer networks

Several recent research results describe how to design Distributed Hash Tables (DHTs) that are robust to adversarial attack via Byzantine faults. Unfortunately, all of these results require a significant blowup in communication costs over standard DHTs. For example, to perform a lookup operation, all such robust DHTs of which we are aware require sending O(log³n) messages while standard DHTs require sending only O(logn), where n is the number of nodes in the network. In this paper, we describe protocols to reduce the communication costs of all such robust DHTs. In particular, we give a protocol to reduce the number of messages sent to perform a lookup operation from O(log³n) to O(log²n) in expectation. Moreover, we also give a protocol for sending a large (i.e. containing Ω(log⁴n) bits) message securely through a robust DHT that requires, in expectation, only a constant blowup in the total number of bits sent compared with performing the same operation in a standard DHT. This is an improvement over the O(log²n) bit blowup that is required to perform such an operation in all current robust DHTs. Both of our protocols are robust against an adaptive adversary.     

Online reputation measurement of companies based on user-generated content in online social networks

Social media websites such as Facebook, Twitter, etc. has changed the way peoples communicate and make decision. In this regard, various companies are willing to use these media to raise their reputation. In this paper, a reputation management system is proposed which measures the reputation of a given company by using the social media data, particularly tweets of Twitter. Taking into account the name of the company and its' related tweets, it is determined that a given tweet has either negative or positive impact on the company's reputation or product. The proposed method is based on N-gram learning approach, which consists of two steps: train step and test step. In the training step, we consider four profiles i.e. positive, negative, neutral, and irrelevant profiles for each company. Then 80% of the available tweets are used to build the companies' profiles. Each profile contains the terms that have been appeared in the tweets of each company together with the terms' frequencies. Then in the test step, which is performed on the 20% remaining tweets of the dataset, each tweet is compared with all of the built profiles, based on distance criterion to examine how the given tweet affects a company's reputation. Evaluation of the proposed method indicates that this method has a better efficiency and performance in terms of recall and precision compared to the previous methods such as Neural Network and Bayesian method.     

Practical load balancing for content requests in peer-to-peer networks

This paper studies the problem of balancing the demand for content in a peer-to-peer network across heterogeneous peer nodes that hold replicas of the content. Previous decentralized load balancing techniques in distributed systems base their decisions on periodic updates containing information about load or available capacity observed at the serving entities. We show that these techniques do not work well in the peer-to-peer context; either they do not address peer node heterogeneity, or they suffer from significant load oscillations which result in unutilized capacity. We propose a new decentralized algorithm, Max-Cap, based on the maximum inherent capacities of the replica nodes. We show that unlike previous algorithms, it is not tied to the timeliness or frequency of updates, and consequently requires significantly less update overhead. Yet, Max-Cap can handle the heterogeneity of a peer-to-peer environment without suffering from load oscillations. Mema Roussopoulos is an Assistant Professor of Computer Science on the Gordon McKay Endowment at Harvard University. Before joining Harvard, she was a Postdoctoral Fellow in the Computer Science Department at Stanford University. She received her PhD and Master’s degrees in Computer Science from Stanford, and her Bachelor’s degree in Computer Science from the University of Maryland at College Park. Her interests are in the areas of distributed systems, networking, and mobile and wireless computing. Mary Baker is a Senior Research Scientist at HP Labs. Her research interests include distributed systems, networks, mobile systems, security, and digital preservation. Before joining HP Labs she was on the faculty of the computer science department at Stanford University where she ran the MosquitoNet project. She received her PhD from the University of California at Berkeley.     

Reputation based friend-to-friend networks

Friend-to-Friend networks are a form of Peer-to-Peer networks that provide anonymity of the users who share data. Turtle is an example of such a network, where a peer only connects to real life friends. In this paper we propose an extension of Turtle for connecting to other nodes based on their reputation. Furthermore, we use trust management to dynamically adjust this reputation based on the honest or malicious behaviour of all connected peers. Using these techniques, a node is able to create links to other nodes and identify possible misbehaving friends. Finally, we present simulation results of a reputation based Turtle Friend-to-Friend network.

Meta-searches in peer-to-peer networks

We propose a method for carrying out enhanced collaborative searches, called meta-searches, in peer-to-peer networks. In addition to performing regular searches, our method supports searches based on other network users’ previous searches on the same or similar topic. In essence, when a user performs a search, s/he will receive not only the usual result set, but also information on other users’ previous results, as well as relevancy information (such as how many times a resource that appeared in the result set was successfully downloaded). The core components of meta-search are query relevancy calculation, query matching algorithms, and relevancy file format. In this paper we discuss the underlying concepts and principles, and describe the component design in detail. Meta-search provides a way of benefiting from other users’ successful searches without any additional effort, thus potentially improving the efficiency and experience of a search.     

Adaptive group scheduling mechanism using mobile agents in peer-to-peer grid computing environment

Peer-to-peer grid computing is an attractive computing paradigm for high throughput applications. However, both volatility due to the autonomy of volunteers (i.e., resource providers) and the heterogeneous properties of volunteers are challenging problems in the scheduling procedure. Therefore, it is necessary to develop a scheduling mechanism that adapts to a dynamic peer-to-peer grid computing environment. In this paper, we propose a Mobile Agent based Adaptive Group Scheduling Mechanism (MAAGSM). The MAAGSM classifies and constructs volunteer groups to perform a scheduling mechanism according to the properties of volunteers such as volunteer autonomy failures, volunteer availability, and volunteering service time. In addition, the MAAGSM exploits a mobile agent technology to adaptively conduct various scheduling, fault tolerance, and replication algorithms suitable for each volunteer group. Furthermore, we demonstrate that the MAAGSM improves performance by evaluating the scheduling mechanism in Korea@Home. SungJin Choi is a Ph.D. student in the Department of Computer Science and Engineering at Korea University. His research interests include mobile agent, peer-to-peer computing, grid computing, and distributed systems. Mr. Choi received a M.S. in computer science from Korea University. He is a student member of the IEEE. MaengSoon Baik is a senior research member at the SAMSUNG SDS Research & Develop Center. His research interests include mobile agent, grid computing, server virtualization, storage virtualization, and utility computing. Dr. Baik received a Ph.D. in computer science from Korea University. JoonMin Gil is a professor in the Department of Computer Science Education at Catholic University of Daegu, Korea. His recent research interests include grid computing, distributed and parallel computing, Internet computing, P2P networks, and wireless networks. Dr. Gil received his Ph.D. in computer science from Korea University. He is a member of the IEEE and the IEICE. SoonYoung Jung is a professor in the Department of Computer Science Education at Korea University. His research interests include grid computing, web-based education systems, database systems, knowledge management systems, and mobile computing. Dr. Jung received his Ph.D. in computer science from Korea University. ChongSun Hwang is a professor in the Department of Computer Science and Engineering at Korea University. His research interests include distributed systems, distributed algorithms, and mobile computing. Dr. Hwang received a Ph.D. in statistics and computer science from the University of Georgia.     

Adaptive searching and replication of images in mobile hierarchical peer-to-peer networks

Due to the limitation of resources in mobile handheld devices and bandwidth constraints in wireless networks, it is important to efficiently manage image content and traffic to improve network throughput and response time. The efficient distribution of images in wireless networks is based on many parameters like the quality of service requirement, available bandwidth, restricted memory and diverse user profiles. We consider digital images as data objects accessed in a hierarchical mobile ad-hoc peer-to-peer network, referred as M-P2P. Our objective is to optimize the number of replicas of images in such a network architecture based on the resource limitations of the requesting peers equipped with miniature mobile devices as well as of wireless constraints in mobile ad-hoc networks (MANET). While replicating images, fragments of different resolution and their residues are used to optimize the traffic in the network and to decrease the search turnaround time. We call this replication algorithm as Ada-Rep. Performance evaluation is done by simulating Ada-Rep, base replication and uniform replication methods to evaluate parameters such as response time, failure rate, memory usage, traffic etc. Our results prove the efficiency and better performance of Ada-Rep.     

Adaptive approach to restraining content pollution in peer-to-peer networks

Peer-to-Peer (P2P) networks face the challenge of frequent pollution attacks. In such attacks, malicious peers pollute the network by sharing mislabeled, corrupt or infected content in an attempt to disrupt the system and waste network resources. When faced by such phenomenon, regular peers get discouraged from participating in the P2P network as they find less value in the system. In this work, we investigate the amount of resources required to restrain pollution attacks by means of content validation. We introduce multiple adaptive techniques that can minimize the spread of polluted content, while at the same time reduce the cost of content validation for peers participating in the network. Furthermore, the proposed pollution-restraint techniques are resistant to collusion from malicious peers, and they do not contribute to excessive communication overhead in the P2P network.     

Real-time data dissemination in mobile peer-to-peer networks

Mobile peer-to-peer networks have found many uses such as streaming of audio and video data. There are circumstances, such as emergency situations and disaster recovery, when real-time delivery is a fundamental requirement. The problem is challenging due to the limited network capacity, the variable transmission rates and the unpredictability with respect to the network conditions in the mobile peer-to-peer network.In this paper we address the problem of real-time data dissemination of multimedia streams in mobile peer-to-peer networks. Four routing algorithms are proposed based on a packet's deadline, priority or a combination of these metrics. They are simulated under different setups in a mobile peer-to-peer network with Bluetooth connectivity and nodes broadcasting audio and video streams using different priorities. We compare the performance of the algorithms using a number of metrics. Detailed experimental results are presented. Based on these results, propositions on the usage of the algorithms and the design of network requirements are presented.     

Contention-based performance evaluation of multidimensional range search in peer-to-peer networks

Performance evaluation of peer-to-peer search techniques has hitherto been based on simple performance metrics, such as message hop counts and total network traffic, mostly disregarding the inherently concurrent nature of peer-to-peer networks, where contention may arise. This paper is concerned with quantifying the effects of contention in P2P networks, focusing on networks for multidimensional range search. We evaluate peer-to-peer networks derived from recently proposed works, introducing two novel metrics related to concurrency and contention, namely responsiveness and throughput. Our results highlight the impact of contention on these networks, and demonstrate that some studied networks do not scale in the presence of contention. Also, our results indicate that certain P2P network properties believed to be desirable (e.g. even data distribution or uniform peer access) may not be as critical as previously believed.     

Building a tuple space on structured peer-to-peer networks

Research on peer-to-peer (P2P) networks has attracted much attention recently. A key factor is their ability to handle dynamics in the system in a distributed fashion, including ad hoc and dynamic join and departure of the peers and dynamic changes in the underlying network environment. As more and more innovating P2P applications appear, the need to support intuitive communication and synchronization among the peers in the P2P system becomes imperative. In this paper, we discuss how to build a tuple space on top of P2P systems and use on-line games as a target application. A simple API is supported for accessing the data stored in the tuple space and communicating between the peers. Through experiments and a demonstration game, we show that our system can facilitate the development of network games in a fully distributed environment.

Adaptive low-latency peer-to-peer streaming and its application

Peer-to-peer (P2P) streaming is emerging as a viable communications paradigm. Recent research has focused on building efficient and optimal overlay multicast trees at the application level. A few commercial products are being implemented to provide voice services through P2P streaming platforms. However, even though many P2P protocols from the research community claim to be able to support large scale low-latency streaming, none of them have been adopted by a commercial voice system so far. This gap between advanced research prototypes and commercial implementations shows that there is a lack of a practical and scalable P2P system design that can provide low-latency service in a real implementation. After analyzing existing P2P system designs, we found two important issues that could lead to improvements. First, many existing designs that aim to build a low-latency streaming platform often make the unreasonable assumption that the processing time involved at each node is zero. However in a real implementation, these delays can add up to a significant amount of time after just a few overlay hops and make interactive applications difficult. Second, scant attention has been paid to the fact that even in a conversation involving a large number of users, only a few of the users are actually actively speaking at a given time. We term these users, who have more critical demands for low-latency, active users. In this paper, we detail the design of a novel peer-to-peer streaming architecture called ACTIVE. We then present a complete commercial scale voice chat system called AudioPeer that is powered by the ACTIVE protocol. The ACTIVE system significantly reduces the end-to-end delay experienced among active users while at the same time being capable of providing streaming services to very large multicast groups. ACTIVE uses realistic processing assumptions at each node and dynamically optimizes the streaming structure while the group of active users changes over time. Consequently, it provides virtually all users with the low-latency service that before was only possible with a centralized approach. We present results from both simulations and our real implementation, which clearly show that our ACTIVE system is a feasible approach to scalable, low-latency P2P streaming.     

Using a distributed quadtree index in peer-to-peer networks

Peer-to-peer (P2P) networks have become a powerful means for online data exchange. Currently, users are primarily utilizing these networks to perform exact-match queries and retrieve complete files. However, future more data intensive applications, such as P2P auction networks, P2P job-search networks, P2P multiplayer games, will require the capability to respond to more complex queries such as range queries involving numerous data types including those that have a spatial component. In this paper, a distributed quadtree index that adapts the MX-CIF quadtree is described that enables more powerful accesses to data in P2P networks. This index has been implemented for various prototype P2P applications and results of experiments are presented. Our index is easy to use, scalable, and exhibits good load-balancing properties. Similar indices can be constructed for various multidimensional data types with both spatial and non-spatial components.     

SKY: efficient peer-to-peer networks based on distributed Kautz graphs

Many proposed P2P networks are based on traditional interconnection topologies. Given a static topology, the maintenance mechanism for node join/departure is critical to designing an efficient P2P network. Kautz graphs have many good properties such as constant degree, low congestion and optimal diameter. Due to the complexity in topology maintenance, however, to date there have been no effective P2P networks that are proposed based on Kautz graphs with base > 2. To address this problem, this paper presents the “distributed Kautz (D-Kautz) graphs”, which adapt Kautz graphs to the characteristics of P2P networks. Using the D-Kautz graphs we further propose SKY, the first effective P2P network based on Kautz graphs with arbitrary base. The effectiveness of SKY is demonstrated through analysis and simulations. Supported partially by the National Natural Science Foundation of China (Grant Nos. 60673167 and 60703072), the Hunan Provincial Natural Science Foundation of China (Grant No. 08JJ3125), and the National Basic Research Program of China (973) (Grant No. 2005CB321801)     

Semantic indexing in structured peer-to-peer networks

The past few years have seen tremendous advances in distributed storage infrastructure. Unstructured and structured overlay networks have been successfully used in a variety of applications, ranging from file-sharing to scientific data repositories. While unstructured networks benefit from low maintenance overhead, the associated search costs are high. On the other hand, structured networks have higher maintenance overheads, but facilitate bounded time search of installed keywords. When dealing with typical data sets, though, it is infeasible to install every possible search term as a keyword into the structured overlay.     

A novel approach to improving search efficiency in unstructured peer-to-peer networks

The decentralized peer-to-peer (P2P) technique has been widely used to implement scalable file sharing systems. It organizes nodes in a system into a structured or unstructured network. The advantages of the unstructured P2P systems are that they have lower maintenance complexity and can better adapt to node heterogeneity as well as network dynamics. However, the search process in unstructured systems is not as efficient as in structured P2P systems because the same search message may go through a node multiple times. To facilitate the complex search and improve the search efficiency, we propose a novel approach of assigning identifications to nodes in an unstructured system. Our method can prevent a node from receiving duplicate search messages and retain the low maintenance overhead for the system. The performance evaluations demonstrate that the proposed approach can improve the search efficiency of unstructured P2P systems while keeping the maintenance overhead at a comparable or even lower level, compared with the traditional unstructured systems.     

Proactive replication for rare objects in unstructured peer-to-peer networks

Unstructured peer-to-peer (P2P) networks have become a very popular architecture for content distribution in large-scale and dynamic environments. The search efficiency problem in unstructured P2P networks has not been adequately addressed so far, especially concerning search for rare objects. In this paper, we propose a proactive replication strategy to improve search efficiency for rare objects. It uses an object-probing technique for peers to decide whether or not to establish replications for their objects when they join the network. This strategy can effectively increase the popularity of rare objects in order to enhance search efficiency. We also present a rare object search algorithm to reduce the overhead caused by the replication strategy. When a peer forwards a search request, the forward probability is calculated according to its neighbors' degrees and the number of neighbors' objects. Therefore, the search request is forwarded to the peers more likely containing target objects. Simulations show that our proactive replication strategy greatly improves search efficiency for rare objects with moderate communication overhead. The rare object search algorithm not only improves search efficiency for rare objects, but also achieves load balance in search.     

An efficient incentive scheme with a distributed authority infrastructure in peer-to-peer networks

Today’s peer-to-peer networks are designed based on the assumption that the participating nodes are cooperative, which does not hold in reality. Incentive mechanisms that promote cooperation must be introduced. However, the existing incentive schemes (using either reputation or virtual currency) suffer from various attacks based on false reports. Even worse, a colluding group of malicious nodes in a peer-to-peer network can manipulate the history information of its own members, and the damaging power increases dramatically with the group size. Such malicious nodes/collusions are difficult to detect, especially in a large network without a centralized authority. In this paper, we propose a new distributed incentive scheme, in which the amount that a node can benefit from the network is proportional to its contribution, malicious nodes can only attack others at the cost of their own interests, and a colluding group cannot gain advantage by cooperation regardless of its size. Consequently, the damaging power of colluding groups is strictly limited. The proposed scheme includes three major components: a distributed authority infrastructure, a key sharing protocol, and a contract verification protocol.