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.
Recent work has explored the effect of network factors on user experience with applications, including VoIP @cite_17 , Web @cite_0 and Internet video @cite_43 . Rather than deriving a model for user experience based on multiple factors, our work focuses on the effect of reliability on user demand. Others have started to explore the use of alternative experimental designs to evaluate user experience. @cite_36 apply quasi-experimental design to evaluate the effect of video stream quality on viewer behavior, @cite_23 relies on it for causal analysis of user behavior in social media. @cite_65 explores the effect of contextual factors such as price and competition on user demand. We apply similar methods to understand the effect of service reliability on user behavior.
@cite_36 @cite_65 @cite_0 @cite_43 @cite_23 @cite_17
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Video-on-demand over IP (VOD) is one of the best-known examples of "next-generation" Internet applications cited as a goal by networking and multimedia researchers. Without empirical data, researchers have generally relied on simulated models to drive their design and developmental efforts. In this paper, we present one of the first measurement studies of a large VOD system, using data covering 219 days and more than 150,000 users in a VOD system deployed by China Telecom. Our study focuses on user behavior, content access patterns, and their implications on the design of multimedia streaming systems. Our results also show that when used to model the user-arrival rate, the traditional Poisson model is conservative and overestimates the probability of large arrival groups. We introduce a modified Poisson distribution that more accurately models our observations. We also observe a surprising result, that video session lengths has a weak inverse correlation with the video's popularity. Finally, we gain better understanding of the sources of video popularity through analysis of a number of internal and external factors. 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. The distribution of videos over the Internet is drastically transforming how media is consumed and monetized. Content providers, such as media outlets and video subscription services, would like to ensure that their videos do not fail, startup quickly, and play without interruptions. In return for their investment in video stream quality, content providers expect less viewer abandonment, more viewer engagement, and a greater fraction of repeat viewers, resulting in greater revenues. The key question for a content provider or a CDN is whether and to what extent changes in video quality can cause changes in viewer behavior. Our work is the first to establish a causal relationship between video quality and viewer behavior, taking a step beyond purely correlational studies. To establish causality, we use Quasi-Experimental Designs, a novel technique adapted from the medical and social sciences. We study the impact of video stream quality on viewer behavior in a scientific data-driven manner by using extensive traces from Akamai's streaming network that include 23 million views from 6.7 million unique viewers. We show that viewers start to abandon a video if it takes more than 2 seconds to start up, with each incremental delay of 1 second resulting in a 5.8 increase in the abandonment rate. Further, we show that a moderate amount of interruptions can decrease the average play time of a viewer by a significant amount. A viewer who experiences a rebuffer delay equal to 1 of the video duration plays 5 less of the video in comparison to a similar viewer who experienced no rebuffering. Finally, we show that a viewer who experienced failure is 2.32 less likely to revisit the same site within a week than a similar viewer who did not experience a failure. An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article’s social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc.