Replicating content across a geographically distributed set of servers and redirecting clients to the closest server in terms of latency has emerged as a common paradigm for improving client performance. In this paper, we analyze latencies measured from servers in Google’s content distribution network (CDN) to clients all across the Internet to study the effectiveness of latency-based server selection. Our main result is that redirecting every client to the server with least latency does not suffice to optimize client latencies. First, even though most clients are served by a geographically nearby CDN node, a sizeable fraction of clients experience latencies several tens of milliseconds higher than other clients in the same region. Second, we find that queueing delays often override the benefits of a client interacting with a nearby server. To help the administrators of Google’s CDN cope with these

ABSTRACT – The latency between machines on the Internet can dramatically affect users ’ experience for many distributed applications. Particularly, in multiplayer online games, players seek to cluster themselves so that those in the same session have low latency to each other. A system that predicts latencies between machine pairs allows such matchmaking to consider many more machine pairs than can be probed in a scalable fashion while users are waiting. Using a far-reaching trace of latencies between players on over 3.5 million game consoles, we designed Htrae, a latency prediction system for game matchmaking scenarios. One novel feature of Htrae is its synthesis of geolocation with a network coordinate system. It uses geolocation to select reasonable initial network coordinates for new machines joining the system, allowing it to converge more quickly than standard network coordinate systems and produce substantially lower prediction error than state-of-the-art latency prediction systems. For instance, it produces 90th percentile errors less than half those of iPlane and Pyxida. Our design is general enough to make it a good fit for other latency-sensitive peer-topeer applications besides game matchmaking.

Existing empirical studies of Internet structure and path properties indicate that the Internet is tree-like. This work quantifies the degree to which at least two important Internet measures—latency and bandwidth—approximate tree metrics. We evaluate our ability to model end-to-end measures using tree embeddings by actually building tree representations. In addition to being simple and intuitive models, these trees provide a range of commonly-required functionality beyond serving as an analytical tool. The contributions of our study are twofold. First, we investigate the ability to portray the inherent hierarchical structure of the Internet using the most pure and compact topology, trees. Second, we evaluate the ability of our compact representation to facilitate many natural tasks, such as the selection of servers with short latency or high bandwidth from a client. Experiments show that these tasks can be done with high degree of success and modest overhead.

The topology of the Internet has been extensively studied in recent years, driving a need for increasingly complex measurement infrastructures. These measurements have produced detailed topologies with steadily increasing temporal resolution, but concerns exist about the ability of active measurement to measure the true Internet topology. Difficulties in ensuring the accuracy of every individual measurement when millions of measurements are made daily, and concerns about the bias that might result from measurement along the tree of routes from each vantage point to the wider reaches of the Internet must be addressed. However, early discussions of these concerns were based mostly on synthetic data, oversimplified models or data with limited or biased observer distributions. In this paper, we show the importance that extensive sampling from a broad distribution of vantage points has on the resulting topology and bias. We present two methods for designing and analyzing the topology coverage by vantage points: one, when system-wide knowledge exists, provides a near-optimal assignment of measurements to vantage points; while the second one is suitable for an oblivious system and is purely probabilistic. The majority of the paper is devoted to a first look at the importance of the distribution’s quality. We show that diversity in the locations and types of vantage points is required for obtaining an unbiased topology. We analyze the effect that broad distribution has over the convergence of various autonomous systems topology characteristics. We show that although diverse and broad distribution is not required for all inspected properties, it is required for some. Finally, some recent bias claims that were made against active traceroute sampling are revisited, and we empirically show that diverse and broad distribution can question their conclusions.

Current large-scale topology mapping systems require multiple days to characterize the Internet due to the large amount of probing traffic they incur. The accuracy of maps from existing systems is unknown, yet empirical evidence suggests that additional fine-grained probing exposes hidden links and temporal dynamics. Through longitudinal analysis of data from the Archipelago and iPlane systems, in conjunction with our own active probing, we examine how to shorten Internet topology mapping cycle time. In particular, this work develops discriminatory primitives that maximize topological fidelity while being efficient. We propose and evaluate adaptive probing techniques that leverage external knowledge (e.g., common subnetting structures) and data from prior cycle(s) to guide the selection of probed destinations and the assignment of destinations to vantage points. Our Interface Set Cover (ISC) algorithm generalizes previous dynamic probing work. Crucially, ISC runs across probing cycles to minimize probing while detecting load balancing and reacting to topological changes. To maximize the information gain of each trace, our Subnet Centric Probing technique selects destinations more likely to expose their network’s internal structure. Finally, the Vantage Point Spreading algorithm uses network knowledge to increase path diversity to destination ingress points.

Internet Service Providers typically do not reveal details of their interdomain routing policies due to security concerns, or for commercial or legal reasons. As a result, it is difficult to hold ISPs accountable for their contractual agreements. Existing solutions can check basic properties, e.g., whether route announcements correspond to valid routes, but they do not verify how these routes were chosen. In essence, today’s Internet forces us to choose between per-AS privacy and verifiability. In this paper, we argue that making this difficult tradeoff is unnecessary. We propose private and verifiable routing (PVR), a technique that enables ISPs to check whether their neighbors are fulfilling their contractual promises to them, and to obtain evidence of any violations, without disclosing information that the routing protocol does not already reveal. As initial evidence that PVR is feasible, we sketch a PVR system that can verify some simple BGP policies. We conclude by highlighting several research challenges as future work.

An Internet hitlist is a set of addresses that cover and can represent the the Internet as a whole. Hitlists have long been used in studies of Internet topology, reachability, and performance, serving as the destinations of traceroute or performance probes. Most early topology studies used manually generated lists of prominent addresses, but evolution and growth of the Internet make human maintenance untenable. Random selection scales to today’s address space, but most random addresses fail to respond. In this paper we present what we believe is the first automatic generation of hitlists informed censuses of Internet addresses. We formalize the desirable characteristics of a hitlist: responsiveness, each representative responds to pings; completeness, they cover all the allocated IPv4 address space; and stability, list evolution is minimized when possible. We quantify the accuracy of our automatic hitlists, showing that only one-third of the Internet allows informed selection of representatives. Of informed representatives, 50–60 % are likely to respond three months later, and we show that causes for non-responses are likely due to dynamic addressing (so no stable representative exists) or firewalls. In spite of these limitations, we show that the use of informed hitlists can add 1.7 million edge links (a 5 % growth) to traceroute-based Internet topology studies Our hitlists are available free-of-charge and are in use by several other research projects. Categories andSubjectDescriptors

Existing secure interdomain routing protocols can verify validity properties about individual routes, such as whether they correspond to a real network path. It is often useful to verify more complex properties relating to the route decision procedure – for example, whether the chosen route was the best one available, or whether it was consistent with the network’s peering agreements. However, this is difficult to do without knowing a network’s routing policy and full routing state, which are not normally disclosed. In this paper, we show how a network can allow its peers to verify a number of nontrivial properties of its interdomain routing decisions without revealing any additional information. If all the properties hold, the peers learn nothing beyond what the interdomain routing protocol already reveals; if a property does not hold, at least one peer can detect this and prove the violation. We present SPIDeR, a practical system that applies this approach to the Border Gateway Protocol, and we report results from an experimental evaluation to demonstrate that SPIDeR has a reasonable overhead.

This paper presents the design and implementation of Application-Aware Anonymity (A 3), an extensible platform for deploying anonymity-based services on the Internet. A 3 allows applications to tailor their anonymity properties and performance characteristics according to specific communication requirements. To support flexible path construction, A 3 exposes a declarative language (A 3 LOG) that enables applications to compactly specify path selection and instantiation policies executed by a declarative networking engine. We demonstrate that our declarative language is sufficiently expressive to encode novel multi-metric performance constraints as well as existing relay selection algorithms employed by Tor and other anonymity systems, using only a few lines of concise code. We experimentally evaluate the A 3 system using a combination of trace-driven simulations and deployment on Planet-Lab. Our experimental results demonstrate that A 3 can flexibly support a wide range of path selection and instantiation strategies at low performance overhead.

University of Washington Operators and researchers want accurate router-level views of the Internet for purposes including troubleshooting and modeling. However, tools such as traceroute return IP addresses. Because routers may have dozens of IP addresses, or aliases, multiple measurements may return different addresses, obscuring whether they represent the same machine. While many techniques exist to address this issue by identifying some IP aliases, these techniques, even in combination, find only a subset of alias pairs. To improve this state, we design and evaluate a new alias resolution technique using the IP prespecified timestamp option. This option allows a sender to request timestamp values from multiple IP addresses in the same probe. By careful arrangement of these IP addresses, we show that we can infer aliases in many cases. In this paper, we conduct a measurement study of how many routers support IP timestamps, demonstrating that enough honor the option to base our technique on it. Using our technique, and compared to the most accurate alias information available, we find that 94.7 % of the aliases identified by our technique are true positives. Further, we show that our IP timestamp-based technique complements existing alias resolution techniques, providing significant gains by discovering previously unidentifiable aliases.