Design and implementation of a service oriented architecture for peer-assisted video-on-demand

Compared with the traditional client/server streaming model, peer-assisted video streaming has been shown to provide better scalability with lower infrastructure cost. In this paper, we describe how peer-assisted video streaming can be implemented through real-time service oriented architecture. The first part of the paper presents an overall design of the Peer-Assisted ContenT Service (PACTS). We discuss the motivation, principles and service oriented architecture of PACTS modules and specify the workflow among them. By organizing elements of traditional video streaming and peer-to-peer computing into loosely-coupled composable middleware services and distributing them among participating entities, PACTS enables high-quality low-cost video streaming at a large scale and in real time. The second part of the paper describes an implementation of PACTS using existing off-the-shelf software followed by a performance evaluation based on practical environment settings. We illustrate the challenges and our approaches in designing distributed and highly efficient algorithms. In particular, the algorithms for performing peering-selection and incentive-driven pre-fetching are studied in detail. These designs are extensively evaluated by packet-level simulations. We show that our implementation of PACTS effectively offload server’s bandwidth demand without sacrificing the service quality. This benefit is further verified in dynamic settings with system churns. The simulation results show that the incentive mechanism from our service level agreement efficiently stabilizes the server bandwidth utilization with less than 4.5% control traffic overhead.     

P2Cast: peer-to-peer patching for video on demand service

Providing video on demand (VoD) service over the Internet in a scalable way is a challenging problem. In this paper, we propose P2Cast—an architecture that uses a peer-to-peer approach to cooperatively stream video using patching techniques, while only relying on unicast connections among peers. We address the following two key technical issues in P2Cast: (1) constructing an application overlay appropriate for streaming; and (2) providing continuous stream playback (without glitches) in the face of disruption from an early departing client. Our simulation experiments show that P2Cast can serve many more clients than traditional client-server unicast service, and that it generally out-performs multicast-based patching if clients can cache more than 10% of a stream’s initial portion. We handle disruptions by delaying the start of playback and applying the shifted forwarding technique. The threshold in P2Cast, i.e., the length of time during which arriving clients form a single session, can serve as a “knob” to adjust the balance between the scalability and the clients’ viewing quality.

Performance analysis of a caching algorithm for a catch-up television service

The catch-up TV (CUTV) service allows users to watch video content that was previously broadcast live on TV channels and later placed on an on-line video store. Upon a request from a user to watch a recently missed episode of his/her favourite TV series, the content is streamed from the video server to the customer’s receiver device. This requires that an individual flow is set up for the duration of the video, and since it is hard to impossible to employ multicast streaming for this purpose (as users seldomly issue a request for the same episode at the same time), these flows are unicast. In this paper, we demonstrate that with the growing popularity of the CUTV service, the number of simultaneously running unicast flows on the aggregation parts of the network threaten to lead to an unwieldy increase in required bandwidth. Anticipating this problem and trying to alleviate it, the network operators deploy caches in strategic places in the network. We investigate the performance of such a caching strategy and the impact of its size and the cache update logic. We first analyse and model the evolution of video popularity over time based on traces we collected during 10 months. Through simulations we compare the performance of the traditional least-recently used and least-frequently used caching algorithms to our own algorithm. We also compare their performance with a “perfect” caching algorithm, which knows and hence does not have to estimate the video request rates. In the experimental data, we see that the video parameters from the popularity evolution law can be clustered. Therefore, we investigate theoretical models that can capture these clusters and we study the impact of clustering on the caching performance. Finally, some considerations on the optimal cache placement are presented.     

Diverse community: Demand differentiation in P2P live streaming

Peer-to-Peer (P2P) technology has become an attractive approach for enabling large-scale video streaming applications, but the factor of users’ subjective preferences is not paid enough attention in such networks. As users have various demand on video qualities, we can provide them with video streams at different resolutions without impairing their satisfaction. The adaptive streaming rate technique is a promising method. However, in providing adaptive streaming rate services, P2P live streaming design faces the following challenge: how to provide all users with uninterrupted video with their desired qualities in case that their demand dynamically changes? To shed more light on this problem, we first derive a model and formulate the problem as a resource demand vs supply problem. Then we present a framework to address the challenge via efficient bandwidth allocation and group cooperation. Through comprehensive simulations, we evaluate the effectiveness of the proposed framework, and conclude that it effectively helps existing solutions, such as Partial Participation Scheme (PPS), achieve better performance.     

Analyzing the impact of intermediaries in electronic markets: an empirical investigation of online consumer-to-consumer (C2C) auctions

It has been widely believed that the Internet and electronic markets will eliminate traditional intermediaries. However, a close examination of the market mechanism indicates that the impact of intermediaries is indispensable. Specifically, we consider intermediaries’ impacts in price discovery and trust building. Intermediaries provide a buffer for temporary misalignment between supply and demand by buying low and selling high, and also help build trust by engaging in transactions with risk-averse buyers and sellers who otherwise will not participate in the market. Using a dataset from eBay’s online auctions, we examine empirically these two impacts of human intermediaries. We find that the presence of brokers has a significant impact on market liquidity, resulting in more successful trades and higher auction prices. In addition, we find that brokers are more likely to engage in transactions with less established sellers. Their presence reduces reputation penalty faced by these players and further facilitates the successful sale of items in auctions.     

Automating user reviews using ontologies: an agent-based approach

The Web is becoming a global market place, where the same services and products are offered by different providers. When obtaining a service, consumers have to select one provider among many alternatives to receive a service or buy a product. In real life, when obtaining a service, many consumers depend on the user reviews. User reviews—presumably written by other consumers—provide details on the consumers’ experiences and thus are more informative than ratings. The down side is that such user reviews are written in natural language, making it extremely difficult to be interpreted by computers. Therefore, current technologies do not allow automation of user reviews and require too much human effort for tasks such as writing and reading reviews for the providers, aggregating existing information, and finally choosing among the possible candidates. In this paper, we represent consumers’ reviews as machine processable structures using ontologies and develop a layered multiagent framework to enable consumers to find satisfactory service providers for their needs automatically. The framework can still function successfully when consumers evolve their language and when deceptive reviewers enter the system. We show the flexibility of the framework by employing different algorithms for various tasks and evaluate them for different circumstances.     

On-line service scheduling

This paper is concerned with a scheduling problem that occurs in service systems where customers are classified as ‘ordinary’ and ‘special’. Ordinary customers can be served on any service facility, while special customers can be served only on the flexible service facilities. Customers arrive dynamically over time and their needs become known upon arrival. We assume any service, once started, will be carried out to its completion. In this paper, we study the worst-case performance of service policies used in practice. In particular, we evaluate three classes of service policies: policies with priority, policies without priority, and their combinations. We obtain tight worst-case performance bounds for all service policies considered.     

Video personalization in heterogeneous and resource-constrained environments

Access to multimedia data and multimedia services is becoming increasingly common in networked mobile environments. In such environments, both the mobile client devices and multimedia servers are typically resource constrained. Moreover, the mobile client population is often heterogeneous in terms of the clients’ preferences with regard to multimedia content, the clients’ quality of service requirements and system-level resource constraints. In order to provide a resource-constrained mobile client with its desired video content, it is often necessary to personalize the requested multimedia content in a manner that satisfies simultaneously the various client-specified content preferences and the system-level resource constraints. Also, in order to simultaneously reduce the client-experienced latency, provide optimal quality of service to the clients and ensure efficient utilization of server and network resources, it is necessary to perform client request aggregation on the server end. To this end, a video personalization strategy is proposed to provide mobile, resource-constrained clients with personalized video content that is most relevant to the clients’ requests while satisfying simultaneously multiple client-side system-level resource constraints. A client request aggregation strategy is also proposed to cluster client requests with similar video content preferences and similar client-side resource constraints such that the number of requests the server needs to process and service, and the client-experienced latency are both reduced simultaneously. The primary contributions of the paper are: (1) the formulation and implementation of a Multiple-choice Multi-dimensional Knapsack Problem (MMKP)-based video personalization strategy; and (2) the design and implementation of a client request aggregation strategy based on a multi-stage clustering algorithm. Experimental results comparing the proposed MMKP-based video personalization strategy to existing 0/1 Knapsack Problem (0/1KP)-based and the Fractional Knapsack Problem (FKP)-based video personalization strategies are presented. It is observed that: (1) the proposed MMKP-based personalization strategy includes more relevant video content in response to the client’s request compared to the existing 0/1KP-based and FKP-based personalization strategies; and (2) in contrast to the 0/1KP-based and FKP-based personalization strategies which can satisfy only a single client-side resource constraint at a time, the proposed MMKP-based personalization strategy is capable of satisfying multiple client-side resource constraints simultaneously. Experimental results comparing the client-experienced latency with and without the proposed server-side client request aggregation strategy are also presented. It is shown that the proposed client request aggregation strategy reduces the mean client-experienced latency without significant reduction in the average relevance of the delivered video content and without significant deviation in the client-side resources actually consumed by the delivered video content from the client-specified resource constraints.     

OCS: An effective caching scheme for video streaming on overlay networks

Video streaming is vital for many important applications such as distance learning, digital video libraries, and movie-on-demand. Since video streaming requires significant server and networking resources, caching has been used to reduce the demand on these resources. In this paper, we propose a novel collaboration scheme for video caching on overlay networks, called Overlay Caching Scheme (OCS), to further minimize service delays and loads placed on an overlay network for video streaming applications. OCS is not a centralized nor a hierarchical collaborative scheme. Despite its design simplicity, OCS effectively uses an aggregate storage space and capability of distributed overlay nodes to cache popular videos and serve nearby clients. Moreover, OCS is light-weight and adaptive to clients’ locations and request patterns. We also investigate other video caching techniques for overlay networks including both collaborative and non-collaborative ones. Compared with these techniques on topologies inspired from actual networks, OCS offers extremely low average service delays and approximately half the server load. OCS also offers smaller network load in most cases in our study.

Seamless channel transition for slotted generalized fibonacci broadcasting

Periodic Broadcasting schemes are cost- effective methods of implementation of Near Video on Demand systems. While the schemes enjoy the advantage of reducing the demand on server bandwidth, they suffer from the problem of insensitivity to the popularity of videos. However, the popularity of a video does not remain constant in the real world; it varies with time, social events, and so on. From a service provider’s viewpoint, given a set of popular videos and limited server bandwidth, it would be desirable to adjust the bandwidth allocated to each of the videos dynamically and seamlessly according to the level of its hotness. In this paper, we first re-formulate the Generalized Fibonacci Broadcasting as the Fixed-Length Segment- Scheduling problem and name the re-formulated scheme the Slotted Generalized Fibonacci Broadcasting (SGFB). We then propose a seamless channel transition enhancement on top of the SGFB scheme so that the service provider is capable of adjusting the channel allocation policy to make the most benefit out of the available bandwidth. The correctness of SGFB together with its performance analysis will be presented. Furthermore, we shall provide mathematical analysis to demonstrate its channel transition behavior. This work is partially supported by NSC-94-2213-E-260-024.     

A methodical procedure for designing consumer oriented on-demand IT service propositions

IT providers are increasingly facing the challenge to adapt their previously resource oriented service portfolios in order to offer their customers services which explicitly support business processes. Such customer centric service propositions, however, seem to contradict the demand for standardized and automated operational IT processes more than traditional IT service offers, as they are even more subject to customer individual reengineering efforts due to permanently changing business requirements. In order to reconcile increased efficiency in operational processes and effectiveness in consumer oriented service propositions, we propose (1) to predefine all service propositions in consideration of both consumer oriented commitments and operational processes, and (2) to allow for standardized customization by offering a selection of complementary service propositions that extend commitments regarding customer oriented functionality and performance. Such service propositions are aligned with a company’s entities such as workplaces. Thereby the customer organization is enabled to trace, control and adjust commitments, value and expenses of IT services per entity in its business. We introduce a procedural model for designing and on-demand requesting this kind of service propositions, and we illustrate the model’s application and impact by examples taken from two large projects with an associated IT provider.     

Spatio-Temporal Tube data representation and Kernel design for SVM-based video object retrieval system

In this article, we propose a new video object retrieval system. Our approach is based on a Spatio-Temporal data representation, a dedicated kernel design and a statistical learning toolbox for video object recognition and retrieval. Using state-of-the-art video object detection algorithms (for faces or cars, for example) we segment video object tracks from real movies video shots. We then extract, from these tracks, sets of spatio-temporally coherent features that we call Spatio-Temporal Tubes. To compare these complex tube objects, we design a Spatio-Temporal Tube Kernel (STTK) function. Based on this kernel similarity we present both supervised and active learning strategies embedded in Support Vector Machine framework. Additionally, we propose a multi-class classification framework dealing with unbalanced data. Our approach is successfully evaluated on two real movies databases, the french movie “L’esquive” and episodes from “Buffy, the Vampire Slayer” TV series. Our method is also tested on a car database (from real movies) and shows promising results for car identification task.     

Mining purchasing decision rules from service encounter data of retail chain stores

In this explorative research, we aim to find the most important service experience variables that determine customer purchasing decision and the clerks’ influence on customers’ purchases. This study was conducted as a case study of a children’s apparel company, denoted Company L, which has 243 retail stores. Company L has implemented Point of Sale (POS) systems in its retail stores, and would like to know what functions could be added to induce storefront employees to deliver better customer service. We, therefore, focus on observing the services provided by storefront employees and their reflection on a customer’s purchasing decision in a retail store. The study generated decision trees via Weka, a data mining open source software platform, to analyze multiple data sources to (1) understand what makes a good service experience for a customer, (2) get explicit knowledge from service encounter information, and (3) externalize the tacit knowledge of storefront service experiences. These findings can be used to improve Company L’s POS system to guide storefront employees to learn from trained decision rules. Moreover, the company can internalize service experience knowledge by aggregating learned rules from the company’s retail stores.     

Network Development Under a Strict Self-Financing Constraint

This paper offers a simplified model in which an agency is in charge of investing in road capacity and maintain it but cannot use the capital market so that the only sources of funds are the toll revenues. We refer to this requirement as the ’strict self-financing constraint’ to distinguish it from the traditional form for self-financing that allows borrowing from the capital market. Two small test problems are analysed: the one link problem and the problem of two parallel links with one link untolled. The numerical illustrations show the cost of the strict self-financing constraint as a function of the importance of the initial infrastructure stock, the rate of growth of demand, the price elasticity of demand and the flexibility in the pricing instruments.     

Examining the influence of service quality and secondary influence on the behavioural intention to change internet service provider

Although the broadband market has considerably matured, follow-up research on the continued adoption of broadband is yet to be conducted. The aim of this research was therefore to investigate empirically the influence of service quality and secondary influence on consumers’ behavioural intention to change from their existing internet service provider (ISP) to an alternative service provider. The investigation focuses upon broadband household consumers within the UK. The study was conducted using a postal survey; a self-administered questionnaire was sent to 1600 households and a total of 358 completed replies were obtained. The results suggest that both service quality and secondary influence were significantly correlated to consumers’ behavioural intentions to change ISP. The implications of these findings are presented, followed by a discussion of the limitations of this research and future research directions.     

Towards capacity and profit optimization of video-on-demand services in a peer-assisted IPTV platform

This paper studies the conditions under which peer-to-peer (P2P) technology may be beneficial in providing IPTV services over typical network architectures. It has three major contributions. First, we contrast two network models used to study the performance of such a system: a commonly used logical “Internet as a cloud” model and a “physical” model that reflects the characteristics of the underlying network. Specifically, we show that the cloud model overlooks important architectural aspects of the network and may drastically overstate the benefits of P2P technology. Second, we propose an algorithm called Zebra that pre-stripes content across multiple peers during idle hours to speed up P2P content delivery in an IPTV environment with limited upload bandwidth. We also perform simulations to measure Zebra’s effectiveness at reducing load on the content server during peak hours. Third, we provide a cost-benefit analysis of P2P video content delivery, focusing on the profit trade-offs for different pricing/incentive models rather than purely on capacity maximization. In particular, we find that under high volume of video demand, a P2P built-in incentive model performs better than any other model, while the conventional no-P2P model generates more profits when the request rate is low. The flat-reward model generally falls in between the usage-based model and the built-in model in terms of profitability except for low request rates. We also find that built-in and flat-reward models are more profitable than the usage-based model for a wide range of subscriber community sizes. Funding for J. Rahe’s research has been provided by AT&T Labs, the State of California under the MICR Oprogram, and by the Toshiba Corporation. Zhen Xiao is partially supported by China MOST project (2006BAH02A10).     

Frame rate as a QoS parameter and its influence on speech perception

The preservation of QoS for multimedia traffic through a data network is a difficult problem. We focus our attention on video frame rate and study its influence on speech perception. When sound and picture are discrepant (e.g., acoustic ‘ba’ combined with visual ‘ga’), subjects perceive a different sound (such as ‘da’). This phenomenon is known as the McGurk effect. In this paper, the influence of degraded video frame rate on speech perception is studied. It is shown that, when frame rate decreases, correct hearing is improved for discrepant stimuli and is degraded for congruent (voice and picture are the same) stimuli. Furthermore, we studied the case where lip closure was always captured by the synchronization of sampling time and lip position. In this case, frame rate has little effect on mishearing for congruent stimuli. For discrepant stimuli, mishearing is decreased with degraded frame rate. These results indicate that the stiff motion of lips resulting from low frame rate cannot give enough labial information for speech perception. In addition, the effect of delaying the picture to correct for low frame rate was studied. The results, however, were not as definitive as expected, because of compound effects related to the synchronization of sound and picture. Finally, we inspected the still pictures of normal Japanese speech and determined a lower limit of frame rate from the view point of assisting hearing.     

Design and analysis of a video-on-demand server

The availability of high-speed networks, fast computers and improved storage technology is stimulating interest in the development of video on-demand services that provide facilities similar to a video cassette player (VCP). In this paper, we present a design of a video-on-demand (VOD) server, capable of supporting a large number of video requests with complete functionality of a remote control (as used in VCPs), for each request. In the proposed design, we have used an interleaved storage method with constrained allocation of video and audio blocks on the disk to provide continuous retrieval. Our storage scheme interleaves a movie with itself (while satisfying the constraints on video and audio block allocation. This approach minimizes the starting delay and the buffer requirement at the user end, while ensuring a jitter-free display for every request. In order to minimize the starting delay and to support more non-concurrent requests, we have proposed the use of multiple disks for the same movie. Since a disk needs to hold only one movie, an array of inexpensive disks can be used, which reduces the overall cost of the proposed system. A scheme supported by our disk storage method to provide all the functions of a remote control such as “fast-forwarding”, “rewinding” (with play “on” or “off”), “pause” and “play” has also been discussed. This scheme handles a user request independent of others and satisfies it without degrading the quality of service to other users. The server design presented in this paper achieves the multiple goals of high disk utilization, global buffer optimization, cost-effectiveness and high-quality service to the users.     

Merging video streams in a multimedia storage server: complexity and heuristics

Due to recent advances in network, storage and data compression technologies, video-on-demand (VOD) service has become economically feasible. It is a challenging task to design a video storage server that can efficiently service a large number of concurrent requests on demand. One approach to accomplishing this task is to reduce the I/O demand to the VOD server through data- and resource-sharing techniques. One form of data sharing is the stream-merging approach proposed in [5]. In this paper, we formalize a static version of the stream-merging problem, derive an upper bound on the I/O demand of static stream merging, and propose efficient heuristic algorithms for both static and dynamic versions of the stream-merging problem.     

Do moods affect programmers’ debug performance?

There is much research that shows people’s mood can affect their activities. This paper argues that this also applies to programmers, especially their debugging. Literature-based framework is presented linking programming with various cognitive activities as well as linking cognitive activities with moods. Further, the effect of mood on debugging was tested in two experiments. In the first experiment, programmers (n = 72) saw short movie clips selected for their ability to provoke specific moods. Afterward, they completed a debugging test. Results showed the video clips had a significant effect on programmers’ debugging performance; especially, there was a significant difference after watching low- and high-arousal-evoking video clips. In the second experiment, programmers’ mood was manipulated by asking participants (n = 19) to dry run algorithms for at least 16 min. They performed some physical exercises before continuing dry running algorithms again. The results showed a significant increase in arousal and valence that coincided with an improvement in programmers’ task performance after the physical exercises. Together, this suggests that programmers’ moods influence some programming tasks such as debugging.