Multi-channel P2P Research Articles

Enrich multi-channel P2P VoD streaming based on dynamic replication strategy

Peer-to-Peer Video-on-Demand (VoD) is a favorable solution which compromises thousands of videos to millions of users with completeinteractive video watching stream. Most of the profitable P2P streaming groupsPPLive, PPStream and UUSee have announced a multi-channel P2P VoD system that approvals user to view extra one channel at a time. The present multiple channel P2P VoD system resonant a video at a low streaming rate due to the channel resource inequity and channel churn. In order to growth the streaming capacity, this paper highlights completely different effective helpers created resource balancing scheme that actively recognizes the supply-and-demand inequity in multiple channels. Moreover, peers in an extra channel help its unused bandwidth resources to peers in a shortage channel that minimizes the server bandwidth consumption. To provide a desired replication ratio for optimal caching, it develops a dynamic replication strategy that optimally tunes the number of replicas based on dynamic popularity in a distributed and dynamic routine. This work accurately forecasts the varying popularity over time using Auto-Regressive Integrated Moving Average (ARIMA) model, an effective time-series forecasting technique that supports dynamic environment. Experimental assessment displays that the offered dynamic replication strategy which should achieves high streaming capacity under reduced server workload when associated to existing replication algorithms.

ReDePoly: reducing delays in multi-channel P2P live streaming systems using distributed intelligence

Video streaming over peer-to-peer (P2P) networks is a promising approach for scalable IPTV and online games. Distributing high-quality videos among users who employ numerous channels in multi-channel P2P live streaming systems, however, still suffers from many challenges arisen from the network size and dynamicity, channel resource imbalance, and bandwidth limitations. Other challenges also exist, including instability of peers, the peers’ low participation, large startup and playback delays, low quality of received video, and resource insufficiency in unpopular channels. In this paper, ReDePoly, with some levels of distributed intelligence, is proposed to reduce the bootstrapping delay and to increase the service quality. The key idea is to substitute agents with bootstrapping peers in the channels to model user behaviors and to share the aggregated knowledge among the agents to disseminate the learned models. Accordingly, the agents dynamically predict the behavior of participating peers to pre-assign them to the predicted channels and to parallelize the threads related to viewing behavior and channel switching. In case of congested channels, the new agents are distilled to mitigate the agent responsibilities. Simulation results show that the proposed approach outperforms the other existing methods regarding the channel switching delay, recovery delay, and quality of service.

Tracker Assisted Peer Scheduling Strategy in Multi-channel P2P VoD Streaming

Peer-to-Peer Video-on-Demand (VoD) is a capable solution which offers thousands of videos to millions of users with complete interactive watching. Most of the commercial P2P streaming deployments PPLive, PPStream, UUSee have introduced a multi-channel P2P VoD system that allows user to view more than one channel at a time. Recent research studies have proposed a cross channel resource sharing algorithms to utilize the individual peer resources effectively, including bandwidth and cache capacity by enabling crosschannel cooperation among multiple channels. However, current multiple channel P2P VoD system deliver a video at a low streaming rate due to the channel resource imbalance and channel churn. In order to improve the streaming capacity, this paper proposes different effective helpers based resource balancing scheme that actively identifies the supply-anddemand imbalance in multiple channels. Furthermore, peers in a surplus channel serve its unused bandwidth resources to peers in a deficit channel that minimizes the server bandwidth consumption. This approach proposes a tracker assisted peer scheduling policy that effectively schedules the different chunks within each video in the process of fetching and serving chunks without impairing the streaming quality. Experimental evaluation shows that the proposed tracker assisted scheduling strategy achieves high streaming capacity under reduced server workload and improves streaming quality when compared to existing algorithms.

The Streaming Capacity of Sparsely Connected P2P Systems With Distributed Control

Peer-to-Peer (P2P) streaming technologies can take advantage of the upload capacity of clients, and hence can scale to large content distribution networks with lower cost. A fundamental question for P2P streaming systems is the maximum streaming rate that all users can sustain. Prior works have studied the optimal streaming rate for a complete network, where every peer is assumed to communicate with all other peers. This is however an impractical assumption in real systems. In this paper, we are interested in the achievable streaming rate when each peer can only connect to a small number of neighbors. We show that even with a random peer selection algorithm and uniform rate allocation, as long as each peer maintains Ω(logN) downstream neighbors, where N is the total number of peers in the system, the system can asymptotically achieve a streaming rate that is close to the optimal streaming rate of a complete network.We then extend our analysis to multi-channel P2P networks, and we study the scenario where “helpers” from channels with excessive upload capacity can help peers in channels with insufficient upload capacity. We show that by letting each peer select Ω(logN) neighbors randomly from either the peers in the same channel or from the helpers, we can achieve a close-to-optimal streaming capacity region. Simulation results are provided to verify our analysis.

Improving Streaming Capacity in P2P Live Streaming Systems via Resource Sharing

Peer-to-Peer (P2P) streaming systems provide a large number of channels to users. The streaming capacity for a channel is defined as the maximum streaming rate that can be received by every user in the channel. In the this paper, we study the streaming capacity problem in both tree-based and mesh-based P2P live streaming systems. In tree-based multi-channel P2P live streaming systems, we propose a cross-channel resource sharing approach to improve the streaming capacity. We employ cross-channel helpers to establish the cross-channel overlay links, with which the unused upload bandwidths in a channel can be utilized to help the bandwidth-deficient peers in another channel, thus improving the streaming capacity. In meshed-based P2P live streaming systems, we formulate the streaming capacity problem into a resource optimization problem. By solving the resource optimization problem, we can optimally allocate the link rates for each peer to improve the streaming capacity. Through simulations, we demonstrate that the proposed resource sharing approaches can significantly improve the streaming capacity compared to the scheme without resource sharing.

Adaptive resource management for P2P live streaming systems

Peer-to-Peer (P2P) has become a popular live streaming delivery technology owing to its scalability and low cost. P2P streaming systems often employ multi-channels to deliver streaming to users simultaneously, which leads to a great challenge of allocating server resources among these channels appropriately. Most existing P2P systems resort to over-allocating server resources to different channels, which results in low-efficiency and high-cost. To allocate server resources to different channels efficiently, we propose a dynamic resource allocation algorithm based on a streaming quality model for P2P live streaming systems. This algorithm can improve the channel streaming quality for multi-channel P2P live streaming system and also guarantees the streaming quality of the channels under extreme Internet conditions. In an experiment, the proposed algorithm is validated by the trace data.

Modeling and analysis of multi-channel P2P VoD systems

Peer-to-Peer (P2P) technology has recently become a tremendously attractive solution to offload servers in large-scale Video-on-Demand (VoD) applications by utilizing the upload capability of participating peers. However, the alleviation of server load may not be very effective in the multi-channel P2P VoD systems due to the intra-channel and inter-channel upload bandwidth imbalances. In this paper, we evaluate the impact of these bandwidth imbalances on the system performance through two analytically models: a queuing network model which describes the whole system, and a stage-based model which describes a single channel. Our models capture several aspects of peer behavior, such as participating in the system, sojourning in a channel, downloading and uploading the content, wandering around channels and leaving the system. We apply our models to three potential P2P VoD designs: Naive bandwidth Allocation Design (NAD), Independent-channel Chunk-aware bandwidth Allocation design (ICA) and Cross-channel Chunk-aware bandwidth Allocation design (CCA). By developing an asymptotic theory to provide theoretical results, we analytically show that ICA can perform better than NAD, and CCA can perform best of the three designs in terms of server load. We also present optimization problems and simple heuristic strategies for ICA and CCA. Our analytical results are validated by extensive simulations.

Improving Streaming Capacity in Multi-Channel P2P VoD Systems via Intra-Channel and Cross-Channel Resource Allocation

Multi-channel Peer-to-Peer (P2P) Video-on-Demand (VoD) systems can be categorized intoindependent-channelP2P VoD systems andcorrelated-channelP2P VoD systems. Streaming capacity for a channel is defined as the maximal streaming rate that can be received by every user of the channel. In this paper, we study the streaming capacity problem in multi-channel P2P VoD systems. In an independent-channel P2P VoD system, there is no resource correlation among channels. Therefore, we can find the average streaming capacity for the independent-channel P2P VoD system by finding the streaming capacity for each individual channel, respectively. We propose a distributed algorithm to solve the streaming capacity problem for a single channel in an independent-channel P2P VoD system. The average streaming capacity for a correlated-channel P2P VoD system depends on both the intra-channel and cross-channel resource allocation. To better utilize the cross-channel resources, we first optimize the server upload allocation among channels to maximize the average streaming capacity and then propose cross-channel helpers to enable cross-channel sharing of peer upload bandwidths. We demonstrate in the simulations that the correlated-channel P2P VoD systems with both intra-channel and cross-channel resource allocation can obtain a higher average streaming capacity compared to the independent-channel P2P VoD systems with only intra-channel resource allocation.

Modeling and Analysis of Multichannel P2P Live Video Systems

In recent years, there have been several large-scale deployments of P2P live video systems. Existing and future P2P live video systems will offer a large number of channels, with users switching frequently among the channels. In this paper, we develop infinite-server queueing network models to analytically study the performance of multichannel P2P live video systems. Our models capture essential aspects of multichannel video systems, including peer channel switching, peer churn, peer bandwidth heterogeneity, and Zipf-like channel popularity. We apply the queueing network models to two P2P streaming designs: the isolated channel design (ISO) and the View-Upload Decoupling (VUD) design. For both of these designs, we develop efficient algorithms to calculate critical performance measures, develop an asymptotic theory to provide closed-form results when the number of peers approaches infinity, and derive near-optimal provisioning rules for assigning peers to groups in VUD. We use the analytical results to compare VUD with ISO. We show that VUD design generally performs significantly better, particularly for systems with heterogeneous channel popularities and streaming rates.

Redesigning multi-channel P2P live video systems with View-Upload Decoupling

In current multi-channel P2P live video systems, there are several fundamental performance problems including exceedingly-large channel switching delays, long playback lags, and poor performance for less popular channels. These performance problems primarily stem from two intrinsic characteristics of multi-channel P2P video systems: channel churn and channel-resource imbalance. In this paper, we propose a radically different cross-channel P2P streaming framework, called View-Upload Decoupling (VUD). VUD strictly decouples peer downloading from uploading, bringing stability to multi-channel systems and enabling cross-channel resource sharing. We propose a set of peer assignment and bandwidth allocation algorithms to properly provision bandwidth among channels, and introduce substream-swarming to reduce the bandwidth overhead. We evaluate the performance of VUD via extensive simulations as well with a PlanetLab implementation. Our simulation and PlanetLab results show that VUD is resilient to channel churn, and achieves lower switching delay and better streaming quality. In particular, the streaming quality of small channels is greatly improved.