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.
Previous work on broadband networks has often focused on characterizing services in terms of performance (e.g., link capacity and latency) from a range of platforms and vantage points, including customized home gateways @cite_55 @cite_45 , applications in end-user devices @cite_53 @cite_57 @cite_40 @cite_2 , Web-based tests @cite_63 @cite_7 @cite_28 , and well-provisioned measurement nodes outside the access networks @cite_26 . We use longitudinal data collected by two of these efforts, the FCC's MBA initiative and Namehelp @cite_66 , to study broadband reliability.
@cite_26 @cite_7 @cite_28 @cite_55 @cite_53 @cite_57 @cite_40 @cite_45 @cite_63 @cite_2 @cite_66
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Mobile broadband networks play an increasingly important role in society, and there is a strong need for independent assessments of their robustness and performance. A promising source of such information is active end-to-end measurements. It is, however, a challenging task to go from individual measurements to an assessment of network reliability, which is a complex notion encompassing many stability and performance related metrics. This paper presents a framework for measuring the user-experienced reliability in mobile broadband networks. We argue that reliability must be assessed at several levels, from the availability of the network connection to the stability of application performance. Based on the proposed framework, we conduct a large-scale measurement study of reliability in 5 mobile broadband networks. The study builds on active measurements from hundreds of measurement nodes over a period of 10 months. The results show that the reliability of mobile broadband networks is lower than one could hope: more than 20 of connections from stationary nodes are unavailable more than 10 minutes per day. There is, however, a significant potential for improving robustness if a device can connect simultaneously to several networks. We find that in most cases, our devices can achieve 99.999 ("five nines") connection availability by combining two operators. We further show how both radio conditions and network configuration play important roles in determining reliability, and how external measurements can reveal weaknesses and incidents that are not always captured by the operators' existing monitoring tools. We present the first study of broadband services in their broader context, evaluating the impact of service characteristics (such as capacity, latency and loss), their broadband pricing and user demand. We explore these relationships, beyond correlation, with the application of natural experiments. Most efforts on broadband service characterization have so far focused on performance and availability, yet we lack a clear understanding of how such services are being utilized and how their use is impacted by the particulars of the market. By analyzing over 23-months of data collected from 53,000 end hosts and residential gateways in 160 countries, along with a global survey of retail broadband plans, we empirically study the relationship between broadband service characteristics, pricing and demand. We show a strong correlation between capacity and demand, even though subscribers rarely fully utilize their links, but note a law of diminishing returns with relatively smaller increases in demand at higher capacities. Despite the fourfold increase in global IP traffic, we find that user demand on the network over a three year period remained constant for a given bandwidth capacity. We exploit natural experiments to examine the causality between these factors. The reported findings represent an important step towards understanding how user behavior, and the market features that shape it, affect broadband networks and the Internet at large. A large and rapidly growing proportion of users connect to the Internet via residential broadband networks such as Digital Subscriber Lines (DSL) and cable. Residential networks are often the bottleneck in the last mile of today's Internet. Their characteristics critically affect Internet applications, including voice-over-IP, online games, and peer-to-peer content sharing delivery systems. However, to date, few studies have investigated commercial broadband deployments, and rigorous measurement data that characterize these networks at scale are lacking. In this paper, we present the first large-scale measurement study of major cable and DSL providers in North America and Europe. We describe and evaluate the measurement tools we developed for this purpose. Our study characterizes several properties of broadband networks, including link capacities, packet round-trip times and jitter, packet loss rates, queue lengths, and queue drop policies. Our analysis reveals important ways in which residential networks differ from how the Internet is conventionally thought to operate. We also discuss the implications of our findings for many emerging protocols and systems, including delay-based congestion control (e.g., PCP) and network coordinate systems (e.g., Vivaldi). With broadband availability now approaching universal service in the United States, the focus on broadband metrics is shifting toward the assessment of broadband performance. The initial focus has been on the measurement and reporting of the speeds achieved by broadband services. As we develop more comprehensive and, hopefully, more meaningful frameworks for characterizing, measuring, and evaluating broadband performance, one obvious measure of merit will be reliability. In a survey, consumers identified concerns about reliability as second only to speed in importance. Moreover, as our collective reliance on broadband as critical socio-economic infrastructure increases, and as a growing range of services move into the Internet, it is reasonable to expect that demand for reliable broadband service will increase. At the same time, the growth of streaming and interactive rich multimedia, the convergence of fixed and mobile services, and the evolution of the Internet services industry pose new challenges for developing appropriate reliability metrics. While the measurement of broadband availability or speeds and the appropriate interpretation of measurement data are not simple tasks, evaluating reliability is inherently much more complex and difficult. Research on performance, robustness, and evolution of the global Internet is fundamentally handicapped without accurate and thorough knowledge of the nature and structure of the contractual relationships between Autonomous Systems (ASs). In this work we introduce novel heuristics for inferring AS relationships. Our heuristics improve upon previous works in several technical aspects, which we outline in detail and demonstrate with several examples. Seeking to increase the value and reliability of our inference results, we then focus on validation of inferred AS relationships. We perform a survey with ASs' network administrators to collect information on the actual connectivity and policies of the surveyed ASs. Based on the survey results, we find that our new AS relationship inference techniques achieve high levels of accuracy: we correctly infer 96.5 customer to provider (c2p), 82.8 peer to peer (p2p), and 90.3 sibling to sibling (s2s) relationships. We then cross-compare the reported AS connectivity with the AS connectivity data contained in BGP tables. We find that BGP tables miss up to 86.2 of the true adjacencies of the surveyed ASs. The majority of the missing links are of the p2p type, which highlights the limitations of present measuring techniques to capture links of this type. Finally, to make our results easily accessible and practically useful for the community, we open an AS relationship repository where we archive, on a weekly basis, and make publicly available the complete Internet AS-level topology annotated with AS relationship information for every pair of AS neighbors. The performance of Internet services is intrinsically tied to propagation delays between end points (i.e., network latency). Standard active probe-based or passive host-based methods for measuring end-to-end latency are difficult to deploy at scale and typically offer limited precision and accuracy. In this paper, we investigate a novel but non-obvious source of latency measurement---logs from network time protocol (NTP) servers. Using NTP-derived data for studying latency is compelling due to NTP's pervasive use in the Internet and its inherent focus on accurate end-to-end delay estimation. We consider the efficacy of an NTP-based approach for studying propagation delays by analyzing logs collected from 10 NTP servers distributed across the United States. These logs include over 73M latency measurements to 7.4M worldwide clients (as indicated by unique IP addresses) collected over the period of one day. Our initial analysis of the general characteristics of propagation delays derived from the log data reveals that delay measurements from NTP must be carefully filtered in order to extract accurate results. We develop a filtering process that removes measurements that are likely to be inaccurate. After applying our filter to NTP measurements, we report on the scope and reach for US-based clients and the characteristics of the end-to-end latency for those clients. We present a measurement study of wireless experience in a diverse set of home environments by deploying an infrastructure, we call WiSe. Our infrastructure consists of OpenWrt-based Access Points (APs) that have been given away to residents for free to be installed as their primary wireless access mechanism. These APs are configured with our specialized measurement and monitoring software that communicates with our measurement controller through an open API. We have collected wireless performance traces from 30 homes for a period in excess of 6 months. To analyze the characteristics of these home wireless environments, we have also developed a simple metric that estimates the likely TCP throughput different clients can expect based on current channel and environmental conditions. With this infrastructure, we provide multiple quantitative observations, some of which are anecdotally understood in our community. For example, while a majority of links performed well most of the time, we observed cases of poor client experience about 2.1 of the total time. In analyzing the point-to-point wireless channel, insights about two qualitatively different operating regimes-bandwidth and power-limited-have proven indispensable in the design of good communication schemes. In this paper, we propose a new scaling law formulation for wireless networks that allows us to develop a theory that is analogous to the point-to-point case. We identify fundamental operating regimes of wireless networks and derive architectural guidelines for the design of optimal schemes. Our analysis shows that in a given wireless network with arbitrary size, area, power, bandwidth, etc., there are three parameters of importance: the short-distance signal-to-noise ratio (SNR), the long-distance SNR, and the power path loss exponent of the environment. Depending on these parameters, we identify four qualitatively different regimes. One of these regimes is especially interesting since it is fundamentally a consequence of the heterogeneous nature of links in a network and does not occur in the point-to-point case; the network capacity is both power and bandwidth limited. This regime has thus far remained hidden due to the limitations of the existing formulation. Existing schemes, either multihop transmission or hierarchical cooperation, fail to achieve capacity in this regime; we propose a new hybrid scheme that achieves capacity. Measuring real-time end-to-end network path performance metrics is important for several distributed applications such as media streaming systems (e.g., for switching to paths with higher bandwidth and lower jitter) and content distribution systems (e.g., for selecting servers with lower latency). However, it is challenging to perform such end-to-end pairwise measurements in large distributed systems while achieving high accuracy and avoid interfering with existing traffic. On the end hosts, the measurements can overload the machine by causing interference among themselves and other processes. On the network, the measurement packets from different hosts can interfere among themselves and with other flows on bottleneck links. In this paper, we propose a system to monitor end-host and network resources and adapt the number of measurements according to the observed load. Our scheme avoids interference by measuring only a small subset of network paths and reconstructing the entire network path properties from the partial, indirect measurements. Our simulation experiments and real testbed experiments on PlanetLab show that our path selection algorithm working with resource constraints does not adversely affect the accuracy of inference and our system can effectively adapt to the changing resource usage at the end hosts. We develop a new metric for quantifying end-to-end throughput in multihop wireless networks, which we term random access transport capacity, since the interference model presumes uncoordinated transmissions. The metric quantifies the average maximum rate of successful end-to-end transmissions, multiplied by the communication distance, and normalized by the network area. We show that a simple upper bound on this quantity is computable in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops and optimal per hop success probability and show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants, which contain most of the design-relevant network parameters. Numerical results demonstrate that the upper bound is accurate for the purpose of determining the optimal hop count and success (or outage) probability. A crucial problem for the efficient design and management of integrated services networks is how to best allocate network resources for heterogeneous and bursty traffic streams in multiplexers that support prioritized service disciplines. In this paper, we introduce a new approach for determining per-connection performance parameters such as delay-bound violation probability and loss probability in multi-service networks. The approach utilizes a traffic characterization consisting of the variances of a stream's rate distribution over multiple interval lengths, which captures its burstiness properties and autocorrelation structure. From this traffic characterization, we provide a simple and efficient resource allocation algorithm by deriving stochastic delay-bounds for static priority schedulers and employing a Gaussian approximation over intervals. To evaluate the scheme, we perform trace-driven simulation experiments with long traces of MPEG-compressed video and show that our approach is accurate enough to capture most of the inherent statistical multiplexing gain, achieving average network utilizations of up to 90 for these traces and substantially outperforming previous "effective bandwidth" techniques.