Better exposing congestion can improve traffic management in the wide-area, at peering points, among residential broadband connections, and in the data center. TCP's network utilization and efficiency depends on congestion information, while recent research proposes economic and policy models based on congestion. Such motivations have driven widespread support of Explicit Congestion Notification (ECN)in modern operating systems. We reappraise the Internet's ECN readiness, updating and extending previous measurements. Across large and diverse server populations, we find a three-fold increase in ECN support over prior studies. Using new methods, we characterize ECN within mobile infrastructure and at the client-side, populations previously unmeasured. Via large-scale path measurements, we find the ECN feedback loop failing in the core of the network 40% of the time, typically at AS boundaries. Finally, we discover new examples of infrastructure violating ECN Internet standards, and discuss remaining impediments to running ECN while suggesting mechanisms to aid adoption.

Connectivity is central to pervasive computing environments. We seek to catalyze a world of rich and diverse connectivity through technologies that drastically simplify the task of providing, choosing, and using wireless network services; creating a new and more competitive environment for these capabilities. A critical requirement is that users actually benefit from this rich environment, rather than simply being overloaded with choices. We address this with an intelligent software agent that transparently and continually chooses from among available network services based on its user's individual needs and preferences, while requiring only minimal guidance and user interaction. We present an overview and model of the network service selection problem. We then describe an adaptive user agent that learns its user's network service preferences from a very minimal, intuitive set of inputs, and autonomously and continually selects the service that best meets the user's needs. Results from preliminary user experiments are presented that demonstrate the effectiveness of our agent.

This paper describes a method of inferring logical relationships between network prefixes within an Autonomous System (AS) using only passive monitoring of BGP messages. By clustering these prefixes based upon similarities between their update times, we create a hierarchy linking the prefixes within the larger AS. We can frequently identify groups of prefixes routed to the same ISP Point of Presence (POP), despite the lack of identifying information in the BGP messages. Similarly, we observe disparate prefixes under common organizational control, or with long shared network paths. In addition to discovering interesting network characteristics, our passive method facilitates topology discovery by potentially reducing the number of active probes required in traditional traceroute-based Internet mapping mechanisms.