This paper presents DONet, a data-driven overlay network for live media streaming. The core operations in DONet are very simple: every node periodically exchanges data availability information with a set of partners, and retrieves unavailable data from one or more partners, or supplies available data to partners. We emphasize three salient features of this data-driven design: 1) easy to implement, as it does not have to construct and maintain a complex global structure; 2) efficient, as data forwarding is dynamically determined according to data availability while not restricted by specific directions; and 3) robust and resilient, as the partnerships enable adaptive and quick switching among multi-suppliers. We show through analysis that DONet is scalable with bounded delay. We also address a set of practical challenges for realizing DONet, and propose an efficient member and partnership management algorithm, together with an intelligent scheduling algorithm that achieves real-time and continuous distribution of streaming contents. We have extensively evaluated the performance of DONet over the PlanetLab. Our experiments, involving almost all the active PlanetLab nodes, demonstrate that DONet achieves quite good streaming quality even under formidable network conditions. Moreover, its control overhead and transmission delay are both kept at low levels. An Internet-based DONet implementation, called CoolStreaming v.0.9, was released on May 30, 2004, which has attracted over 30000 distinct users with more than 4000 simultaneously being online at some peak times. We discuss the key issues toward designing CoolStreaming in this paper, and present several interesting observations from these large-scale tests; in particular, the larger the overlay size, the better the streaming quality it can deliver.

/pdf/coolstreaming-donet-a-data-driven-overlay-network-for-peer-1a8385c85n.pdf

CoolStreaming/DONet: a data-driven overlay network for peer-to-peer live media streaming

This paper presents DONet, a Data-driven Overlay Network for live media streaming. The core operations in DONet are very simple: every node periodically exchanges data availability information with a set of partners, and retrieves unavailable data from one or more partners, or supplies available data to partners. We emphasize three salient features of this data-driven design: 1) easy to implement, as it does not have to construct and maintain a complex global structure; 2) efficient, as data forwarding is dynamically determined according to data availability while not restricted by specific directions; and 3) robust and resilient, as the partnerships enable adaptive and quick switching among multi-suppliers. We show through analysis that DONet is scalable with bounded delay. We also address a set of practical challenges for realizing DONet, and propose an efficient memberand partnership management algorithm, together with an intelligent scheduling algorithm that achieves real-time and continuous distribution of streaming contents. We have extensively evaluated the performance of DONet over the PlanetLab. Our experiments, involving almost all the active PlanetLab nodes, demonstrate that DONet achieves quite good streaming quality even under formidable network conditions. Moreover, its control overhead and transmission delay are both kept at low levels. An Internet-based DONet implementation, called CoolStreaming v.0.9, was released on May 30, 2004, which has attracted over 30000 distinct users with more than 4000 simultaneously being online at some peak times. We discuss the key issues toward designing CoolStreaming in this paper, and present several interesting observations from these large-scale tests; in particular, the larger the overlay size, the better the streaming quality it can deliver.

/pdf/coolstreaming-donet-a-data-driven-overlay-network-for-22lb993r34.pdf

CoolStreaming/DONet: A Data-Driven Overlay Network for Efficient Live Media Streaming

DONet: A Data-Driven Overlay Network For Efficient Live Media Streaming

The sensing capabilities of networked sensors are affected by environmental factors in real deployment and it is imperative to have practical considerations at the design stage in order to anticipate this sensing behavior. We investigate the coverage issues in wireless sensor networks based on probabilistic coverage and propose a distributed probabilistic coverage algorithm (PCA) to evaluate the degree of confidence in detection probability provided by a randomly deployed sensor network. The probabilistic approach is a deviation from the idealistic assumption of uniform circular disc for sensing coverage used in the binary detection model. Simulation results show that area coverage calculated by using PCA is more accurate than the idealistic binary detection model

Probabilistic coverage in wireless sensor networks

Current disk prefetch policies in major operating systems track access patterns at the level of the file abstraction. While this is useful for exploiting application-level access patterns, file-level prefetching cannot realize the full performance improvements achievable by prefetching. There are two reasons for this. First, certain prefetch opportunities can only be detected by knowing the data layout on disk, such as the contiguous layout of file metadata or data from multiple files. Second, nonsequential access of disk data (requiring disk head movement) is much slower than sequential access, and the penalty for mis-prefetching a 'random' block, relative to that of a sequential block, is correspondingly more costly.

To overcome the inherent limitations of prefetching at the logical file level, we propose to perform prefetching directly at the level of disk layout, and in a portable way. Our technique, called DiskSeen, is intended to be supplementary to, and to work synergistically with, file-level prefetch policies, if present. DiskSeen tracks the locations and access times of disk blocks, and based on analysis of their temporal and spatial relationships, seeks to improve the sequentiality of disk accesses and overall prefetching performance.

Our implementation of the DiskSeen scheme in the Linux 2.6 kernel shows that it can significantly improve the effectiveness of prefetching, reducing execution times by 20%-53% for micro-benchmarks and real applications such as grep, CVS, and TPC-H.

/pdf/diskseen-exploiting-disk-layout-and-access-history-to-2f3qxxpmp4.pdf

DiskSeen: exploiting disk layout and access history to enhance I/O prefetch

The diverse server, client, and unique file object types used today slow Web performance. Caching alone offers limited performance relief because it cannot handle many different file types easily. One solution combines caching with Web prefetching: obtaining the Web data a client might need from data about that client's past surfing activity. The prediction by partial match model, for example, makes prefetching decisions by reviewing URLs clients have accessed on a particular server, then structuring them in a Markov predictor tree. The authors propose a variation of this model that builds common surfing patterns and regularities into the tree.

A popularity-based prediction model for Web prefetching

While current peer-to-peer (P2P) systems facilitate static file sharing, newly developed applications demand that P2P systems be able to manage dynamically changing files. Maintaining consistency between frequently updated files and their replicas is a fundamental reliability requirement for a P2P system. In this paper, we present SCOPE, a structured P2P system supporting consistency among a large number of replicas. By building a replica-partition-tree (RPT) for each key, SCOPE keeps track of the locations of replicas and then propagates update notifications. Our theoretical analyses and experimental results demonstrate that SCOPE can effectively maintain replica consistency while preventing hot spot and node-failure problems. Its efficiency in maintenance and failure-recovery is particularly attractive to the deployment of large-scale P2P systems.

/pdf/scope-scalable-consistency-maintenance-in-structured-p2p-5eeo7qpp2g.pdf

SCOPE: scalable consistency maintenance in structured P2P systems

The demand of delivering streaming media content in the Internet has become increasingly high for scientific, educational, and commercial applications. Three representative technologies have been developed for this purpose, each of which has its merits and serious limitations. Infrastructure-based CDNs with dedicated network bandwidths and powerful media replicas can provide high quality streaming services but at a high cost. Server-based proxies are cost-effective but not scalable due to the limited proxy capacity and its centralized control. Client-based P2P networks are scalable but do not guarantee high quality streaming service due to the transient nature of peers. To address these limitations, we present a novel and efficient design of a scalable and reliable media proxy system supported by P2P networks. This system is called PROP abbreviated from our technical theme of "collaborating and coordinating PROxy and its P2P clients". Our objective is to address both scalability and reliability issues of streaming media delivery in a cost-effective way. In the PROP system, the clients' machines in an intranet are self-organized into a structured P2P system to provide a large media storage and to actively participate in the streaming media delivery, where the proxy is also embedded as an important member to ensure quality of streaming service. The coordination and collaboration in the system are efficiently conducted by our P2P management structure and replacement policies. We have comparatively evaluated our system by trace-driven simulations with synthetic workloads and with a real-life workload trace extracted from the media server logs in an enterprise network. The results show that our design significantly improves the quality of media streaming and the system scalability.

/pdf/prop-a-scalable-and-reliable-p2p-assisted-proxy-streaming-13lvesf5lg.pdf

PROP: a scalable and reliable P2P assisted proxy streaming system

Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Probabilistic coverage is an appropriate approach to balancing the conflicting design requirements of monitoring applications. Under probabilistic coverage, we present an analytical model to analyze object detection quality with respect to different network conditions and sensor scheduling schemes. Our analytical model facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Applying the model to real sensor networks, we design a set of sensing scheduling protocols to achieve targeted object detection quality while minimizing power consumption. The correctness of our model and the effectiveness of the proposed protocols are validated through extensive simulation experiments.

/pdf/analyzing-object-detection-quality-under-probabilistic-18vzrm7cd9.pdf

Analyzing object detection quality under probabilistic coverage in sensor networks

Sensor networks are used for a wide range of object tracking applications, such as vehicle tracking in military surveillance and wild animal tracking in habitat monitoring [1]. These applications, by their nature, enforce certain tracking quality and lifetime requirements. These two requirements, however, are two conflicting optimization goals due to the stringent energy constraints of sensor nodes.

/pdf/a-study-on-object-tracking-quality-under-probabilistic-3tv2b16in0.pdf

A study on object tracking quality under probabilistic coverage in sensor networks

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