PatchPeer: A scalable video-on-demand streaming system in hybrid wireless mobile peer-to-peer networks

Video-on-demand service in wireless networks is one important step to achieving the goal of providing video services anywhere anytime. Typically, carrier mobile networks are used to deliver videos wirelessly. Since every video stream comes from the base station, regardless of what bandwidth sharing techniques are being utilized, the media stream system is still limited by the network capacity of the base station. The key to overcome the scalability issue is to exploit resources available at mobile clients in a peer-to-peer setting. We observe that it is common to have a carrier mobile network and a mobile peer-to-peer network co-exist in a wireless environment. A feature of such hybrid environment is that the former offers high availability assurance, while the latter presents an opportunistic use of resources available at mobile clients. Our proposed video-on-demand technique, PatchPeer, leverages this network characteristic to allow the video-on-demand system scale beyond the bandwidth capacity of the server. Mobile clients in PatchPeer are no longer passive receivers, but also active senders of video streams to other mobile clients. Our extensive performance study shows that PatchPeer can accept more clients than the current state-of-the-art technique, while maintaining the same Quality-of-Service to clients.

A contribution-based service differentiation scheme for peer-to-peer systems

Trust is required in a file sharing peer-to-peer system to achieve better cooperation among peers. In reputation-based peer-to-peer systems, reputation is used to build trust among peers. In these systems, highly reputable peers will usually be selected to upload requested files, decreasing significantly malicious uploads in the system. However, these peers need to be motivated by increasing the benefits that they receive from the system. In addition, it is necessary to motivate free riders to contribute to the system by sharing files. Malicious peers should be also motivated to contribute positively by uploading authentic files instead of malicious ones. Service differentiation is required to motivate peers to get involved by sharing and uploading the requested files. To provide the right incentives for peers to contribute to the system, the new concept of Contribution Behavior is introduced for partially decentralized peer-to-peer systems. In this paper, the Contribution Behavior of the peer is used as a guideline for service differentiation instead of peer’s reputation. Both Availability and Involvement of the peer are used to assess its Contribution Behavior. Performance evaluations confirm the ability of the proposed scheme to effectively identify both free riders and malicious peers and reduce the level of service provided to them. On the other hand, good peers receive better service. Simulation results also confirm that based on a Rational Behavior, peers are motivated to increase their contribution to receive services. Moreover, using our scheme, peers must continuously participate, reducing significantly the milking phenomenon.

Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method

Unlabeled training examples are readily available in many applications, but labeled examples are fairly expensive to obtain. For instance, in our previous works on classification of peer-to-peer (P2P) Internet traffics, we observed that only about 25% of examples can be labeled as “P2P”or “NonP2P” using a port-based heuristic rule. We also expect that even fewer examples can be labeled in the future as more and more P2P applications use dynamic ports. This fact motivates us to investigate the techniques which enhance the accuracy of P2P traffic classification by exploiting the unlabeled examples. In addition, the Internet data flows dynamically in large volumes (streaming data). In P2P applications, new communities of peers often join and old communities of peers often leave, requiring the classifiers to be capable of updating the model incrementally, and dealing with concept drift. Based on these requirements, this paper proposes an incremental Tri-Training (iTT) algorithm. We tested our approach on a real data stream with 7.2 Mega labeled examples and 20.4 Mega unlabeled examples. The results show that iTT algorithm can enhance accuracy of P2P traffic classification by exploiting unlabeled examples. In addition, it can effectively deal with dynamic nature of streaming data to detect the changes in communities of peers. We extracted attributes only from the IP layer, eliminating the privacy concern associated with the techniques that use deep packet inspection.

Improving robustness of P2P applications in mobile environments

Mobile P2P networks possess particular characteristics which make accessibility of services deployed on peers a challenge. This has to be taken into account when considering robustness of applications that depend on successfully accessing a set of services. While ensuring robustness is traditionally handled through replication or redundancy, those solutions are not readily applicable to decentralized and dynamic networks. Instead, current solutions are based on efficient P2P structure maintenance or unstructured network search algorithms. A novel and alternative method proposed in this paper is based on the observation that some redundancy may exist between services offered on the network, a fact which could be used to recreate an unavailable service from services accessible to a peer. Instead of adding redundancy to the system, our solution exploits the already existing redundancy to improve robustness of mobile P2P applications. We model the interaction with services as finite-state transducers and propose a heuristic to obtain redundancy between any pair of services. Then, a set of algorithms that uses this inter-service redundancy to recreate the interaction with one service from the other is discussed. The computational cost is polynomial with respect to services’ size, and in practice, the non-redundant functionality and related control need to be implemented locally.

Adaptive component composition and load balancing for distributed stream processing applications

Providing real-time and QoS support to stream processing applications running on top of large-scale overlays is challenging due to the inherent heterogeneity and resource limitations of the nodes and the multiple QoS demands of the applications that must concurrently be met. In this paper we propose an integrated adaptive component composition and load balancing mechanism that (1) allows the composition of distributed stream processing applications on the fly across a large-scale system, while satisfying their QoS demands and distributing the load fairly on the resources, and (2) adapts dynamically to changes in the resource utilization or the QoS requirements of the applications. Our extensive experimental results using both simulations as well as a prototype deployment illustrate the efficiency, performance and scalability of our approach.

DAg-stream: Distributed video adaptation for overlay streaming to heterogeneous devices

Combining the advantages of Peer-to-Peer (P2P) content distribution concept and metadata driven adaptation of videos in compressed domain, in this paper, we propose a simple but scalable design of distributed adaptation and overlay streaming using MPEG-21 gBSD, called DAg-stream. The objective is not only to shift the bandwidth burden to end participating peers, but also to move the computation load for adapting video contents away from dedicated media-streaming/adaptation servers. It is an initiative to merge the adaptation operations and the P2P streaming basics to support the expansion of context-aware mobile P2P systems. DAg-stream organizes mobile and heterogeneous peers into overlays. For each video, a separate overlay is formed. No control message is exchanged among peers for overlay maintenance. We present a combination of infrastructure-centric and application end-point architecture. The infrastructure-centric architecture refers to a tree controller, named DAg-master, which is responsible for tree/overlay administering and maintenance. The application end-point architecture refers to video sharing, streaming and adaptation by the participating resourceful peers. The motivation for this work is based on the experiences and lessons learned so far about developing a video adaptation system for heterogeneous devices. In this article, we present our architecture and some experimental evaluations supporting the design concept for overlay video streaming and online adaptation.

DHT routing analysis in a logarithmically transformed space

This paper presents an analytical model that helps understanding the common foundations of routing in DHTs and provides means for analytical comparison of different systems and different parameter combinations. In the proposed model, a logarithmic transformation is applied to the metric space embedding node identifiers. We show that in this transformed space - similarly to short-range connections in the real metric space - long-range connections have linear properties: connections are uniformly distributed and routing via long-range contacts progresses linearly toward the target. Using this transformation model, we introduce a λ long-range connection density parameter to characterize DHT routing and analyze common properties and differences between existing DHT routing mechanisms. For the the two extreme DHT families (“most random” and completely deterministic), we also present a detailed stochastic analysis of routing in the transformed space and express analytically the expected value of the number of routing hops.

On peer-to-peer (P2P) content delivery

In both academia and industry, peer-to-peer (P2P) applications have attracted great attentions. P2P applications such as Napster, Gnutella, FastTrack, BitTorrent, Skype and PPLive, have witnessed tremendous success among the end users. Unlike a client-server based system, peers bring with them serving capacity. Therefore, as the demand of a P2P system grows, the capacity of the network grows, too. This enables a P2P application to be cheap to build and superb in scalability. In this paper, we survey the state of the art of the research and the development of P2P content delivery application. Using examples of the deployed P2P applications and research prototypes, we survey the best practices in P2P overlay building and P2P scheduling. We hope that the information may help the readers to build a reliable, robust P2P content delivery application.

A unifying framework of rating users and data items in peer-to-peer and social networks

We propose a unifying family of quadratic cost functions to be used in Peer-to-Peer ratings. We show that our approach is general since it captures many of the existing algorithms in the fields of visual layout, collaborative filtering and Peer-to-Peer rating, among them Koren spectral layout algorithm, Katz method, Spatial ranking, Personalized PageRank and Information Centrality. Besides of the theoretical interest in finding common basis of algorithms that where not linked before, we allow a single efficient implementation for computing those various rating methods. We introduce a distributed solver based on the Gaussian Belief Propagation algorithm which is able to efficiently and distributively compute a solution to any single cost function drawn from our family of quadratic cost functions. By implementing our algorithm once, and choosing the computed cost function dynamically on the run we allow a high flexibility in the selection of the rating method deployed in the Peer-to-Peer network. Using simulations over real social network topologies obtained from various sources, including the MSN Messenger social network, we demonstrate the applicability of our approach. We report simulation results using networks of millions of nodes.

Peer sampling with improved accuracy

Node sampling services provide peers in a peer-to-peer system with a source of randomly chosen addresses of other nodes. Ideally, samples should be independent and uniform. The restrictions of a distributed environment, however, introduce various dependancies between samples. We review gossip-based sampling protocols proposed in previous work, and identify sources of inaccuracy. These include replicating the items from which samples are drawn, and imprecise management of the process of refreshing items. Based on this analysis, we propose a new protocol, Eddy, which aims to minimize temporal and spatial dependancies between samples. We demonstrate, through extensive simulation experiments, that these changes lead to an improved sampling service. Eddy maintains a balanced distribution of items representing active system nodes, even in the face of realistic levels of message loss and node churn. As a result, it behaves more like a centralized random number generator than previous protocols. We demonstrate this by showing that using Eddy improves the accuracy of a simple algorithm that uses random samples to estimate the size of a peer-to-peer network.

On the Minimization of Communication in Networked Systems with a Central Station

The problem of minimizing communication in a distributed networked system is considered in a discrete-event formalism where the system is modeled as a finite-state automaton. The system consists of a central station and a set of N local agents, each observing a set of local events. The central station needs to know exactly the state of the system, whereas local agents need to disambiguate certain pre-specified pairs of states for purposes of control or diagnosis. This requirement is achieved by communication, which occurs only between the central station and the local agents but not among the local agents. A communication policy is defined as a set of event occurrences to be communicated between the central station and the local agents. A communication policy is said to be minimal if any removal of communication of event occurrences will affect the correctness of the solution. Under an assumption on the absence of cycles (other than self-loops) in the system model, this paper presents an algorithm that computes a minimal communication policy in polynomial time in all parameters of the system. These results improve upon previous algorithms for solving minimum communication problems.

Theory of relative defect proneness

In this study, we investigated the functional form of the size-defect relationship for software modules through replicated studies conducted on ten open-source products. We consistently observed a power-law relationship where defect proneness increases at a slower rate compared to size. Therefore, smaller modules are proportionally more defect prone. We externally validated the application of our results for two commercial systems. Given limited and fixed resources for code inspections, there would be an impressive improvement in the cost-effectiveness, as much as 341% in one of the systems, if a smallest-first strategy were preferred over a largest-first one. The consistent results obtained in this study led us to state a theory of relative defect proneness (RDP): In large-scale software systems, smaller modules will be proportionally more defect-prone compared to larger ones. We suggest that practitioners consider our results and give higher priority to smaller modules in their focused quality assurance efforts.

SUPNET: An end-to-end solution to scalable unstructured P2P networking

Despite many improvements on original unstructured P2P networks, these systems still suffer from several problems, the most important of which are, (a) lack of guarantees on the integrity of the network topology in the face of churns, (b) excessive traffic cost and (c) poor quality of search results. This paper introduces an end-to-end scalable unstructured P2P networking solution called SUPNET to address many of these issues. SUPNET is based on our pragmatic, design oriented approach to engineering complex networks. Rather than modeling dynamical behavior in already-existing networks, we actively design and implement local stochastic dynamics so that an engineered global system, with predictable structures emerges. The resulting protocol, SUPNET, consists of two sub-protocols for network management and content search. The network management sub-protocol is scalable and highly robust and is capable of utilizing the heterogeneous distribution of network resources. Its high stability is the result of implementation of a novel distributed feedback mechanism. The search sub-protocol is capable of locating every item, even if a single copy of that item exists in the network, while producing a traffic that scales provably sub-linear with the network size. It also contains mechanisms for very efficient location of popular items as well as distributed parameter tuning algorithms. These, along with inherently self-organized and de-centralized operation, relative ease of implementation and solid analytical foundation, make SUPNET a compelling solution for unstructured P2P networking.

Open media service architecture for advanced collaboration environments

Advanced collaboration environments are extensively utilized for distance learning, e-science, and other distributed global collaboration events. In such environments, high-quality and seamless media services play an important role in improving the quality of user experience to participants. In this paper, to support high-quality media-based services, we design open media service architecture for advanced collaboration environments, by combining the open interface for state-of-the-art media tools, the performance monitoring tools for devices and networks, and application-level adaptation schemes for media streaming. By implementing the proposed architecture on top of an open-source Access Grid (AG) collaboration toolkit, we verify that high-quality collaboration among several collaboration sites can be effectively realized over a multicast-enabled network testbed with improved media quality experience.

Simulation-based analysis of UML statechart diagrams: methods and case studies

We present a comprehensive unified modeling language (UML) statechart diagram analysis framework. This framework allows one to progressively perform different analysis operations to analyze UML statechart diagrams at different levels of model complexity. The analysis operations supported by the framework are based on analyzing Petri net models converted from UML statechart diagrams using a previously proposed transformation approach. After introducing the general framework, the paper emphasizes two simulation-based analysis operations from the framework: direct MSC inspection, which provides a visual representation of system behavior described by statechart diagrams; and a pattern-based trace query technique, which can be used to define and query system properties. Two case-study examples are presented with different emphasis. The gas station example is a simple multi-object system used to demonstrate both the visual and query-based analysis operations. The early warning system example uses only one object, but features composite states and includes analysis specifically aimed at one composite state feature, history states.

Processing Optimal Sequenced Route Queries Using Voronoi Diagrams

The Optimal Sequenced Route (OSR) query strives to find a route of minimum length starting from a given source location and passing through a number of typed locations in a specific sequence imposed on the types of the locations. In this paper, we propose a pre-computation approach to OSR query in both vector and metric spaces. We exploit the geometric properties of the solution space and theoretically prove its relation to additively weighted Voronoi diagrams. Our approach recursively accesses these diagrams to incrementally build the OSR. Introducing the analogous diagrams for the space of road networks, we show that our approach is also efficiently applicable to this metric space. Our experimental results verify that our pre-computation approach outperforms the previous index-based approaches in terms of query response time. This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC), IIS-0238560 (PECASE), IIS-0324955 (ITR), IIS-0534761, and unrestricted cash gifts from Google and Microsoft. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.

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Using visual and text features for direct marketing on multimedia messaging services domain

Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience. Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS) show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.

Partially Observable Markov Decision Process Approximations for Adaptive Sensing

Adaptive sensing involves actively managing sensor resources to achieve a sensing task, such as object detection, classification, and tracking, and represents a promising direction for new applications of discrete event system methods. We describe an approach to adaptive sensing based on approximately solving a partially observable Markov decision process (POMDP) formulation of the problem. Such approximations are necessary because of the very large state space involved in practical adaptive sensing problems, precluding exact computation of optimal solutions. We review the theory of POMDPs and show how the theory applies to adaptive sensing problems. We then describe a variety of approximation methods, with examples to illustrate their application in adaptive sensing. The examples also demonstrate the gains that are possible from nonmyopic methods relative to myopic methods, and highlight some insights into the dependence of such gains on the sensing resources and environment.

Building measure-based prediction models for UML class diagram maintainability

The usefulness of measures for the analysis and design of object oriented (OO) software is increasingly being recognized in the field of software engineering research. In particular, recognition of the need for early indicators of external quality attributes is increasing. We investigate through experimentation whether a collection of UML class diagram measures could be good predictors of two main subcharacteristics of the maintainability of class diagrams: understandability and modifiability. Results obtained from a controlled experiment and a replica support the idea that useful prediction models for class diagrams understandability and modifiability can be built on the basis of early measures, in particular, measures that capture structural complexity through associations and generalizations. Moreover, these measures seem to be correlated with the subjective perception of the subjects about the complexity of the diagrams. This fact shows, to some extent, that the objective measures capture the same aspects as the subjective ones. However, despite our encouraging findings, further empirical studies, especially using data taken from real projects performed in industrial settings, are needed. Such further study will yield a comprehensive body of knowledge and experience about building prediction models for understandability and modifiability.