The research measurement community needs methods and datasets to identify user concentrations and to accurately weight ASes against each other for analyzing measurements' coverage. However, academic researchers traditionally lack visibility into how many users are in each network or how much traffic flows to each network and so often fall back on treating all IP addresses or networks equally. As an alternative, some recent studies have used the APNIC per AS Population Estimates dataset, but it is unvalidated and its methodology is not fully public.

Google, Netflix, Meta, and Akamai serve content to users from offnet servers in thousands of ISPs. These offnets benefit both services and ISPs, via better performance and reduced interdomain and WAN traffic. We argue that this widespread distribution of servers leads to a concentration of traffic and a previously unacknowledged risk, as many ISPs colocate offnets from multiple providers. This trend contributes to many Internet users likely accessing multiple popular services and fetching the majority of their Internet traffic from a single facility -- perhaps even a single rack -- creating shared resources and a correlated risk in cases of failures, attacks, and overload. Alternate ways to access the services often lack sufficient capacity and share resources with more services, creating the potential for cascading failures.

Much of the content and structure of the Web remains inaccessible to evaluate at scale because it is gated by user authentication. This limitation restricts researchers to examining only a superficial layer of a website: the landing page or public, search-indexable pages. Since it is infeasible to create individual accounts across thousands of webpages, we examine the prevalence of Single Sign-On (SSO) on the web to explore the feasibility of using a few accounts to authenticate to many sites. We find that 58% of the top 10K websites with logins are accessible with popular 3rd-party SSO providers, such as Google, Facebook, and Apple, indicating that leveraging SSO offers a scalable solution to access a large volume of user-gated content.

Content delivery networks (CDNs) provide fast service to clients by replicating content at geographically distributed sites. Most CDNs route clients to a particular site using anycast or unicast with DNS-based redirection. We analyze anycast and unicast and explain why neither of them provides both precise control of user-to-site mapping and high availability in the face of failures, two fundamental goals of CDNs. Anycast compromises control (and hence performance), and unicast compromises availability. We then present new hybrid techniques and demonstrate via experiments on the real Internet that these techniques provide both a high level of traffic control and fast failover following site failures.

The impact of Internet phenomena depends on how they impact users, but researchers lack visibility into how to translate Internet events into their impact. Distressingly, the research community seems to have lost hope of obtaining this information without relying on privileged viewpoints. We argue for optimism thanks to new network measurement methods and changes in Internet structure which make it possible to construct an "Internet traffic map". This map would identify the locations of users and major services, the paths between them, and the relative activity levels routed along these paths. We sketch our vision for the map, detail new measurement ideas for map construction, and identify key challenges that the research community should tackle. The realization of an Internet traffic map will be an Internet-scale research effort with Internet-scale impacts that reach far beyond the research community, and so we hope our fellow researchers are excited to join us in addressing this challenge.

This corrigendum corrects and extends our results on the benefit of peer locking in mitigating the propagation of route leaks on the Internet, originally published in [2]. The updated results show even higher benefits of peer locking than originally reported, and an extended analysis covering additional peer locking deployment scenarios shows partial deployments also yield significant reduction in propagation of leaked routes.

Does an outage impact any users? Can a geolocation database known to be good at locating users and bad at infrastructure be trusted for a particular prefix? Is a content-heavy network likely to peer with a particular network? For these questions and many more, knowing which prefixes contain Internet users aids in interpreting Internet analysis. However, existing datasets of Internet activity are out of date, unvalidated, based on privileged data, or too coarse. As a step towards identifying which IP prefixes contain users, we present multiple novel techniques to identify which IP prefixes host web clients without relying on privileged data. Our techniques identify client activity in ASes responsible for 98.8% of Microsoft CDN traffic and in prefixes responsible for 95.2% of Microsoft CDN traffic. Less than 1% of prefixes identified by our technique as active do not contact Microsoft at all. We present measurements of Internet usage worldwide and sketch future directions for extending the techniques to measure relative activity levels across prefixes.

Content Hypergiants deliver the vast majority of Internet traffic to end users. In recent years, some have invested heavily in deploying services and servers inside end-user networks. With several dozen Hypergiants and thousands of servers deployed inside networks, these off-net (meaning outside the Hypergiant networks) deployments change the structure of the Internet. Previous efforts to study them have relied on proprietary data or specialized per-Hypergiant measurement techniques that neither scale nor generalize, providing a limited view of content delivery on today's Internet.

Anycast is used to serve content including web pages and DNS, and anycast deployments are growing. However, prior work examining root DNS suggests anycast deployments incur significant inflation, with users often routed to suboptimal sites. We reassess anycast performance, first extending prior analysis on inflation in the root DNS. We show that inflation is very common in root DNS, affecting more than 95\% of users. However, we then show root DNS latency \emph{hardly matters} to users because caching is so effective. These findings lead us to question: is inflation inherent to anycast, or can inflation be limited when it matters? To answer this question, we consider Microsoft's anycast CDN serving latency-sensitive content. Here, latency matters orders of magnitude more than for root DNS. Perhaps because of this need, only 35\% of CDN users experience any inflation, and the amount they experience is smaller than root DNS. We show that CDN anycast latency has little inflation due to extensive peering and engineering. These results suggest prior claims of anycast inefficiency reflect experiments on a single application rather than anycast's technical potential, and they demonstrate the importance of context when measuring system performance.

The Tier-1 ISPs have been considered the Internet's backbone since the dawn of the modern Internet 30 years ago, as they guarantee global reachability. However, their influence and importance are waning as Internet flattening decreases the demand for transit services and increases the importance of private interconnections. Conversely, major cloud providers -- Amazon, Google, IBM, and Microsoft-- are gaining in importance as more services are hosted on their infrastructures. They ardently support Internet flattening and are rapidly expanding their global footprints, which enables them to bypass the Tier-1 ISPs and other large transit providers to reach many destinations.

Many systems rely on traceroutes to monitor or characterize the Internet. The quality of the systems' inferences depends on the completeness and freshness of the traceroutes, but the refreshing of traceroutes is constrained by limited resources at vantage points. Previous approaches predict which traceroutes are likely out-of-date in order to allocate measurements, or monitor BGP feeds for changes that overlap traceroutes. Both approaches miss many path changes for reasons including the difficulty in predicting changes and the coarse granularity of BGP paths.

The construction of private WANs by cloud providers enables them to extend their networks to more locations and establish direct connectivity with end user ISPs. Tenants of the cloud providers benefit from this proximity to users, which is supposed to provide improved performance by bypassing the public Internet. However, the performance impact of cloud providers' private WANs is not widely understood.To isolate the impact of a private WAN, we measure from globally distributed vantage points to two large cloud providers, comparing performance when using their worldwide WAN and when instead using the public Internet. The benefits are not universal. While 48% of our vantage points saw improved performance when using the WAN, 43% had statistically indistinguishable median performance, and 9% had better performance over the public Internet. We find that the benefits of the private WAN tend to improve with client-to-server distance, but the benefits (or drawbacks) for a particular vantage point depend on specifics of its geographic and network connectivity.

Online services all seek to provide their customers with the best Quality of Experience (QoE) possible. Milliseconds of delay can cause users to abandon a cat video or move onto a different shopping site, which translates into lost revenue. Thus, minimizing latency between users and content is crucial. To reduce latency, content and cloud providers have built massive, global networks. However, their networks must interact with customer ISPs via BGP, which has no concept of performance.

The network communications between the cloud and the client have become the weak link for global cloud services that aim to provide low latency services to their clients. In this paper, we first characterize WAN latency from the viewpoint of a large cloud provider Azure, whose network edges serve hundreds of billions of TCP connections a day across hundreds of locations worldwide. In particular, we focus on instances of latency degradation and design a tool, BlameIt, that enables cloud operators to localize the cause (i.e., faulty AS) of such degradation. BlameIt uses passive diagnosis, using measurements of existing connections between clients and the cloud locations, to localize the cause to one of cloud, middle, or client segments. Then it invokes selective active probing (within a probing budget) to localize the cause more precisely. We validate BlameIt by comparing its automatic fault localization results with that arrived at by network engineers manually, and observe that BlameIt correctly localized the problem in all the 88 incidents. Further, BlameIt issues 72X fewer active probes than a solution relying on active probing alone, and is deployed in production at Azure.

The IPv4 Record Route (RR) Option instructs routers to record their IP addresses in a packet. RR is subject to a nine hop limit and, traditionally, inconsistent support from routers. Recent changes in interdomain connectivity---the so-called "flattening Internet"---and new best practices for how routers should handle RR packets suggest that now is a good time to reassess the potential of the RR Option.

Content delivery networks must balance a number of trade-offs when deciding how to direct a client to a CDN server. Whereas DNS-based redirection requires a complex global traffic manager, anycast depends on BGP to direct a client to a CDN front-end. Anycast is simple to operate, scalable, and naturally resilient to DDoS attacks. This simplicity, however, comes at the cost of precise control of client redirection. We examine the performance implications of using anycast in a global, latency-sensitive, CDN. We analyze millions of client-side measurements from the Bing search service to capture anycast versus unicast performance to nearby front-ends. We find that anycast usually performs well despite the lack of precise control but that it directs roughly 20% of clients to a suboptimal front-end. We also show that the performance of these clients can be improved through a simple history-based prediction scheme.

Data centers must support a range of workloads with differing demands. Although existing approaches handle routine traffic smoothly, intense hotspots--even if ephemeral--cause excessive packet loss and severely degrade performance. This loss occurs even though congestion is typically highly localized, with spare buffer capacity at nearby switches. In this paper, we argue that switches should share buffer capacity to effectively handle this spot congestion without the monetary hit of deploying large buffers at individual switches. Specifically, we present detour-induced buffer sharing (DIBS), a mechanism that achieves a near lossless network without requiring additional buffers at individual switches. Using DIBS, a congested switch detours packets randomly to neighboring switches to avoid dropping the packets. We implement DIBS in hardware, on software routers in a testbed, and in simulation, and we demonstrate that it reduces the 99th percentile of delay-sensitive query completion time by up to 85%, with very little impact on other traffic.

Modern content-distribution networks both provide bulk content and act as "serving infrastructure" for web services in order to reduce user-perceived latency. Serving infrastructures such as Google's are now critical to the online economy, making it imperative to understand their size, geographic distribution, and growth strategies. To this end, we develop techniques that enumerate IP addresses of servers in these infrastructures, find their geographic location, and identify the association between clients and clusters of servers. While general techniques for server enumeration and geolocation can exhibit large error, our techniques exploit the design and mechanisms of serving infrastructure to improve accuracy. We use the EDNS-client-subnet DNS extension to measure which clients a service maps to which of its serving sites. We devise a novel technique that uses this mapping to geolocate servers by combining noisy information about client locations with speed-of-light constraints. We demonstrate that this technique substantially improves geolocation accuracy relative to existing approaches. We also cluster server IP addresses into physical sites by measuring RTTs and adapting the cluster thresholds dynamically. Google's serving infrastructure has grown dramatically in the ten months, and we use our methods to chart its growth and understand its content serving strategy. We find that the number of Google serving sites has increased more than sevenfold, and most of the growth has occurred by placing servers in large and small ISPs across the world, not by expanding Google's backbone.

Today's data centers must support a range of workloads with different demands. While existing approaches handle routine traffic smoothly, ephemeral but intense hotspots cause excessive packet loss and severely degrade performance. This loss occurs even though the congestion is typically highly localized, with spare buffer capacity available at nearby switches.

UHF RFID has emerged as a mature technology with important applications in inventory control, pervasive computing and e-commerce but integration into mobile phone platforms has not yet been fully realized. One of the primary barriers contributing to this is a lack of understanding about how readers will impact the battery life of a mobile smartphone. In this work we describe our experimental investigation into the energy consumption impact of compact UHF Class-1 Generation-2 RFID readers as compared with existing sensors that are common in smartphones. Our evaluation shows that the energy consumption of compact RFID readers is affected by environmental conditions and network parameters that influence reader-tag link quality and that reader energy consumption is comparable to existing integrated Bluetooth, GPS and WiFi interfaces on smartphones. Existing experimental work on UHF readers (e.g., [1]) has focused mainly on tag read performance while our goal is to characterize how various factors impact the energy consumption of compact readers relative to existing smartphone sensors.

Stix is a platform managing emerging large-scale broadband wireless access (BWA) networks. It has been developed to make it easy to manage such networks for community deployments and wireless Internet service providers while keeping the network management infrastructure scalable and flexible. Stix is based on the notions of goal-oriented and in-network management. With Stix, administrators graphically specify network management activities as workflows, which are deployed at a distributed set of agents within the network that cooperate in executing those workflows and storing management information. We implement the Stix system on embedded boards and show that the implementation has a low memory footprint. Using real topology and logging data from a large-scale BWA network operator, we show that Stix is significantly more scalable (via reduction in management traffic) compared to the commonly employed centralized management approach. Finally we use two case studies to demonstrate the ease with which Stix platform can be used for carrying out network reconfiguration and performance management tasks, thereby also showing its potential as a flexible platform to realize self-management mechanisms.

Recurrent applications that mostly run in the background are a significant source of power consumption on battery-limited mobile phones. We highlight the pitfalls of scheduling such applications independently without awareness of each other's schedules. We illustrate the significant energy savings that can be achieved via batch scheduling of recurrent mobile phone applications. We then present our on-going work on developing a general batch scheduling framework for such applications and also outline our early experiences studying the benefit of batch scheduling on two different mobile phone platforms-Nokia N95 and HTC (Android) - that are commonly used in the research community.

We present our work in assisting students who are blind to understand fundamental data structures. We have developed a system called PLUMB EXTRA3 (EXploring data sTRuctures using Audible Algorithm Animation) that conveys an algorithm animation using audio cues and speech. This extends our earlier work on presenting graphs to users who are blind.

We demonstrate the most recent version of our system to communicate graphs and relational information to blind users. We have developed a system called exPLoring graphs at UMB (PLUMB) that displays a drawn graph on a tablet PC and uses auditory cues to help a blind user navigate the graph. This work has applications to assist blind individuals in Computer Science and other educational disciplines, navigation and map manipulation.