Supercharged PlanetLab Platform Architecture

This report describes the Supercharged Planetlab Platform (SPP), a system designed as a prototype of an internet-scale overlay hosting platform. Overlay networks have become an important vehicle for delivering Internet applications. Overlay network nodes are typically implemented using general purpose servers or clusters. The SPP offers a more integrated architecture, combining general-purpose servers with high performance Network Processor (NP) subsystems. SPP nodes have recently been deployed as part of the Global Environment for Network Innovation (GENI) and are available for use by research users

Algorithms for Constructing Overlay Networks For Live Streaming

We present a polynomial time approximation algorithm for constructing an overlay multicast network for streaming live media events over the Internet. The class of overlay networks constructed by our algorithm include networks used by Akamai Technologies to deliver live media events to a global audience with high fidelity. We construct networks consisting of three stages of nodes. The nodes in the first stage are the entry points that act as sources for the live streams. Each source forwards each of its streams to one or more nodes in the second stage that are called reflectors. A reflector can split an incoming stream into multiple identical outgoing streams, which are then sent on to nodes in the third and final stage that act as sinks and are located in edge networks near end-users. As the packets in a stream travel from one stage to the next, some of them may be lost. A sink combines the packets from multiple instances of the same stream (by reordering packets and discarding duplicates) to form a single instance of the stream with minimal loss. Our primary contribution is an algorithm that constructs an overlay network that provably satisfies capacity and reliability constraints to within a constant factor of optimal, and minimizes cost to within a logarithmic factor of optimal. Further in the common case where only the transmission costs are minimized, we show that our algorithm produces a solution that has cost within a factor of 2 of optimal. We also implement our algorithm and evaluate it on realistic traces derived from Akamai's live streaming network. Our empirical results show that our algorithm can be used to efficiently construct large-scale overlay networks in practice with near-optimal cost

Performance-Engineered Network Overlays for High Quality Interaction in Virtual Worlds

Overlay hosting systems such as PlanetLab, and cloud computing environments such as Amazon’s EC2, provide shared infrastructures within which new applications can be developed and deployed on a global scale. This paper ex-plores how systems of this sort can be used to enable ad-vanced network services and sophisticated applications that use those services to enhance performance and provide a high quality user experience. Specifically, we investigate how advanced overlay hosting environments can be used to provide network services that enable scalable virtual world applications and other large-scale distributed applications requiring consistent, real-time performance. We propose a novel network architecture called Forest built around per-session tree-structured communication channels that we call comtrees. Comtrees are provisioned and support both unicast and multicast packet delivery. The multicast mechanism is designed to be highly scalable and light-weight enough to support the rapid changes to multicast subscriptions needed for efficient support of state updates within virtual worlds. We evaluate performance using a combination of analysis and experimental measurement of a partial system prototype that supports fully functional distributed game sessions. Our results provide the data needed to enable accurate projections of performance for a variety of session and system configurations

Overlay Networks: An Akamai Perspective

The Internet is transforming every aspect of communication in human so-ciety by enabling a wide range of applications for business, commerce, en-tertainment, news, and social interaction. Modern and future distributed applications require high reliability, performance, security, and scalability

SCOPE: Synergistic Content Distribution and Peer-to-Peer Networks

Distributing content on the Internet is an important economic, educational, social, and cultural endeavor. To this end, several existing efforts use traditional server-based content distribution networks (CDNs) to replicate and distribute Web and multimedia content of big content producers, such as news Web sites, or big businesses, such as online shopping websites, etc., to millions of Internet users. This approach places a large number of content servers at strategic locations on the Internet, incurring a very large deployment and operating cost. Therefore, it is available only to some wealthy companies/organizations. Individual users and small content publishers may rely on a more economical content dissemination approach based on recent peer-to-peer technology to distribute their own content. Nevertheless, it is the ephemeral and the limited resources nature of peer-to-peer networks that hinder a wide spread adoption of peer-to-peer technology as a reliable content distribution solution. It is, therefore, important that a new generation of cost-effective and reliable content distribution framework be proposed and investigated. Building on the successes and failures of previous content distribution approaches, the proposed research goal is to find and evaluate a Synergistic Content Distribution and Peer-to-Peer Networks (SCOPE). SCOPE leverages the reliability and the resourcefulness of traditional server-based CDNs while tapping on the economical and dynamic resources of peers

ML-based Adaptive Video Streaming techniques for 5G and beyond mobile data networks

Μια εφαρμογή τεχνητού νευρωνικού δικτύου για προσαρμοστική ροή βίντεο έχει χρησιμοποιηθεί και μελετηθεί για αρκετά χρόνια. Ωστόσο, για ορισμένες εφαρμογές, είναι ακόμη υπό ανάπτυξη και έχουν γίνει μια από τις ακαδημαϊκές και βιομηχανικές ερευνητικές γραμμές στη μηχανική μάθηση. Μία από αυτές τις εφαρμογές επικεντρώνεται στην αντίληψη των τελικών χρηστών για προσαρμοστικές τεχνικές ροής βίντεο σε δίκτυα κινητής τηλεφωνίας 5G. Αυτή η διατριβή εξετάζει πώς αντιπροσωπεύονται διαφορετικές τοπολογίες νευρωνικών δικτύων, με στόχο να επηρεάσουν την ανάπτυξη μοντέλων πρόβλεψης QoE για ροή βίντεο. Επιπλέον, αυτό το έγγραφο παρουσιάζει μια αρχιτεκτονική νευρωνικού δικτύου αιχμής για στόχους πρόβλεψης QoE που συνδέει το στρεπτικό στρώμα με το αμφίδρομο επίπεδο LSTM. Για σύγκριση, η αποτελεσματικότητα αρκετών προηγουμένως προτεινόμενων μοντέλων νευρωνικών δικτύων - ένα δίκτυο τριών επιπέδων CNN και ένα δίκτυο δύο επιπέδων LSTM perceptron - έχει δημιουργηθεί και αξιολογηθεί. Για να εξηγήσει τις υπερπαραμέτρους και τις τοπολογίες τους, αυτή η διπλωματική εργασία παρουσίασε δύο στρώματα biLSTM, τριεπίπεδα FNN και μικτά μοντέλα CNN και LSTM QoE. Αυτά τα μοντέλα νευρωνικών δικτύων εκπαιδεύτηκαν χρησιμοποιώντας πραγματικά πειραματικά δεδομένα από το Πανεπιστήμιο του Τέξας στο inστιν - Βάση δεδομένων QoE βίντεο LIVE NETFLIX του εργαστηρίου Image and Video Engineering Lab. Τα αποτελέσματα προσομοίωσης αξιολογήθηκαν χρησιμοποιώντας μετρήσεις PCC, SROCC και RMSE για να αποδειχθεί η αποτελεσματικότητα της ακριβούς πρόβλεψης QoE για ένα προσαρμοστικό σύστημα ροής βίντεο 5G. Επιπλέον, υπολογίστηκε η πολυπλοκότητα της προτεινόμενης αρχιτεκτονικής των νευρωνικών δικτύων. Μετά την ανάλυση των αποτελεσμάτων σύγκρισης των μελετημένων μοντέλων QoE, το μοντέλο FNN παρείχε το καλύτερο επίπεδο ακρίβειας πρόβλεψης βάσει της αξίας RSME και, ταυτόχρονα, κατέλαβε ένα από τα χαμηλότερα επίπεδα υπολογιστικής πολυπλοκότητας. Αυτό υποδεικνύει ότι το FNN μπορεί να είναι η καλύτερη μέθοδος για την πρόβλεψη QoE για ροή βίντεο 5G λόγω της σχετικά χαμηλής πολυπλοκότητάς του και της ανταγωνιστικά υψηλής ακρίβειας πρόβλεψης.An artificial neural network application for adaptive video streaming has been used and studied for several years. However, for some applications, they are still under development and have become one of the academic and industrial research lines in machine learning. One of these applications focuses on perceptual end-users prediction for adaptive video streaming techniques in 5G mobile networks. This dissertation looks at how different neural network topologies are represented, with the goal of influencing the development of QoE prediction models for streaming video. In addition, this paper presents a cutting-edge neural network architecture for QoE prediction targets that connects the convolutional layer to the bidirectional LSTM layer. For comparison, the efficacy of several previously suggested neural network models - a three-layer CNN and a two-layer LSTM perceptron network - has been built and assessed. To explain their hyperparameters and topologies, this dissertation presented two-layered biLSTM, three-layered FNN, and mixed CNN and LSTM QoE models. These neural netowrks models were trained using real experimental data from the University of Texas at Austin – Image and Video Engineering Lab's LIVE NETFLIX video QoE database. Simulation results were evaluated using PCC, SROCC and RMSE metrics to demonstrate the effectiveness of accurate QoE prediction for an adaptive 5G video streaming system. Additionally, the complexity of the proposed architecture of neural networks was calculated. After analyzing the comparison results of the studied QoE models, the FNN model provided the best level of forecasting accuracy by the RSME value and, at the same time, occupied one of the lowest levels of computational complexity. This indicates that FNN can be the best method for QoE prediction for 5G video streaming due to its relatively low complexity and competitively high prediction accuracy

Algoritmos para retransmissão de vídeo H.264 em redes sobrepostas

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2010Com a popularização da banda larga os usuários domésticos passaram a consumir diversos serviços multimídia pela Internet: Telefonia IP, rádio, vídeo sob demanda. Por enquanto, em se tratando de vídeos, os principais serviços disponíveis na rede não oferecem as características presentes na TV, ou seja, não são ao vivo e necessitam de buferização, gerando portanto atraso. Eles também requerem uma grande largura de banda nos servidores, linearmente crescente com número de clientes. Através das redes sobrepostas (orientadas a dados) pode-se remediar a falta de suporte ao IP Multicast, que seria uma solução para essa classe de aplicação, sem os problemas com a largura de banda. Entretanto tais redes não especificam o tratamento das perdas de pacotes, de modo que ou estas são ignoradas, ou são recuperadas em qualquer situação. Este trabalho faz uma análise dos recursos do padrão de compressão de vídeo digital H.264 e apresenta critérios claros para retransmissão a partir dos diferentes tipos de codificação presentes nesse formato. Estabelece prioridades a partir desses tipos e define três algoritmos para a escolha de partes perdidas para a recuperação. Um deles (SeRViSO) observa uma meta fixa levando em conta as prioridades, em termos de quanto dos dados deve retransmitir. O segundo (Adaptativo) usa uma meta variável, buscando inicialmente a retransmissão de todas as partes perdidas de um segmento, mas a cada vez que a retransmissão de partes do mesmo segmento for solicitada a meta diminui. O último algoritmo ((m,k)-Firm) também tem metas fixas, mas uma para cada tipo de codificação, e não tolera perdas de partes com o mesmo tipo de codificação muito próximas. Todos eles são definidos dentro do escopo da proposta da rede sobreposta SeRViSO. As vantagens de um tratamento claro das perdas são óbvias: maior adaptação às condições da rede e perdas menos importantes, ou seja, que não redundem em propagação de erro por muitos quadros. Os resultados dos testes com um protótipo confirmam isso, de forma que a perda de partes do tipo mais importante foi consistentemente menor que a perda média

Optimized protection of streaming media authenticity

Ph.DDOCTOR OF PHILOSOPH