Effectiveness of Cloud Services for Scientific and VoD Applications

Cloud platforms have emerged as the primary data warehouse for a variety of applications, such as DropBox, iCloud, Google Music, etc. These applications allow users to store data in the cloud and access it from anywhere in the world. Commercial clouds are also well suited for providing high-end servers for rent to execute applications that require computation resources sporadically. Cloud users only pay for the time they actually use the hardware and the amount of data that is transmitted to and from the cloud, which has the potential to be more cost effective than purchasing, hosting, and maintaining dedicated hardware. In this dissertation, we look into the efficiency of the cloud Infrastructure-as-a-Service (IaaS) model for two real time high bandwidth applications: A scientific application of short-term weather forecasting and Video on Demand services. We show that, cloud services are efficient in both network and computation for real time scientific application of weather forecasting. We present a related list reordering approach, which reduces the network traffic of serving videos from VoD services and improve the efficiency of caches deployed to serve them. Also, we present transcoding policies to reduce the transcoding workload and present prediction models to maintain performance of providing ABR streaming of VoD services at the client with online transcoding in the cloud

Cooperative announcement-based caching for video-on-demand streaming

Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth

Entrega de conteúdos multimédia em over-the-top: caso de estudo das gravações automáticas

Doutoramento em Engenharia EletrotécnicaOver-The-Top (OTT) multimedia delivery is a very appealing approach for providing ubiquitous, exible, and globally accessible services capable of low-cost and unrestrained device targeting. In spite of its appeal, the underlying delivery architecture must be carefully planned and optimized to maintain a high Qualityof- Experience (QoE) and rational resource usage, especially when migrating from services running on managed networks with established quality guarantees. To address the lack of holistic research works on OTT multimedia delivery systems, this Thesis focuses on an end-to-end optimization challenge, considering a migration use-case of a popular Catch-up TV service from managed IP Television (IPTV) networks to OTT. A global study is conducted on the importance of Catch-up TV and its impact in today's society, demonstrating the growing popularity of this time-shift service, its relevance in the multimedia landscape, and tness as an OTT migration use-case. Catch-up TV consumption logs are obtained from a Pay-TV operator's live production IPTV service containing over 1 million subscribers to characterize demand and extract insights from service utilization at a scale and scope not yet addressed in the literature. This characterization is used to build demand forecasting models relying on machine learning techniques to enable static and dynamic optimization of OTT multimedia delivery solutions, which are able to produce accurate bandwidth and storage requirements' forecasts, and may be used to achieve considerable power and cost savings whilst maintaining a high QoE. A novel caching algorithm, Most Popularly Used (MPU), is proposed, implemented, and shown to outperform established caching algorithms in both simulation and experimental scenarios. The need for accurate QoE measurements in OTT scenarios supporting HTTP Adaptive Streaming (HAS) motivates the creation of a new QoE model capable of taking into account the impact of key HAS aspects. By addressing the complete content delivery pipeline in the envisioned content-aware OTT Content Delivery Network (CDN), this Thesis demonstrates that signi cant improvements are possible in next-generation multimedia delivery solutions.A entrega de conteúdos multimédia em Over-The-Top (OTT) e uma proposta atractiva para fornecer um serviço flexível e globalmente acessível, capaz de alcançar qualquer dispositivo, com uma promessa de baixos custos. Apesar das suas vantagens, e necessario um planeamento arquitectural detalhado e optimizado para manter níveis elevados de Qualidade de Experiência (QoE), em particular aquando da migração dos serviços suportados em redes geridas com garantias de qualidade pré-estabelecidas. Para colmatar a falta de trabalhos de investigação na área de sistemas de entrega de conteúdos multimédia em OTT, esta Tese foca-se na optimização destas soluções como um todo, partindo do caso de uso de migração de um serviço popular de Gravações Automáticas suportado em redes de Televisão sobre IP (IPTV) geridas, para um cenário de entrega em OTT. Um estudo global para aferir a importância das Gravações Automáticas revela a sua relevância no panorama de serviços multimédia e a sua adequação enquanto caso de uso de migração para cenários OTT. São obtidos registos de consumos de um serviço de produção de Gravações Automáticas, representando mais de 1 milhão de assinantes, para caracterizar e extrair informação de consumos numa escala e âmbito não contemplados ate a data na literatura. Esta caracterização e utilizada para construir modelos de previsão de carga, tirando partido de sistemas de machine learning, que permitem optimizações estáticas e dinâmicas dos sistemas de entrega de conteúdos em OTT através de previsões das necessidades de largura de banda e armazenamento, potenciando ganhos significativos em consumo energético e custos. Um novo mecanismo de caching, Most Popularly Used (MPU), demonstra um desempenho superior as soluções de referencia, quer em cenários de simulação quer experimentais. A necessidade de medição exacta da QoE em streaming adaptativo HTTP motiva a criaçao de um modelo capaz de endereçar aspectos específicos destas tecnologias adaptativas. Ao endereçar a cadeia completa de entrega através de uma arquitectura consciente dos seus conteúdos, esta Tese demonstra que são possíveis melhorias de desempenho muito significativas nas redes de entregas de conteúdos em OTT de próxima geração

Livenet: A low-latency video transport network for large-scale live streaming

Low-latency live streaming has imposed stringent latency requirements on video transport networks. In this paper we report on the design and operation of the Alibaba low-latency video transport network, LiveNet. LiveNet builds on a flat CDN overlay with a centralized controller for global optimization. As part of this, we present our design of the global routing computation and path assignment, as well as our fast data transmission architecture with fine-grained control of video frames. The performance results obtained from three years of operation demonstrate the effectiveness of LiveNet in improving CDN performance and QoE metrics. Compared with our prior state-of-The-Art hierarchical CDN deployment, LiveNet halves the CDN delay and ensures 98% of views do not experience stalls and that 95% can start playback within 1 second. We further report our experiences of running LiveNet over the last 3 years

Survey of Transportation of Adaptive Multimedia Streaming service in Internet

[DE] World Wide Web is the greatest boon towards the technological advancement of modern era. Using the benefits of Internet globally, anywhere and anytime, users can avail the benefits of accessing live and on demand video services. The streaming media systems such as YouTube, Netflix, and Apple Music are reining the multimedia world with frequent popularity among users. A key concern of quality perceived for video streaming applications over Internet is the Quality of Experience (QoE) that users go through. Due to changing network conditions, bit rate and initial delay and the multimedia file freezes or provide poor video quality to the end users, researchers across industry and academia are explored HTTP Adaptive Streaming (HAS), which split the video content into multiple segments and offer the clients at varying qualities. The video player at the client side plays a vital role in buffer management and choosing the appropriate bit rate for each such segment of video to be transmitted. A higher bit rate transmitted video pauses in between whereas, a lower bit rate video lacks in quality, requiring a tradeoff between them. The need of the hour was to adaptively varying the bit rate and video quality to match the transmission media conditions. Further, The main aim of this paper is to give an overview on the state of the art HAS techniques across multimedia and networking domains. A detailed survey was conducted to analyze challenges and solutions in adaptive streaming algorithms, QoE, network protocols, buffering and etc. It also focuses on various challenges on QoE influence factors in a fluctuating network condition, which are often ignored in present HAS methodologies. Furthermore, this survey will enable network and multimedia researchers a fair amount of understanding about the latest happenings of adaptive streaming and the necessary improvements that can be incorporated in future developments.Abdullah, MTA.; Lloret, J.; Canovas Solbes, A.; García-García, L. (2017). Survey of Transportation of Adaptive Multimedia Streaming service in Internet. Network Protocols and Algorithms. 9(1-2):85-125. doi:10.5296/npa.v9i1-2.12412S8512591-

PEER-TO-PEER VIDEO CONTENT DELIVERY OPTIMIZATION SERVICE IN A DISTRIBUTED NETWORK

Η δυναμικά προσαρμοζόμενη ροή βίντεο μέσω HTTP (DASH) παρέχει βελτιώσεις στην ποιότητα της εμπειρίας χρήσης (QoE) κατά την αναπαραγωγή βίντεο σε δίκτυα παλαιότερα των δικτύων 5ης γενιάς (5G). Ωστόσο, οι εφαρμογές τύπου νέφους τις οποίες μπορεί να παρέχει η αρχιτεκτονική δικτύων 5ης γενιάς, σε συνδυασμό με την υλοποίηση υπολογιστικών υποδομών νέφους στο άκρο του δικτύου και κοντά στους τελικούς χρήστες, μπορεί να βελτιώσει σημαντικά τόσο την ποιότητα της προσφερόμενης υπηρεσίας (QoS) όσο και την εμπειρία χρήσης λόγω της δυνατότητας προσωρινής αποθήκευσης περιεχομένου βίντεο στο άκρο του δικτύου, λόγω της δυνατότητας παροχής προσωρινής αποθήκευσης μέρους του βίντεο στο άκρο του δικτύου. Επιπροσθέτως, εκτός της αποθήκευσης στο και διανομής βίντεο από το άκρο του δικτύου προς τους τελικούς χρήστες, οι νέες υποδομές βίντεο θα παρέχουν τη δυνατότητα διανομής περιεχομένου βίντεο απευθείας από συσκευή σε συσκευή (D2D). Αξιοποιώντας τις τεχνολογίες αυτές, μπορούν να υλοποιηθούν καινοτόμες υπηρεσίες ροής βίντεο, οι οποίες μπορούν όχι μόνο να βελτιώσουν την εμπειρία χρήσης των τελικών χρηστών κατά την αναπαραγωγή βίντεο, αλλά και να μειώσουν το συνολικό κόστος διανομής βίντεο καθώς και την συμφόρηση των δικτύων, άρα και την καθυστέρηση από άκρο σε άκρο και τη συμφόρηση στα δίκτυα διανομής περιεχομένου (CDN) των παρόχων υπηρεσιών διανομής και ροής βίντεο. Στην παρούσα διπλωματική εργασία μελετούμε την επίπτωση που έχουν διάφοροι συνδυασμοί τεχνικών προσωρινής αποθήκευσης, διανομής, καθώς και επιλογής ανάλυσης, σε περιεχόμενο βίντεο, πάνω στην ποιότητα της προσφερόμενης υπηρεσίας και στην εμπειρία των τελικών χρηστών που βρίσκονται στο άκρο του δικτύου, οι οποίες μπορούν να αξιοποιηθούν στη δημιουργία μιας καινοτόμας υπηρεσίας που βελτιστοποιεί τη διανομή περιεχομένου βίντεο μεταξύ ομότιμων κόμβων (P2P) σε ένα κατανεμημένο δίκτυο.Dynamtic Adaptive Streaming over HTTP (DASH) has yield several improvements in the video playback Quality of Experimence (QoE) for the end users in pre-fifth generation (5G) networks. However, cloud applications that 5G networks enable, combined with cloud infrastructures at the edge of the network and in close vicinity to the end users, can offer significant improvements in both the offered Quality of Service (QoS) and QoE because of the video content caching capabilities at the edge of the network that the edge cloud can offer. Furthermore, in addition to edge caching and edge video streaming to the end users, new video infrastructures can offer Device-to-Device (D2D) video content exchange and delivery. Taking advantage of these technologies, innovative video streaming services can be developed which not only improve the video playback QoE for the end users but also reduce the video delivery costs and generated network traffic, which also means reduced end-to-end latency and reduced overhead in video content providers’ Content Delivery Network (CDN). In this thesis we study the impact of using different combinations of distinct video caching techniques, video segment request and streaming algorithms and video resolution selection logics on the QoS and the QoE of end users at the network edge, which can be used in developing an innovative Peer-to-Peer (P2P) video content delivery optimization service in a distributed network