Experimental Evaluation of Large Scale WiFi Multicast Rate Control

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality

Multi-stream partitioning and parity rate allocation of scalable video content for efficient IPTV delivery

We address the joint problem of clustering heterogenous clients and allocating scalable video source rate and FEC redundancy in IPTV systems. We propose a streaming solution that delivers varying portions of the scalably encoded content to different client subsets, together with suitably selected parity data. We formulate an optimization problem where the receivers are clustered depending on the quality of their connection so that the average video quality in the IPTV system is maximized. Then we propose a novel algorithm for determining optimally the client clusters, the source and parity rate allocation to each cluster, and the set of serving rates at which the source+parity data is delivered to the clients. We implement our system through a novel design based on scalable video coding that allows for much more efficient network utilization relative to the case of source versioning. Through simulations we demonstrate that the proposed solution substantially outperforms baseline IPTV schemes that multicast the same source and FEC streams to the whole client population, as is commonly done in practice today

Service quality assurance for the IPTV networks

The objective of the proposed research is to design and evaluate end-to-end solutions to support the Quality of Experience (QoE) for the Internet Protocol Television (IPTV) service. IPTV is a system that integrates voice, video, and data delivery into a single Internet Protocol (IP) framework to enable interactive broadcasting services at the subscribers. It promises significant advantages for both service providers and subscribers. For instance, unlike conventional broadcasting systems, IPTV broadcasts will not be restricted by the limited number of channels in the broadcast/radio spectrum. Furthermore, IPTV will provide its subscribers with the opportunity to access and interact with a wide variety of high-quality on-demand video content over the Internet. However, these advantages come at the expense of stricter quality of service (QoS) requirements than traditional Internet applications. Since IPTV is considered as a real-time broadcast service over the Internet, the success of the IPTV service depends on the QoE perceived by the end-users. The characteristics of the video traffic as well as the high-quality requirements of the IPTV broadcast impose strict requirements on transmission delay. IPTV framework has to provide mechanisms to satisfy the stringent delay, jitter, and packet loss requirements of the IPTV service over lossy transmission channels with varying characteristics. The proposed research focuses on error recovery and channel change latency problems in IPTV networks. Our specific aim is to develop a content delivery framework that integrates content features, IPTV application requirements, and network characteristics in such a way that the network resource utilization can be optimized for the given constraints on the user perceived service quality. To achieve the desired QoE levels, the proposed research focuses on the design of resource optimal server-based and peer-assisted delivery techniques. First, by analyzing the tradeoffs on the use of proactive and reactive repair techniques, a solution that optimizes the error recovery overhead is proposed. Further analysis on the proposed solution is performed by also focusing on the use of multicast error recovery techniques. By investigating the tradeoffs on the use of network-assisted and client-based channel change solutions, distributed content delivery frameworks are proposed to optimize the error recovery performance. Next, bandwidth and latency tradeoffs associated with the use of concurrent delivery streams to support the IPTV channel change are analyzed, and the results are used to develop a resource-optimal channel change framework that greatly improves the latency performance in the network. For both problems studied in this research, scalability concerns for the IPTV service are addressed by properly integrating peer-based delivery techniques into server-based solutions.Ph.D

Enabling Multipath and Multicast Data Transmission in Legacy and Future Internet

The quickly growing community of Internet users is requesting multiple applications and services. At the same time the structure of the network is changing. From the performance point of view, there is a tight interplay between the application and the network design. The network must be constructed to provide an adequate performance of the target application. In this thesis we consider how to improve the quality of users' experience concentrating on two popular and resource-consuming applications: bulk data transfer and real-time video streaming. We share our view on the techniques which enable feasibility and deployability of the network functionality leading to unquestionable performance improvement for the corresponding applications. Modern mobile devices, equipped with several network interfaces, as well as multihomed residential Internet hosts are capable of maintaining multiple simultaneous attachments to the network. We propose to enable simultaneous multipath data transmission in order to increase throughput and speed up such bandwidth-demanding applications as, for example, file download. We design an extension for Host Identity Protocol (mHIP), and propose a multipath data scheduling solution on a wedge layer between IP and transport, which effectively distributes packets from a TCP connection over available paths. We support our protocol with a congestion control scheme and prove its ability to compete in a friendly manner against the legacy network protocols. Moreover, applying game-theoretic analytical modelling we investigate how the multihomed HIP multipath-enabled hosts coexist in the shared network. The number of real-time applications grows quickly. Efficient and reliable transport of multimedia content is a critical issue of today's IP network design. In this thesis we solve scalability issues of the multicast dissemination trees controlled by the hybrid error correction. We propose a scalable multicast architecture for potentially large overlay networks. Our techniques address suboptimality of the adaptive hybrid error correction (AHEC) scheme in the multicast scenarios. A hierarchical multi-stage multicast tree topology is constructed in order to improve the performance of AHEC and guarantee QoS for the multicast clients. We choose an evolutionary networking approach that has the potential to lower the required resources for multimedia applications by utilizing the error-correction domain separation paradigm in combination with selective insertion of the supplementary data from parallel networks, when the corresponding content is available. Clearly both multipath data transmission and multicast content dissemination are the future Internet trends. We study multiple problems related to the deployment of these methods.Internetin nopeasti kasvava käyttäjäkunta vaatii verkolta yhä enemmän sovelluksia ja palveluita. Samaan aikaan verkon rakenne muuttuu. Suorituskyvyn näkökulmasta on olemassa selvä vuorovaikutussovellusten ja verkon suunnittelun välillä. Verkko on rakennettava siten, että se pystyy takaamaan riittävän suorituskyvyn halutuille palveluille. Tässä väitöskirjassa pohditaan, miten verkon käyttökokemusta voidaan parantaa keskittyen kahteen suosittuun ja resursseja vaativaan sovellukseen: tiedonsiirtoon ja reaaliaikaiseen videon suoratoistoon. Esitämme näkemyksemme tekniikoista, jotka mahdollistavat tarvittavien verkkotoiminnallisuuksien helpon toteuttavuuden sekä kiistatta parantavat sovelluksien suorityskykyä. Nykyaikaiset mobiililaitteet monine verkkoyhteyksineen, kuten myös kotitietokoneet, pystyvät ylläpitämään monta internet-yhteyttä samanaikaisesti. Siksi ehdotamme monikanavaisen tiedonsiirron käyttöä suorituskyvyn parantamiseksi ja etenkin vaativien verkkosovelluksien, kuten tiedostonsiirron, nopeuttamiseksi. Tässä väitöskirjassa suunnitellaan Host Identity Protocol (mHIP) -laajennus, sekä esitetään tiedonsiirron vuorotteluratkaisu, joka hajauttaa TCP-yhteyden tiedonsiirtopaketit käytettävissä oleville kanaville. Protokollamme tueksi luomme myös ruuhkautumishallinta-algoritmin ja näytämme sen pystyvän toimimaan yhteen nykyisien verkkoprotokollien kanssa. Tämän lisäksi tutkimme peliteoreettista mallinnusta käyttäen, miten monikanavaiset HIP-verkkopäätteet toimivat muiden kanssa jaetuissa verkoissa. Reaaliaikaisten sovellusten määrä kasvaa nopeasti. Tehokas ja luotettava multimediasisällön siirto on olennainen vaatimus nykypäivän IP-verkoissa. Tässä työssä ratkaistaan monilähetyksen (multicast) jakelustruktuurin skaalautuvuuteen liittyviä ongelmia. Ehdotamme skaalautuvaa monilähetysarkkitehtuuria suurille peiteverkoille. Ratkaisumme puuttuu adaptiivisen virhekorjauksen (Adaptive Hybrid Error Correction, AHEC) alioptimaalisuuteen monilähetystilanteissa. Luomme hierarkisen monivaiheisen monilähetyspuutopologian parantaaksemme AHECin suorituskykyä, sekä taataksemme monilähetysasiakkaiden palvelun laadun. Valitsimme evoluutiomaisen lähestymistavan, jolla on potentiaalia keventää multimediasovelluksien verkkoresurssivaatimuksia erottamalla virhekorjauksen omaksi verkkotunnuksekseen, sekä käyttämällä valikoivaa täydentävää tiedonlisäystä rinnakkaisverkoista vastaavan sisällön ollessa saatavilla. Sekä monikanava- että monilähetystiedonsiirto ovat selvästi osa internetin kehityssuuntaa. Tässä väitöskirjassa tutkimme monia ongelmia näiden tekniikoiden käyttöönottoon liittyen

Measurement-Driven Algorithm and System Design for Wireless and Datacenter Networks

The growing number of mobile devices and data-intensive applications pose unique challenges for wireless access networks as well as datacenter networks that enable modern cloud-based services. With the enormous increase in volume and complexity of traffic from applications such as video streaming and cloud computing, the interconnection networks have become a major performance bottleneck. In this thesis, we study algorithms and architectures spanning several layers of the networking protocol stack that enable and accelerate novel applications and that are easily deployable and scalable. The design of these algorithms and architectures is motivated by measurements and observations in real world or experimental testbeds. In the first part of this thesis, we address the challenge of wireless content delivery in crowded areas. We present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast. AMuSe is based on accurate receiver feedback and incurs a small control overhead. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes which periodically send information about the channel quality to the multicast sender. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe's feedback to optimally tune the physical layer multicast rate. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. We implemented the AMuSe system on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi nodes. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions. Further, 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. As an example application, MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality. Next, we specifically focus on ensuring high Quality of Experience (QoE) for video streaming over WiFi multicast. We formulate the problem of joint adaptation of multicast transmission rate and video rate for ensuring high video QoE as a utility maximization problem and propose an online control algorithm called DYVR which is based on Lyapunov optimization techniques. We evaluated the performance of DYVR through analysis, simulations, and experiments using a testbed composed of Android devices and o the shelf APs. Our evaluation shows that DYVR can ensure high video rates while guaranteeing a low but acceptable number of segment losses, buffer underflows, and video rate switches. We leverage the lessons learnt from AMuSe for WiFi 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. DyMo employs eMBMS for broadcasting instructions which indicate the reporting rates as a function of the observed Quality of Service (QoS) for each UE. This simple feedback mechanism collects very limited QoS reports which can be used for network optimization. We evaluated the performance of DyMo analytically and via simulations. DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. In the second part of the thesis, we study datacenter networks which are key enablers of the end-user applications such as video streaming and storage. Datacenter applications such as distributed file systems, one-to-many virtual machine migrations, and large-scale data processing involve bulk multicast flows. We propose a hardware and software system for enabling physical layer optical multicast in datacenter networks using passive optical splitters. We built a prototype and developed a simulation environment to evaluate the performance of the system for bulk multicasting. Our evaluation shows that the optical multicast architecture can achieve higher throughput and lower latency than IP multicast and peer-to-peer multicast schemes with lower switching energy consumption. Finally, we study the problem of congestion control in datacenter networks. Quantized Congestion Control (QCN), a switch-supported standard, utilizes direct multi-bit feedback from the network for hardware rate limiting. Although QCN has been shown to be fast-reacting and effective, being a Layer-2 technology limits its adoption in IP-routed Layer 3 datacenters. We address several design challenges to overcome QCN feedback's Layer- 2 limitation and use it to design window-based congestion control (QCN-CC) and load balancing (QCN-LB) schemes. Our extensive simulations, based on real world workloads, demonstrate the advantages of explicit, multi-bit congestion feedback, especially in a typical environment where intra-datacenter traffic with short Round Trip Times (RTT: tens of s) run in conjunction with web-facing traffic with long RTTs (tens of milliseconds)

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