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Predictionbased prefetching to support VCR-like operations in gossipbased P2P VoD systems
- In Proceedings of the 15th International Conference on Parallel and Distributed Systems (ICPADS
, 2009
"... streaming systems is challenging. The uncertainty of frequent VCR operations makes it difficult to provide high quality realtime streaming services over distributed self-organized P2P overlay networks. Recently, prefetching has emerged as a promising approach to smooth the streaming quality. However ..."
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streaming systems is challenging. The uncertainty of frequent VCR operations makes it difficult to provide high quality realtime streaming services over distributed self-organized P2P overlay networks. Recently, prefetching has emerged as a promising approach to smooth the streaming quality. However, how to efficiently and effectively prefetch suitable segments is still an open issue. In this paper, we propose PREP, a PREdiction-based Prefetching scheme to support VCR-like operations over gossip-based P2P on-demand streaming systems. By employing the reinforcement learning technique, PREP transforms users ’ streaming service procedure into a set of abstract states and presents an online prediction model to predict a user’s VCR behavior via analyzing the large volumes of user viewing logs collected on the tracker. We further present a distributed data scheduling algorithm to proactively fetch segments according to the predicted VCR behavior. Moreover, PREP takes advantage of the inherent peer collaboration of gossip protocol to optimize the response latency. Through comprehensive simulations, we demonstrate the efficiency of PREP by gaining the accumulated hit ratio close to 75 % while reducing the response latency close to 70 % with only less than 15 % extra stress on the server side. I.
Coarse-grained Cloud Synchronization Mechanism Design May Lead to Severe Traffic Overuse
- Journal of Tsinghua Science and Technology
, 2013
"... Abstract: In recent years, cloud sync(hronization) services such as GoogleDrive and Dropbox have provided Internet users with convenient and reliable data storing/sharing functionality. The cloud synchronization mechanism (in particular, how to deliver the user-side data updates to the cloud) plays ..."
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Abstract: In recent years, cloud sync(hronization) services such as GoogleDrive and Dropbox have provided Internet users with convenient and reliable data storing/sharing functionality. The cloud synchronization mechanism (in particular, how to deliver the user-side data updates to the cloud) plays a critical role in cloud sync services because it greatly affects the cloud operation cost (in terms of sync traffic) and user experience (in terms of sync delay). By comprehensively measuring tens of popular cloud sync services, we find that their cloud sync mechanisms differ greatly in sync performance and design granularity. Quite surprisingly, some very popular services (like GoogleDrive and 115 SyncDisk) utilize a quite coarse-grained cloud sync mechanism that may lead to severe traffic overuse. For example, updating 1-MB data may sometimes result in 260-MB sync traffic. In this paper, we conduct a comparative study of various cloud sync mechanisms by analyzing their respective pros and cons under different situations, unravel the pathological processes for their traffic overuse problems, and finally provide insights/solutions for better choosing/designing a cloud sync service. Key words: cloud storage; data synchronization; operation cost; user experience; design granularity
APEX: A Personalization Framework to Improve Quality of Experience for DVD-like Functions in P2P VoD Applications
"... Abstract — The requirement for supporting DVD-like functions raises new challenges to the design of P2P VoD systems. The uncertainty of frequent user DVD-like interactivity makes it difficult to ensure user perceived Quality of Experience (QoE) for real-time streaming services over distributed self- ..."
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Abstract — The requirement for supporting DVD-like functions raises new challenges to the design of P2P VoD systems. The uncertainty of frequent user DVD-like interactivity makes it difficult to ensure user perceived Quality of Experience (QoE) for real-time streaming services over distributed self-organized P2P overlay networks. Most existing solutions are based on the unreasonable assumption that all the users in P2P VoD systems have the same preference. Few attention has been paid to personalization, which accommodates the differences between users. In this paper, we present a video model which characterizes the personalization information for users ’ contents and preferences. Based on this model, we develop APEX, a practical personalization framework for P2P VoD applications. APEX makes the personalization practical by using a hybrid architecture which leverages the offline pattern mining on the server side and online collaborative filtering on the peer side. Furthermore, APEX helps peers to personalize navigation, prefetching and membership management, aiming at improving QoE for DVD-like functions by reducing response latency and optimizing content sharing. Both theoretical analysis and comprehensive simulations show that APEX outperforms most existing schemes in terms of accumulated hit ratio, response latency, and searching efficiency. I.
Architecture for Cooperative Prefetching in P2P Video-on- Demand System
"... Most P2P VoD schemes focused on service architectures and overlays optimization without considering segments rarity and the performance of prefetching strategies. As a result, they cannot better support VCRoriented service in heterogeneous environment having clients using free VCR controls. Despite ..."
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Most P2P VoD schemes focused on service architectures and overlays optimization without considering segments rarity and the performance of prefetching strategies. As a result, they cannot better support VCRoriented service in heterogeneous environment having clients using free VCR controls. Despite the remarkable popularity in VoD systems, there exist no prior work that studies the performance gap between different prefetching strategies. In this paper, we analyze and understand the performance of different prefetching strategies. Our analytical characterization brings us not only a better understanding of several fundamental tradeoffs in prefetching strategies, but also important insights on the design of P2P VoD system. On the basis of this analysis, we finally proposed a cooperative prefetching strategy called “cooching”. In this strategy, the requested segments in VCR interactivities are prefetched into session beforehand using the information collected through gossips. We evaluate our strategy through extensive simulations. The results indicate that the proposed strategy outperforms the existing prefetching mechanisms.
An Adapted Multi-way Tree with Holder-chain for Large Scale P2P VoD System
"... Abstract. Peer-to-Peer Video-on-demand (P2P VoD) systems have attracted great interest in research community. Many hybrid methods have been proposed to provide high-quality video-on-demand services to support VCR-like operations, which can scale to large population of users. In this paper, we propos ..."
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Abstract. Peer-to-Peer Video-on-demand (P2P VoD) systems have attracted great interest in research community. Many hybrid methods have been proposed to provide high-quality video-on-demand services to support VCR-like operations, which can scale to large population of users. In this paper, we propose a hybrid overlay DONet-AMT, which combines DONet and an adapted multi-way tree. DONet is applied to distribute the video segments efficiently. The adapted multi-way tree consists of two layers: a complete multi-way tree and holder-chain, which can support efficient VCR-like operations. Our scheme can ensure that every video segment has k (typically k≥4) replicas at least when the number of peers is large enough, and thus guarantees high data availability. Finally, the per-formance analysis shows that DONet-AMT can provide better VoD services than DONet-VoD.
InstantLeap: an architecture for fast neighbor discovery in large-scale P2P VoD streaming
, 2010
"... In large-scale peer-to-peer (P2P) video-on-demand (VoD) streaming applications, a fundamental challenge is to quickly locate new supplying peers when-ever a VCR command is issued, in order to achieve smooth viewing experiences. For many existing commercial systems which use tracker servers for neig ..."
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In large-scale peer-to-peer (P2P) video-on-demand (VoD) streaming applications, a fundamental challenge is to quickly locate new supplying peers when-ever a VCR command is issued, in order to achieve smooth viewing experiences. For many existing commercial systems which use tracker servers for neighbor discovery, the increasing scale of P2P VoD systems has overloaded the dedicated servers to the point where they cannot accurately identify the suppliers with the desired content and band-width. To avoid overloading the servers and achieve instant neighbor discovery over the self-organizing P2P overlay, we design a novel method of organizing peers watching a video. The method features a light-weight indexing architecture to support efficient streaming and fast neighbor discovery at the same time. InstantLeap separates the neighbors at each peer into a streaming neighbor list and a shortcut neighbor list, for streaming and neighbor discovery respectively, which are maintained loosely but effectively based on random neighbor list exchanges. Our analysis shows that InstantLeap achieves an O(1) neighbor discovery efficiency upon any playback ‘‘leap’ ’ across the media stream in streaming overlays of any size, and low messaging costs for overlay maintenance upon peer join, departure, and VCR operations. We also verify our design with large-scale simulation studies of dynamic P2P VoD systems based on real-world settings.
Probabilistic Seeking Prediction in P2P VoD Systems
"... Abstract. In P2P VoD streaming systems, user behavior modeling is critical to help optimise user experience as well as system throughput. However, it still remains a challenging task due to the dynamic characteristics of user viewing behavior. In this paper, we consider the problem of user seeking p ..."
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Abstract. In P2P VoD streaming systems, user behavior modeling is critical to help optimise user experience as well as system throughput. However, it still remains a challenging task due to the dynamic characteristics of user viewing behavior. In this paper, we consider the problem of user seeking prediction which is to predict the user’s next seeking position so that the system can proactively make response. We present a novel method for solving this problem. In our method, frequent sequential patterns mining is first performed to extract abstract states which are not overlapped and cover the whole video file altogether. After mapping the raw training dataset to state transitions according to the abstract states, we use a simpel probabilistic contingency table to build the prediction model. We design an experiment on the synthetic P2P VoD dataset. The results demonstrate the effectiveness of our method.
Efficient Content Discovery in P2P-Multimedia System
"... In this paper, we are interested to provide fast content discovery in P2P multimedia systems. To constructs AVL tree for best efficient search peer in P2P multimedia system. Gossip protocol to exchange information to its neighbouring peers and which chunks to be downloaded from which peer. An issue ..."
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In this paper, we are interested to provide fast content discovery in P2P multimedia systems. To constructs AVL tree for best efficient search peer in P2P multimedia system. Gossip protocol to exchange information to its neighbouring peers and which chunks to be downloaded from which peer. An issue in peer management manages the scheduling algorithm from which peer. The analysis is made on hybrid content discovery in P2P multimedia system and how the chunk should be distributed over the peer and also how the request chunk picking from the peers in structured networks to be focused and issues are stated. The comparative study is based on the existing features to be undertaken with structure and unstructured networks for fast and efficient content discovery in P2P Multimedia system.
Extracting viewer interests for automated bookmarking in video-on-demand services
, 2013
"... Abstract Video-on-demand (VoD) services have become popular on the Internet in recent years. In VoD, it is challeng-ing to support the VCR functionality, especially the jumps, while maintaining a smooth streaming quality. Previous stud-ies propose to solve this problem by predicting the jump tar-get ..."
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Abstract Video-on-demand (VoD) services have become popular on the Internet in recent years. In VoD, it is challeng-ing to support the VCR functionality, especially the jumps, while maintaining a smooth streaming quality. Previous stud-ies propose to solve this problem by predicting the jump tar-get locations and prefetching the contents. However, through our analysis on traces from a real-world VoD service, we find that it would be fundamentally difficult to improve a viewer’s VCR experience by simply predicting his future jumps, while ignoring the intentions behind these jumps. Instead of the prediction-based approach, in this paper, we seek to support the VCR functionality by bookmark-ing the videos. There are two key techniques in our pro-posed methodology. First, we infer and differentiate view-ers ’ intentions in VCR jumps by decomposing the inter-seek times, using an expectation-maximization (EM) algo-rithm, and combine the decomposed inter-seek times with the VCR jumps to compute a numerical interest score for each video segment. Second, based on the interest scores, we pro-pose an automated video bookmarking algorithm. The algo-rithm employs the time-series change detection techniques of CUSUM and MB-GT, and bookmarks videos by detecting the abrupt changes on their interest score sequences. We evaluate our proposed techniques using real-world VoD traces from dozens of videos. Experimental results suggest that with our methods, viewers ’ interests within a video can be precisely extracted, and we can position bookmarks on the video’s
