48 research outputs found
Building the Future Internet through FIRE
The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate
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Learning for Network Applications and Control
The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements.
There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms.
In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds.
In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics.
In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs.
We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy.
In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling
A REVIEW STUDY OF EUROPEAN R&D PROJECTS FOR SATELLITE COMMUNICATIONS IN 5G/6G ERA
Κατά τις τελευταίες δεκαετίες τα δορυφορικά συστήματα τηλεπικοινωνιών έχουν προσφέρει μια γκάμα από πολυμεσικές υπηρεσίες όπως δορυφορική τηλεόραση, δορυφορική τηλεφωνία και ευρυζωνική πρόσβαση στο διαδίκτυο. Οι μακροπρόθεσμες τεχνολογικές αναβαθμίσεις σε συνδυασμό με την προσθήκη νέων δορυφορικών συστημάτων γεωστατικής και ελλειπτικής τροχιάς και με την ενσωμάτωση τεχνολογιών πληροφορικής έχουν ωθήσει την αύξηση του μέγιστου εύρους των δορυφόρων στο 1Gbps σε μεμονωμένους δορυφόρους ενώ σε διάταξη αστερισμού μπορούν να ξεπεράσουν το 1 Tbps. Σε συνδυασμό με την μείωση του χρόνου απόκρισης σε ρυθμούς ανταγωνιστικούς με τις χερσαίες υποδομές ανοίγουν νέες ευκαιρίες και νέους ρόλους εντός ενός οικοσυστήματος ετερογενούς δικτύων 5ης γενιάς.
Σε αυτήν την διατριβή, αξιολογούμε επιδοτούμενα επιστημονικά προγράμματα έρευνας και ανάπτυξης της Ευρωπαϊκής Επιτροπής Διαστήματος (ESA) και του προγράμματος επιδότησης Horizon 2020 της Ευρωπαϊκής Ένωσης, προκειμένου να εξηγήσουμε τις δυνατότητες των δορυφόρων εντός ενός ετερογενούς δικτύου 5ης γενιάς, αναφέρουμε συγκεκριμένα αυτά που αφορούν την εξέλιξη των δορυφορικών ψηφιακών συστημάτων και την ικανότητα ενσωμάτωσης τους σε τωρινές αλλά και μελλοντικές υποδομές χερσαίων τηλεπικοινωνιακών δικτύων μέσω της εμφάνισης νέων τεχνολογιών στις ηλεκτρονικές και οπτικές επικοινωνίες αέρος μαζί με την εμφάνιση τεχνολογιών πληροφορικής όπως της δικτύωσης βασισμένης στο λογισμικό και της εικονικοποίησης λειτουργιών δικτύου.
Αναφερόμαστε στους στόχους του κάθε project ξεχωριστά και κατηγοριοποιημένα στους ακόλουθους τομείς έρευνας:
-Συσσωμάτωση των δορυφόρων με τα επίγεια δίκτυα 5ης γενιάς με οργανωμένες μελέτες και στρατηγικές
-Ενσωμάτωση των τεχνολογιών δικτύωσης βασισμένης στο λογισμικό και εικονικοποίησης λειτουργιών δικτύου στο δορυφορικών τμήμα των δικτύων 5ης γενιάς
-Ο ρόλος των δορυφόρων σε εφαρμογές του διαδικτύου των πραγμάτων σε συνάφεια με τα χερσαία δίκτυα 5ης γενιάς
-Ο ρόλος των δορυφόρων στην δίκτυα διανομής πολυμεσικού περιεχομένου & η επιρροή των πρωτοκόλλων διαδικτύου στην ποιότητα υπηρεσίας χρήστη κατά την διάρκεια μιας δορυφορικής σύνδεσης.
-Μελλοντικές βελτιώσεις και εφαρμογές στα δορυφορικά συστήματα με έμφαση στα μελλοντικά πρότυπα του φυσικό επιπέδου
Στο τέλος διαθέτουμε ένα παράρτημα που αφορά τεχνικές αναλύσεις στην εξέλιξη του φυσικού επιπέδου των δορυφορικών συστημάτων, συνοδευόμενο με την συσχετιζόμενη βιβλιογραφία για περαιτέρω μελέτη.Over the last decades satellite telecommunication systems offer many types of multimedia services like Satellite TV, telephony and broadband internet access. The long-term technological evolutions occurred into state-of-the-art satellite systems altogether with the addition of new high throughput geostatic and non-geostatic systems, individual satellites can now achieve a peak bandwidth of up to Gbps, and with possible extension into satellite constellation systems the total capacity can reach up to Tbps. Supplementary, with systems latency being comparable to terrestrial infrastructures and with integration of several computer science technologies, satellite systems can achieve new & more advanced roles inside a heterogeneous 5G network’s ecosystem.
In this thesis, we have studied European Space Agency (ESA’s) and European Union’s (EU) Horizon 2020 Research and Development (R&D) funded projects in order to describe the satellite capabilities within a 5G heterogeneous network, mentioning the impact of the evolution of digital satellite communications and furthermore the integration with the state-of the art & future terrain telecommunication systems by new technologies occurred through the evolution of electronic & free space optical communications alongside with the integration of computer science’s technologies like Software Defined Networking (SDN) and Network Function Virtualization (NFV).
In order to describe this evolution we have studied the concepts of each individual project, categorized chronically and individual by its scientific field of research. Our main scientific trends for this thesis are:
-Satellite Integration studies & strategies into the 5G terrestrial networks
-Integration of SDN and NFV technologies on 5G satellite component
-Satellite’s role in the Internet of Things applications over 5G terrestrial networks
-Satellite’s role in Content Distribution Networks & internet protocols impact over user’s Quality of Experience (QoE) over a satellite link
-The future proposals upon the evolution of Satellite systems by upcoming improvements and corresponding standards
Finally, we have created an Annex for technical details upon the evolution of physical layer of the satellite systems with the corresponding bibliography of this thesis for future study
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
QoE-Centric Control and Management of Multimedia Services in Software Defined and Virtualized Networks
Multimedia services consumption has increased tremendously since the deployment of 4G/LTE networks. Mobile video services (e.g., YouTube and Mobile TV) on smart devices are expected to continue to grow with the emergence and evolution of future networks such as 5G. The end user’s demand for services with better quality from service providers has triggered a trend towards Quality of Experience (QoE) - centric network management through efficient utilization of network resources. However, existing network technologies are either unable to adapt to diverse changing network conditions or limited in available resources.
This has posed challenges to service providers for provisioning of QoE-centric multimedia services. New networking solutions such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) can provide better solutions in terms of
QoE control and management of multimedia services in emerging and future networks. The features of SDN, such as adaptability, programmability and cost-effectiveness make it suitable for bandwidth-intensive multimedia applications such as live video streaming, 3D/HD video and video gaming. However, the delivery of multimedia services over SDN/NFV networks to achieve optimized QoE, and the overall QoE-centric network resource management remain an open question especially in the advent development of future softwarized networks.
The work in this thesis intends to investigate, design and develop novel approaches for QoE-centric control and management of multimedia services (with a focus on video streaming services) over software defined and virtualized networks.
First, a video quality management scheme based on the traffic intensity under Dynamic Adaptive Video Streaming over HTTP (DASH) using SDN is developed. The proposed scheme can mitigate virtual port queue congestion which may cause
buffering or stalling events during video streaming, thus, reducing the video quality.
A QoE-driven resource allocation mechanism is designed and developed for improving the end user’s QoE for video streaming services. The aim of this approach is to find the best combination of network node functions that can provide an optimized QoE level to end-users through network node cooperation. Furthermore, a novel QoE-centric management scheme is proposed and developed, which utilizes Multipath TCP (MPTCP) and Segment Routing (SR) to enhance QoE for video streaming services over SDN/NFV-based networks. The goal of this strategy is to enable service providers to route network traffic through multiple
disjointed bandwidth-satisfying paths and meet specific service QoE guarantees to the end-users. Extensive experiments demonstrated that the proposed schemes in this work improve the video quality significantly compared with the state-of-the-
art approaches. The thesis further proposes the path protections and link failure-free MPTCP/SR-based architecture that increases survivability, resilience, availability and robustness of future networks. The proposed path protection and dynamic link recovery scheme achieves a minimum time to recover from a failed link and avoids link congestion in softwarized networks
Game-theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks
This paper presents a game-theoretic scalable offloading system that provides seamless video streaming services by effectively offloading parts of video traffic in all video streaming services to a WiFi network to alleviate cellular network congestion. The system also consolidates multiple physical paths in a cost-effective manner. In the proposed system, the fountain encoding symbols of compressed video data are transmitted through long term evolution (LTE) and WiFi networks concurrently to flexibly control the amount of video traffic through the WiFi network as well as mitigate video quality degradation caused by wireless channel errors. Furthermore, the progressive second price auction mechanism is employed to allocate the limited LTE resources to multiple user equipment in order to maximize social welfare while converging to the epsilon-Nash equilibrium. Specifically, we design an application-centric resource valuation that explicitly considers both the realistic wireless network conditions and characteristics of video streaming services. In addition, the scalability and convergence properties of the proposed system are verified both theoretically and experimentally. The proposed system is implemented using network simulator 3. Simulation results are provided to demonstrate the performance improvement of the proposed system.111Nsciescopu
QoE management of multimedia streaming services in future networks : a tutorial and survey
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Big Data for Traffic Monitoring and Management
The last two decades witnessed tremendous advances in the Information and
Communications Technologies. Beside improvements in computational power and
storage capacity, communication networks carry nowadays an amount of data which
was not envisaged only few years ago. Together with their pervasiveness,
network complexity increased at the same pace, leaving operators and
researchers with few instruments to understand what happens in the networks,
and, on the global scale, on the Internet. Fortunately, recent advances in data
science and machine learning come to the rescue of network analysts, and allow
analyses with a level of complexity and spatial/temporal scope not possible
only 10 years ago. In my thesis, I take the perspective of an Internet Service
Provider (ISP), and illustrate challenges and possibilities of analyzing the
traffic coming from modern operational networks. I make use of big data and
machine learning algorithms, and apply them to datasets coming from passive
measurements of ISP and University Campus networks. The marriage between data
science and network measurements is complicated by the complexity of machine
learning algorithms, and by the intrinsic multi-dimensionality and variability
of this kind of data. As such, my work proposes and evaluates novel techniques,
inspired from popular machine learning approaches, but carefully tailored to
operate with network traffic