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

Adaptive Streaming: From Bitrate Maximization to Rate-Distortion Optimization

The fundamental conflict between the increasing consumer demand for better Quality-of-Experience (QoE) and the limited supply of network resources has become significant challenges to modern video delivery systems. State-of-the-art adaptive bitrate (ABR) streaming algorithms are dedicated to drain available bandwidth in hope to improve viewers' QoE, resulting in inefficient use of network resources. In this thesis, we develop an alternative design paradigm, namely rate-distortion optimized streaming (RDOS), to balance the contrast demands from video consumers and service providers. Distinct from the traditional bitrate maximization paradigm, RDOS must operate at any given point along the rate-distortion curve, as specified by a trade-off parameter. The new paradigm has found plausible explanations in information theory, economics, and visual perception. To instantiate the new philosophy, we decompose adaptive streaming algorithms into three mutually independent components, including throughput predictor, reward function, and bitrate selector. We provide a unified framework to understand the connections among all existing ABR algorithms. The new perspective also illustrates the fundamental limitations of each algorithm by going behind its underlying assumptions. Based on the insights, we propose novel improvements to each of the three functional components. To alleviate a series of unrealistic assumptions behind bitrate-based QoE models, we develop a theoretically-grounded objective QoE model. The new objective QoE model combines the information from subject-rated streaming videos and the prior knowledge about human visual system (HVS) in a principled way. By analyzing a corpus of psychophysical experiments, we show the QoE function estimation can be formulated as a projection onto convex sets problem. The proposed model presents strong generalization capability over a broad range of source contents, video encoders, and viewing conditions. Most importantly, the QoE model disentangles bitrate with quality, making it an ideal component in the RDOS framework. In contrast to the existing throughput estimators that approximate the marginal probability distribution over all connections, we optimize the throughput predictor conditioned on each client. Although there are lack of training data for each Internet Protocol connection, we can leverage the latest advances in meta learning to incorporate the knowledge embedded in similar tasks. With a deliberately designed objective function, the algorithm learns to identify similar structures among different network characteristics from millions of realistic throughput traces. During the test phase, the model can quickly adapt to connection-level network characteristics with only a small amount of training data from novel streaming video clients with a small number of gradient steps. The enormous space of streaming videos, constantly progressing encoding schemes, and great diversity of throughput characteristics make it extremely challenging for modern data-driven bitrate selectors that are trained with limited samples to generalize well. To this end, we propose a Bayesian bitrate selection algorithm by adaptively fusing an online, robust, and short-term optimal controller with an offline, susceptible, and long-term optimal planner. Depending on the reliability of the two controllers in certain system states, the algorithm dynamically prioritizes the one of the two decision rules to obtain the optimal decision. To faithfully evaluate the performance of RDOS, we construct a large-scale streaming video dataset -- the Waterloo Streaming Video database. It contains a wide variety of high quality source contents, encoders, encoding profiles, realistic throughput traces, and viewing devices. Extensive objective evaluation demonstrates the proposed algorithm can deliver identical QoE to state-of-the-art ABR algorithms at a much lower cost. The improvement is also supported by so-far the largest subjective video quality assessment experiment

Deep neural mobile networking

The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devices that have diverse performance requirements in terms of throughput, latency, and reliability. This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering. In this context, embedding machine intelligence into mobile networks becomes necessary, as this enables systematic mining of valuable information from mobile big data and automatically uncovering correlations that would otherwise have been too difficult to extract by human experts. In particular, deep learning based solutions can automatically extract features from raw data, without human expertise. The performance of artificial intelligence (AI) has achieved in other domains draws unprecedented interest from both academia and industry in employing deep learning approaches to address technical challenges in mobile networks. This thesis attacks important problems in the mobile networking area from various perspectives by harnessing recent advances in deep neural networks. As a preamble, we bridge the gap between deep learning and mobile networking by presenting a survey on the crossovers between the two areas. Secondly, we design dedicated deep learning architectures to forecast mobile traffic consumption at city scale. In particular, we tailor our deep neural network models to different mobile traffic data structures (i.e. data originating from urban grids and geospatial point-cloud antenna deployments) to deliver precise prediction. Next, we propose a mobile traffic super resolution (MTSR) technique to achieve coarse-to-fine grain transformations on mobile traffic measurements using generative adversarial network architectures. This can provide insightful knowledge to mobile operators about mobile traffic distribution, while effectively reducing the data post-processing overhead. Subsequently, the mobile traffic decomposition (MTD) technique is proposed to break the aggregated mobile traffic measurements into service-level time series, by using a deep learning based framework. With MTD, mobile operators can perform more efficient resource allocation for network slicing (i.e, the logical partitioning of physical infrastructure) and alleviate the privacy concerns that come with the extensive use of deep packet inspection. Finally, we study the robustness of network specific deep anomaly detectors with a realistic black-box threat model and propose reliable solutions for defending against attacks that seek to subvert existing network deep learning based intrusion detection systems (NIDS). Lastly, based on the results obtained, we identify important research directions that are worth pursuing in the future, including (i) serving deep learning with massive high-quality data (ii) deep learning for spatio-temporal mobile data mining (iii) deep learning for geometric mobile data mining (iv) deep unsupervised learning in mobile networks, and (v) deep reinforcement learning for mobile network control. Overall, this thesis demonstrates that deep learning can underpin powerful tools that address data-driven problems in the mobile networking domain. With such intelligence, future mobile networks can be monitored and managed more effectively and thus higher user quality of experience can be guaranteed

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Automating SLA enforcement in the cloud computing

Cloud computing is playing an increasingly important role, not only by facilitating digital trading platforms but also by transforming conventional services from client-server models to cloud computing. This domain has given the global economic and technological benefits, it offers to both the service providers and service subscribers. Digital marketplaces are no longer limited only to trade tangible commodities but also facilitates enormous service virtualization across various industries. Software as a Service (SaaS) being the largest service segment, dominates the global cloud migration. Infrastructure as a Service (IaaS) and cloud-based application development also known as Platform as a Service (PaaS) are also next-generation computing platforms for their ultimate futuristic demand by both, public and private sector. These service segments are now hosted on cloud platforms to compute, store, and network, an enormous amount of service requests, which process data incredibly fast and economically. Organizations also perform data analytics and other similar computing amenities to manage their business without maintaining on-premise computing infrastructures which are hard to maintain. This computing capability has extensively improved the popularity and increased the demand for cloud services to an extent, that businesses worldwide are heavily migrating their computing resources to these platforms. Diverse cloud service providers take the responsibility of provisioning such cloud-based services for subscribers. In return, a certain subscription fee is charged to them periodically and depending upon the service package, availability and security. On the flip side, such intensive technology shift and outsourcing reliance have also introduced scenarios that any failure on their part leads to serious consequences to the business community at large. In recent years technology industry has observed critical and increased service outages at various cloud service providers(CSP) such as Amazon AWS, Microsoft, Google, which ultimately interrupts the entire supply chain and causes several well-known web services to be taken offline either due to a human error, failed change control implementation or in more recently due to targeted cyber-attacks like DDoS. These web-based solutions such as compute, storage, network or other similar services are provisioned to cloud service subscribers (CSS) platforms. Regardless of a cloud service deployment, a legal binding such as a Service Level Agreement (SLA) is signed between the CSP and CSS. The SLA holds a service scope and guarantees in case of failure. There are probabilities where these SLA may be violated, revoked, or dishonoured by either party, mostly the CSP. An SLA violation along with an unsettled dispute leads to some financial losses for the service subscribers or perhaps cost them their business reputation. Eventually, the subscriber may request some form of compensation from the provider such as a service credit or a refund. In either case, the burden of proof lies with the subscribers, who have to capture and preserve those data or forensically sound system or service logs, supporting their claims. Most of the time, this is manually processed, which is both expensive and time-consuming. To address this problem, this research first analyses the gaps in existing arrangements. It then suggests automation of SLA enforcement within cloud environments and identifies the main properties of a solution to the problem covering various other avenues associated with the other operating environments. This research then subsequently proposes architectures, based on the concept of fair exchange, and shows that how intelligently the approach enforces cloud SLA using various techniques. Furthermore, by extending the research scope covering two key scenarios (a) when participants are loss averse and (b) when interacting participants can act maliciously. Our proposed architectures present robust schemes by enforcing the suggested solutions which are effective, efficient, and most importantly resilient to modern-day security and privacy challenges. The uniqueness of our research is that it does not only ensure the fairness aspect of digital trading but it also extends and logically implements a dual security layer throughout the service exchange. Using this approach protects business participants by securely automating the dispute resolutions in a more resilient fashion. It also shields their data privacy and security from diverse cyber challenges and other operational failures. These architectures are capable of imposing state-of-the-art defences through integrated secure modules along with full encryption schemes, mitigating security gaps previously not dealt with, based upon fair exchange protocols. The Protocol also accomplishes achieving service exchange scenarios either with or without dispute resolution. Finally, our proposed architectures are automated and interact with hardcoded procedures and verifications mechanism using a variant of trusted third parties and trusted authorities, which makes it difficult to cause potential disagreements and misbehaviours during a cloud-based service exchange by enforcing SLA