Characterizing and Improving the Reliability of Broadband Internet Access

In this paper, we empirically demonstrate the growing importance of reliability by measuring its effect on user behavior. We present an approach for broadband reliability characterization using data collected by many emerging national initiatives to study broadband and apply it to the data gathered by the Federal Communications Commission's Measuring Broadband America project. Motivated by our findings, we present the design, implementation, and evaluation of a practical approach for improving the reliability of broadband Internet access with multihoming.Comment: 15 pages, 14 figures, 6 table

Systems for characterizing Internet routing

2018 Spring.Includes bibliographical references.Today the Internet plays a critical role in our lives; we rely on it for communication, business, and more recently, smart home operations. Users expect high performance and availability of the Internet. To meet such high demands, all Internet components including routing must operate at peak efficiency. However, events that hamper the routing system over the Internet are very common, causing millions of dollars of financial loss, traffic exposed to attacks, or even loss of national connectivity. Moreover, there is sparse real-time detection and reporting of such events for the public. A key challenge in addressing such issues is lack of methodology to study, evaluate and characterize Internet connectivity. While many networks operating autonomously have made the Internet robust, the complexity in understanding how users interconnect, interact and retrieve content has also increased. Characterizing how data is routed, measuring dependency on external networks, and fast outage detection has become very necessary using public measurement infrastructures and data sources. From a regulatory standpoint, there is an immediate need for systems to detect and report routing events where a content provider's routing policies may run afoul of state policies. In this dissertation, we design, build and evaluate systems that leverage existing infrastructure and report routing events in near-real time. In particular, we focus on geographic routing anomalies i.e., detours, routing failure i.e., outages, and measuring structural changes in routing policies

A Control Plane Enabling Automated and Fully Adaptive Network Traffic Monitoring With eBPF

The extended Berkeley Packet Filter (eBPF) enables the dynamic injection of user-defined processing logic at run-time in the Linux networking stack without disrupting any active monitoring process. This enables the selective extraction of only the traffic features that are needed in a given instant of time, which is what we define fully adaptive network traffic monitoring. However, eBPF programs require ad-hoc control plane routines for each specific scenario in order to orchestrate the underlying data plane and export the required metrics, resulting in potentially duplicated source codes to maintain, and creating the risk of deploying, at runtime, unverified user-defined code that controls the devices running the monitoring process. This paper presents a control plane that automatically adapts both its management tasks and data extraction methodologies based on the underlying data plane provided by the user, who can merely focus on the monitoring logic definition. The paper evaluates the performance of the control plane's modules and demonstrates the advantages, in terms of processing speed and memory consumption, of a fully-adaptive monitoring approach with respect to nProbe (a state-of-the-art solution), an adaptive and a non-adaptive methodology in eBPF. Experiments prove that the control plane monitoring options do not significantly affect the underlying data plane (0.15% degraded throughput) and leverage the most efficient extraction primitives (20x faster execution time). Moreover, the fully-adaptive monitoring leads to a higher number of processed packets (10x) and significantly lower memory occupancy (10x) when extracting the smallest set of features

Exploiting the power of multiplicity: a holistic survey of network-layer multipath

The Internet is inherently a multipath network: For an underlying network with only a single path, connecting various nodes would have been debilitatingly fragile. Unfortunately, traditional Internet technologies have been designed around the restrictive assumption of a single working path between a source and a destination. The lack of native multipath support constrains network performance even as the underlying network is richly connected and has redundant multiple paths. Computer networks can exploit the power of multiplicity, through which a diverse collection of paths is resource pooled as a single resource, to unlock the inherent redundancy of the Internet. This opens up a new vista of opportunities, promising increased throughput (through concurrent usage of multiple paths) and increased reliability and fault tolerance (through the use of multiple paths in backup/redundant arrangements). There are many emerging trends in networking that signify that the Internet's future will be multipath, including the use of multipath technology in data center computing; the ready availability of multiple heterogeneous radio interfaces in wireless (such as Wi-Fi and cellular) in wireless devices; ubiquity of mobile devices that are multihomed with heterogeneous access networks; and the development and standardization of multipath transport protocols such as multipath TCP. The aim of this paper is to provide a comprehensive survey of the literature on network-layer multipath solutions. We will present a detailed investigation of two important design issues, namely, the control plane problem of how to compute and select the routes and the data plane problem of how to split the flow on the computed paths. The main contribution of this paper is a systematic articulation of the main design issues in network-layer multipath routing along with a broad-ranging survey of the vast literature on network-layer multipathing. We also highlight open issues and identify directions for future work

Application of overlay techniques to network monitoring

Measurement and monitoring are important for correct and efficient operation of a network, since these activities provide reliable information and accurate analysis for characterizing and troubleshooting a network’s performance. The focus of network measurement is to measure the volume and types of traffic on a particular network and to record the raw measurement results. The focus of network monitoring is to initiate measurement tasks, collect raw measurement results, and report aggregated outcomes. Network systems are continuously evolving: besides incremental change to accommodate new devices, more drastic changes occur to accommodate new applications, such as overlay-based content delivery networks. As a consequence, a network can experience significant increases in size and significant levels of long-range, coordinated, distributed activity; furthermore, heterogeneous network technologies, services and applications coexist and interact. Reliance upon traditional, point-to-point, ad hoc measurements to manage such networks is becoming increasingly tenuous. In particular, correlated, simultaneous 1-way measurements are needed, as is the ability to access measurement information stored throughout the network of interest. To address these new challenges, this dissertation proposes OverMon, a new paradigm for edge-to-edge network monitoring systems through the application of overlay techniques. Of particular interest, the problem of significant network overheads caused by normal overlay network techniques has been addressed by constructing overlay networks with topology awareness - the network topology information is derived from interior gateway protocol (IGP) traffic, i.e. OSPF traffic, thus eliminating all overlay maintenance network overhead. Through a prototype that uses overlays to initiate measurement tasks and to retrieve measurement results, systematic evaluation has been conducted to demonstrate the feasibility and functionality of OverMon. The measurement results show that OverMon achieves good performance in scalability, flexibility and extensibility, which are important in addressing the new challenges arising from network system evolution. This work, therefore, contributes an innovative approach of applying overly techniques to solve realistic network monitoring problems, and provides valuable first hand experience in building and evaluating such a distributed system

Performance Evaluation And Anomaly detection in Mobile BroadBand Across Europe

With the rapidly growing market for smartphones and user’s confidence for immediate access to high-quality multimedia content, the delivery of video over wireless networks has become a big challenge. It makes it challenging to accommodate end-users with flawless quality of service. The growth of the smartphone market goes hand in hand with the development of the Internet, in which current transport protocols are being re-evaluated to deal with traffic growth. QUIC and WebRTC are new and evolving standards. The latter is a unique and evolving standard explicitly developed to meet this demand and enable a high-quality experience for mobile users of real-time communication services. QUIC has been designed to reduce Web latency, integrate security features, and allow a highquality experience for mobile users. Thus, the need to evaluate the performance of these rising protocols in a non-systematic environment is essential to understand the behavior of the network and provide the end user with a better multimedia delivery service. Since most of the work in the research community is conducted in a controlled environment, we leverage the MONROE platform to investigate the performance of QUIC and WebRTC in real cellular networks using static and mobile nodes. During this Thesis, we conduct measurements ofWebRTC and QUIC while making their data-sets public to the interested experimenter. Building such data-sets is very welcomed with the research community, opening doors to applying data science to network data-sets. The development part of the experiments involves building Docker containers that act as QUIC and WebRTC clients. These containers are publicly available to be used candidly or within the MONROE platform. These key contributions span from Chapter 4 to Chapter 5 presented in Part II of the Thesis. We exploit data collection from MONROE to apply data science over network data-sets, which will help identify networking problems shifting the Thesis focus from performance evaluation to a data science problem. Indeed, the second part of the Thesis focuses on interpretable data science. Identifying network problems leveraging Machine Learning (ML) has gained much visibility in the past few years, resulting in dramatically improved cellular network services. However, critical tasks like troubleshooting cellular networks are still performed manually by experts who monitor the network around the clock. In this context, this Thesis contributes by proposing the use of simple interpretable ML algorithms, moving away from the current trend of high-accuracy ML algorithms (e.g., deep learning) that do not allow interpretation (and hence understanding) of their outcome. We prefer having lower accuracy since we consider it interesting (anomalous) the scenarios misclassified by the ML algorithms, and we do not want to miss them by overfitting. To this aim, we present CIAN (from Causality Inference of Anomalies in Networks), a practical and interpretable ML methodology, which we implement in the form of a software tool named TTrees (from Troubleshooting Trees) and compare it to a supervised counterpart, named STress (from Supervised Trees). Both methodologies require small volumes of data and are quick at training. Our experiments using real data from operational commercial mobile networks e.g., sampled with MONROE probes, show that STrees and CIAN can automatically identify and accurately classify network anomalies—e.g., cases for which a low network performance is not justified by operational conditions—training with just a few hundreds of data samples, hence enabling precise troubleshooting actions. Most importantly, our experiments show that a fully automated unsupervised approach is viable and efficient. In Part III of the Thesis which includes Chapter 6 and 7. In conclusion, in this Thesis, we go through a data-driven networking roller coaster, from performance evaluating upcoming network protocols in real mobile networks to building methodologies that help identify and classify the root cause of networking problems, emphasizing the fact that these methodologies are easy to implement and can be deployed in production environments.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Matteo Sereno.- Secretario: Antonio de la Oliva Delgado.- Vocal: Raquel Barco Moren

NetJobs: A new approach to network monitoring for the Grid using Grid jobs

With grid computing, the far-fl�ung and disparate IT resources act as a single "virtual datacenter". Grid computing interfaces heterogeneous IT resources so they are available when and where we need them. Grid allows us to provision applications and allocate capacity among research and business groups that are geographically and organizationally dispersed. Building a high availability Grid is hold as the next goal to achieve: protecting against computer failures and site failures to avoid downtime of resource and honor Service Level Agreements. Network monitoring has a key role in this challenge. This work is concerning the design and the prototypal implementation of a new approach to Network monitoring for the Grid based on the usage of Grid scheduled jobs. This work was carried out within the Network Support task (SA2) of the Enabling Grids for E-sciencE (EGEE) project. This thesis is organized as follows: Chapter 1: Grid Computing From the origins of Grid Computing to the latest projects. Conceptual framework and main features characterizing many kind of popular grids will be presented. Chapter 2: The EGEE and EGI projects This chapter describes the Enabling Grids for E-sciencE (EGEE) project and the European Grid Infrastructure (EGI). EGEE project (2004-2010) was the�flagship Grid infrastructure project of the EU. The third and last two-year phase of the project (started on 1 May 2008) was financed with a total budget of around 47 million euro, with a further estimated 50 million euro worth of computing resources contributed by the partners. A total manpower of 9,000 Person Months, of which over 4,500 Person Months has been contributed by the partners from their own funding sources. At its close, EGEE represented a worldwide infrastructure of approximately to 200,000 CPU cores, collaboratively hosted by more than 300 centres around the world. By the end of the project, around 13 million jobs were executed on the EGEE grid each month. The new organization, EGI.eu, has then been created to continue the coordination and evolution of the European Grid Infrastructure (EGI) based on EGEE Grid. Chapter3: gLite Middleware Chapter three gives an overview on the gLite Grid Middleware. gLite is the middleware stack for grid computing used by the EGEE and EGI projects with in a very large variety of scientifi�c domains. Born from the collaborative efforts of more than 80 people in 12 different academic and industrial research centers as part of the EGEE Project, gLite provides a complete set of services for building a production grid infrastructure. gLite provides a framework for building grid applications tapping into the power of distributed computing and storage resources across the Internet. The gLite services are currently adopted by more than 250 Computing Centres and used by more than 15000 researchers in Europe and around the world. Chapter 4: Network Activity in EGEE/EGI Grid infrastructures are distributed by nature, involving many sites, normally in different administrative domains. Individual sites are connected together by a network, which is therefore a critical part of the whole Grid infrastructure; without the network there is no Grid. Monitoring is a key component for the successful operation of any infrastructure, helping in the discovery and diagnosis of any problem which may arise. Network monitoring is able to contribute to the day-to-day operations of the Grid by helping to provide answers to specific questions from users and site administrators. This chapter will discuss all the effort lavished by EGEE and EGI in the Grid Network domain. Chapter 5: Grid Network Monitoring based on Grid Jobs Net Jobs is a prototype of a light weight solution for the Grid network monitoring. A job-based approach has been used in order to prove the feasibility of this non intrusive solution. It is currently configured to monitor eight production sites spread from Italy to France but this method could be applied to the vast majority of Grid sites. The prototype provides coherent RTT, MTU, number of hops and TCP achievable bandwidth tests