Provider-Controlled Bandwidth Management for HTTP-based Video Delivery

Over the past few years, a revolution in video delivery technology has taken place as mobile viewers and over-the-top (OTT) distribution paradigms have significantly changed the landscape of video delivery services. For decades, high quality video was only available in the home via linear television or physical media. Though Web-based services brought video to desktop and laptop computers, the dominance of proprietary delivery protocols and codecs inhibited research efforts. The recent emergence of HTTP adaptive streaming protocols has prompted a re-evaluation of legacy video delivery paradigms and introduced new questions as to the scalability and manageability of OTT video delivery. This dissertation addresses the question of how to enable for content and network service providers the ability to monitor and manage large numbers of HTTP adaptive streaming clients in an OTT environment. Our early work focused on demonstrating the viability of server-side pacing schemes to produce an HTTP-based streaming server. We also investigated the ability of client-side pacing schemes to work with both commodity HTTP servers and our HTTP streaming server. Continuing our client-side pacing research, we developed our own client-side data proxy architecture which was implemented on a variety of mobile devices and operating systems. We used the portable client architecture as a platform for investigating different rate adaptation schemes and algorithms. We then concentrated on evaluating the network impact of multiple adaptive bitrate clients competing for limited network resources, and developing schemes for enforcing fair access to network resources. The main contribution of this dissertation is the definition of segment-level client and network techniques for enforcing class of service (CoS) differentiation between OTT HTTP adaptive streaming clients. We developed a segment-level network proxy architecture which works transparently with adaptive bitrate clients through the use of segment replacement. We also defined a segment-level rate adaptation algorithm which uses download aborts to enforce CoS differentiation across distributed independent clients. The segment-level abstraction more accurately models application-network interactions and highlights the difference between segment-level and packet-level time scales. Our segment-level CoS enforcement techniques provide a foundation for creating scalable managed OTT video delivery services

Neuromorphic Learning Systems for Supervised and Unsupervised Applications

The advancements in high performance computing (HPC) have enabled the large-scale implementation of neuromorphic learning models and pushed the research on computational intelligence into a new era. Those bio-inspired models are constructed on top of unified building blocks, i.e. neurons, and have revealed potentials for learning of complex information. Two major challenges remain in neuromorphic computing. Firstly, sophisticated structuring methods are needed to determine the connectivity of the neurons in order to model various problems accurately. Secondly, the models need to adapt to non-traditional architectures for improved computation speed and energy efficiency. In this thesis, we address these two problems and apply our techniques to different cognitive applications. This thesis first presents the self-structured confabulation network for anomaly detection. Among the machine learning applications, unsupervised detection of the anomalous streams is especially challenging because it requires both detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research need. We present AnRAD (Anomaly Recognition And Detection), a bio-inspired detection framework that performs probabilistic inferences. We leverage the mutual information between the features and develop a self-structuring procedure that learns a succinct confabulation network from the unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base from the data streams. Compared to several existing anomaly detection methods, the proposed approach provides competitive detection accuracy as well as the insight to reason the decision making. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementation of the recall algorithms on the graphic processing unit (GPU) and the Xeon Phi co-processor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor (GPP). The implementation enables real-time service to concurrent data streams with diversified contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle abnormal behavior detection, the framework is able to monitor up to 16000 vehicles and their interactions in real-time with a single commodity co-processor, and uses less than 0.2ms for each testing subject. While adapting our streaming anomaly detection model to mobile devices or unmanned systems, the key challenge is to deliver required performance under the stringent power constraint. To address the paradox between performance and power consumption, brain-inspired hardware, such as the IBM Neurosynaptic System, has been developed to enable low power implementation of neural models. As a follow-up to the AnRAD framework, we proposed to port the detection network to the TrueNorth architecture. Implementing inference based anomaly detection on a neurosynaptic processor is not straightforward due to hardware limitations. A design flow and the supporting component library are developed to flexibly map the learned detection networks to the neurosynaptic cores. Instead of the popular rate code, burst code is adopted in the design, which represents numerical value using the phase of a burst of spike trains. This does not only reduce the hardware complexity, but also increases the result\u27s accuracy. A Corelet library, NeoInfer-TN, is implemented for basic operations in burst code and two-phase pipelines are constructed based on the library components. The design can be configured for different tradeoffs between detection accuracy, hardware resource consumptions, throughput and energy. We evaluate the system using network intrusion detection data streams. The results show higher detection rate than some conventional approaches and real-time performance, with only 50mW power consumption. Overall, it achieves 10^8 operations per Joule. In addition to the modeling and implementation of unsupervised anomaly detection, we also investigate a supervised learning model based on neural networks and deep fragment embedding and apply it to text-image retrieval. The study aims at bridging the gap between image and natural language. It continues to improve the bidirectional retrieval performance across the modalities. Unlike existing works that target at single sentence densely describing the image objects, we elevate the topic to associating deep image representations with noisy texts that are only loosely correlated. Based on text-image fragment embedding, our model employs a sequential configuration, connects two embedding stages together. The first stage learns the relevancy of the text fragments, and the second stage uses the filtered output from the first one to improve the matching results. The model also integrates multiple convolutional neural networks (CNN) to construct the image fragments, in which rich context information such as human faces can be extracted to increase the alignment accuracy. The proposed method is evaluated with both synthetic dataset and real-world dataset collected from picture news website. The results show up to 50% ranking performance improvement over the comparison models

Distributed multimedia systems

A distributed multimedia system (DMS) is an integrated communication, computing, and information system that enables the processing, management, delivery, and presentation of synchronized multimedia information with quality-of-service guarantees. Multimedia information may include discrete media data, such as text, data, and images, and continuous media data, such as video and audio. Such a system enhances human communications by exploiting both visual and aural senses and provides the ultimate flexibility in work and entertainment, allowing one to collaborate with remote participants, view movies on demand, access on-line digital libraries from the desktop, and so forth. In this paper, we present a technical survey of a DMS. We give an overview of distributed multimedia systems, examine the fundamental concept of digital media, identify the applications, and survey the important enabling technologies.published_or_final_versio

Design and implementation of a multi-agent opportunistic grid computing platform

Opportunistic Grid Computing involves joining idle computing resources in enterprises into a converged high performance commodity infrastructure. The research described in this dissertation investigates the viability of public resource computing in offering a plethora of possibilities through seamless access to shared compute and storage resources. The research proposes and conceptualizes the Multi-Agent Opportunistic Grid (MAOG) solution in an Information and Communication Technologies for Development (ICT4D) initiative to address some limitations prevalent in traditional distributed system implementations. Proof-of-concept software components based on JADE (Java Agent Development Framework) validated Multi-Agent Systems (MAS) as an important tool for provisioning of Opportunistic Grid Computing platforms. Exploration of agent technologies within the research context identified two key components which improve access to extended computer capabilities. The first component is a Mobile Agent (MA) compute component in which a group of agents interact to pool shared processor cycles. The compute component integrates dynamic resource identification and allocation strategies by incorporating the Contract Net Protocol (CNP) and rule based reasoning concepts. The second service is a MAS based storage component realized through disk mirroring and Google file-system’s chunking with atomic append storage techniques. This research provides a candidate Opportunistic Grid Computing platform design and implementation through the use of MAS. Experiments conducted validated the design and implementation of the compute and storage services. From results, support for processing user applications; resource identification and allocation; and rule based reasoning validated the MA compute component. A MAS based file-system that implements chunking optimizations was considered to be optimum based on evaluations. The findings from the undertaken experiments also validated the functional adequacy of the implementation, and show the suitability of MAS for provisioning of robust, autonomous, and intelligent platforms. The context of this research, ICT4D, provides a solution to optimizing and increasing the utilization of computing resources that are usually idle in these contexts

Quality of Service based Retrieval Strategy for Distributed Video on Demand on Multiple Servers

The recent advances and development of inexpensive computers and high speed networking technology have enabled the Video on Demand (VoD) application to connect to shared-computing servers, replacing the traditional computing environments where each application was having its own dedicated computing hardware. The VoD application enables the viewer to select, from a list of video files, his favorite video file and watch its reproduction at will. Early video on demand applications were based on single video server where video streams are initiated from a single server, then with the increase in the number of the clients who became interested in VoD services, the focus became on Distributed VoD architectures (DVoD) where the context of distribution may be distributed system components, distributed streaming servers, distributed media content etc.The VoD server must handle several issues in order to be able to present a successful service. It has to receive the clients’ requests and analyze them, calculate the necessary resources for each request, and decide whether a request can be admitted or not. Once the request is admitted, the server must schedule the request, retrieve the required video data and send the video data in a timely manner so that the client does not suffer data starvation in his buffer during the video reproduction. So, the overall objective of a VoD service provider is to provide a better Quality of Service (QoS). Some issues related to QoS are-efficient use of bandwidth, providing better throughput etc.One of the important issues is to retrieve the video data from the servers in minimum time and to start the playback of the video at client side with a minimum waiting time. The overall time elapsed in retrieving the video data and starting the playback is known as access time. The thesis presents an efficient retrieval strategy for a distributed VoD environment where the basic objective is to minimize the access time by maintaining the presentation continuity at the client side. We have neglected some of the network parameters which may affect the access time, by assuming a high speed network between the servers and the client. The performance of the strategy has been analyzed and is compared with the referred PAR (Play After Retrieval) strategy. Further, the strategy is also analyzed under availability condition which is a more realistic approach

Third International Symposium on Space Mission Operations and Ground Data Systems, part 2

Under the theme of 'Opportunities in Ground Data Systems for High Efficiency Operations of Space Missions,' the SpaceOps '94 symposium included presentations of more than 150 technical papers spanning five topic areas: Mission Management, Operations, Data Management, System Development, and Systems Engineering. The symposium papers focus on improvements in the efficiency, effectiveness, and quality of data acquisition, ground systems, and mission operations. New technology, methods, and human systems are discussed. Accomplishments are also reported in the application of information systems to improve data retrieval, reporting, and archiving; the management of human factors; the use of telescience and teleoperations; and the design and implementation of logistics support for mission operations. This volume covers expert systems, systems development tools and approaches, and systems engineering issues

Une approche flexible et décentralisée du traitement de requêtes dans les systèmes géo-distribués

This thesis studies the design of query processing systems, across a diversity of geo-distributed settings. Optimising performance metrics such as response time, freshness, or operational cost involves design decisions, such as what derived state (e.g., indexes, materialised views, or caches) to maintain, and how to distribute and where to place the corresponding computation and state. These metrics are often in tension, and the trade-offs depend on the specific application and/or environment. This requires the ability to adapt the query engine's topology and architecture, and the placement of its components. This thesis makes the following contributions: - A flexible architecture for geo-distributed query engines, based on components connected in a bidirectional acyclic graph. - A common microservice abstraction and API for these components, the Query Processing Unit (QPU). A QPU encapsulates some primitive query processing task. Multiple QPU types exist, which can be instantiated and composed into complex graphs. - A model for constructing modular query engine architectures as a distributed topology of QPUs, enabling flexible design and trade-offs between performance metrics. - Proteus, a QPU-based framework for constructing and deploying query engines. - Representative deployments of Proteus and experimental evaluation thereof.Cette thèse présente l'étude de la conception de systèmes de traitement de requêtes dans divers cadres géo-distribués. L'optimisation des mesures de performance telles que le temps de réponse, la fraîcheur ou le coût opérationnel implique des décisions de conception tel que le choix de l’état dérivé (indices, vues matérialisées, caches par ex.) à construire et maintenir, et la distribution et le placement de ces derniers et de leurs calculs. Ces métriques sont souvent opposées et les compromis dépendent de l'application et/ou de la spécificité de l'environnement. La capacité d'adapter la topologie et l'architecture du système de traitement de requêtes devient alors essentielle, ainsi que le placement de ses composants. Cette thèse apporte les contributions suivantes : - Une architecture flexible pour les systèmes de traitement de requêtes géo-distribués, basée sur des composants connectés dans un graphe bidirectionnel acyclique. - Une abstraction de micro-service et une API communes pour ces composants, le Query Processing Unit (QPU). Un QPU encapsule une tâche de traitement de requête primitive. Il existe plusieurs types de QPU qui peuvent être instanciés et composés en graphes complexes. - Un modèle pour construire des architectures de systèmes de traitement de requêtes modulaires composées d’une topologie distribuée de QPUs, permettant une conception flexible et des compromis selon les mesures de performance visées. - Proteus, un framework basé sur les QPU, permettant la construction et le déploiement de systèmes de traitement de requêtes. - Déploiements représentatifs de systèmes de traitement de requêtes à l'aide de Proteus, et leur évaluation expérimentale

Design and implementation of a multi-agent opportunistic grid computing platform

Opportunistic Grid Computing involves joining idle computing resources in enterprises into a converged high performance commodity infrastructure. The research described in this dissertation investigates the viability of public resource computing in offering a plethora of possibilities through seamless access to shared compute and storage resources. The research proposes and conceptualizes the Multi-Agent Opportunistic Grid (MAOG) solution in an Information and Communication Technologies for Development (ICT4D) initiative to address some limitations prevalent in traditional distributed system implementations. Proof-of-concept software components based on JADE (Java Agent Development Framework) validated Multi-Agent Systems (MAS) as an important tool for provisioning of Opportunistic Grid Computing platforms. Exploration of agent technologies within the research context identified two key components which improve access to extended computer capabilities. The first component is a Mobile Agent (MA) compute component in which a group of agents interact to pool shared processor cycles. The compute component integrates dynamic resource identification and allocation strategies by incorporating the Contract Net Protocol (CNP) and rule based reasoning concepts. The second service is a MAS based storage component realized through disk mirroring and Google file-system’s chunking with atomic append storage techniques. This research provides a candidate Opportunistic Grid Computing platform design and implementation through the use of MAS. Experiments conducted validated the design and implementation of the compute and storage services. From results, support for processing user applications; resource identification and allocation; and rule based reasoning validated the MA compute component. A MAS based file-system that implements chunking optimizations was considered to be optimum based on evaluations. The findings from the undertaken experiments also validated the functional adequacy of the implementation, and show the suitability of MAS for provisioning of robust, autonomous, and intelligent platforms. The context of this research, ICT4D, provides a solution to optimizing and increasing the utilization of computing resources that are usually idle in these contexts

Satellite Networks: Architectures, Applications, and Technologies

Since global satellite networks are moving to the forefront in enhancing the national and global information infrastructures due to communication satellites' unique networking characteristics, a workshop was organized to assess the progress made to date and chart the future. This workshop provided the forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. Presentations on overview, state-of-the-art in research, development, deployment and applications and future trends on satellite networks are assembled