As the Internet matures, policy questions loom larger in its operation. When should an ISP, city, or government invest in infrastructure? How do their policies affect use? In this work, we develop a new approach to evaluate how policies, economic conditions and technology correlates with Internet use around the world. First, we develop an adaptive and accurate approach to estimate block availability, the fraction of active IP addresses in each /24 block over short timescales (every 11 minutes). Our estimator provides a new lens to interpret data taken from existing long-term outage measurements, thus requiring no additional traffic. (If new collection was required, it would be lightweight, since on average, outage detection requires less than 20 probes per hour per /24 block; less than 1% of background radiation.) Second, we show that spectral analysis of this measure can identify diurnal usage: blocks where addresses are regularly used during part of the day and idle in other times. Finally, we analyze data for the entire responsive Internet (3.7M /24 blocks) over 35 days. These global observations show when and where the Internet sleeps---networks are mostly always-on in the US and Western Europe, and diurnal in much of Asia, South America, and Eastern Europe. ANOVA (Analysis of Variance) testing shows that diurnal networks correlate negatively with country GDP and electrical consumption, quantifying that national policies and economics relate to networks.

Natural and human factors cause Internet outages---from big events like Hurricane Sandy in 2012 and the Egyptian Internet shutdown in Jan. 2011 to small outages every day that go unpublicized. We describe Trinocular, an outage detection system that uses active probing to understand reliability of edge networks. Trinocular is principled: deriving a simple model of the Internet that captures the information pertinent to outages, and populating that model through long-term data, and learning current network state through ICMP probes. It is parsimonious, using Bayesian inference to determine how many probes are needed. On average, each Trinocular instance sends fewer than 20 probes per hour to each /24 network block under study, increasing Internet "background radiation" by less than 0.7%. Trinocular is also predictable and precise: we provide known precision in outage timing and duration. Probing in rounds of 11 minutes, we detect 100% of outages one round or longer, and estimate outage duration within one-half round. Since we require little traffic, a single machine can track 3.4M /24 IPv4 blocks, all of the Internet currently suitable for analysis. We show that our approach is significantly more accurate than the best current methods, with about one-third fewer false conclusions, and about 30% greater coverage at constant accuracy. We validate our approach using controlled experiments, use Trinocular to analyze two days of Internet outages observed from three sites, and re-analyze three years of existing data to develop trends for the Internet.

Prior studies of Internet traffic have considered traffic at different resolutions and time scales: packets and flows for hours or days, aggregate packet statistics for days or weeks, and hourly trends for months. However, little is known about the long-term behavior of individual flows. In this paper, we study individual flows (as defined by the 5-tuple of protocol, source and destination IP address and port) over days and weeks. While the vast majority of flows are short, and most bytes are in short flows, we find that about 20% of the overall bytes are carried in flows that last longer than 10 minutes, and flows lasting 100 minutes or longer make up 2 % of traffic. We show that long-lived flows are qualitatively different from short flows: they are generally slower, less bursty, and are due to different applications and protocols. We investigate the causes of short- and long-lived flows, and show that the traffic mix varies significantly depending on duration time scale, with computer-to-computer traffic more and more dominating in larger time scales.

Most existing mobile beacon-assisted localization algorithms do not make effective use of the node distribution information and let the mobile landmark traverse the entire network, which causes large path length and low utilization rate of beacon messages. In order to reduce the path length and use the mobile beacon more effectively, a novel mobile beacon-assisted localization algorithm based on network-density clustering (MBL(ndc)) for wireless sensor networks is presented, which combines node clustering, incremental localization and mobile beacon assisting together. Simulation results demonstrate that the proposed MBL(ndc) algorithm offers comparable localization accuracy as the mobile beacon-assisted localization algorithm with HILBERT trajectory, but with less than 50% path length of the later, which shortens the period of positioning the whole network.

The characterization of HTTP traffic is crucial for performance evaluation and server design. In this paper, we analyze massive Web traces generated by various busy servers in recent years, trying to find the new features of modern HTTP traffic and user behaviors. Comparing the conclusions of earlier studies with our results, we have spotted considerable unconventional ingredients in modern HTTP traffic that could hardly be described by previous models. We also propose an innovative scheme to automatically categorize these various ingredients in modern traffic. The novel aspects of our work are: (1)It reveals the sophisticated composition of modern HTTP traffic with solid evidence, (2)It provides an automatic method to analyze the composition of modern HTTP traffic and (3)It promises a powerful manner to evaluate the possible performance implication of modern HTTP traffic on existing Web servers. We hope this work would help researchers and designers to better understand new features of HTTP workloads and therefore make corresponding adaptations in design practice.