The highly topological dynamics characterizes the most fundamental property of satellite networks with respect to terrestrial ones. The manifest feature directs the researches on various aspects of satellite networks, including protocol architecture investigations, routing protocol and reliable transmission control protocol design and enhancement, etc. This paper systematically quantifies the dynamical activities of regular low earth orbit (LEO) satellite network topologies. The number and length of network snapshots are formulated concisely. With this work, it compensates for the simplified topological assumptions in many LEO satellite network related researches. The thorough understanding of this basic feature not only provides for an accurate quantification of network behavior for researchers on satellite network community, but also could act as a guidance for future satellite constellation design and optimization.

The long-tailed distribution characterizes many Internet traffic properties which are often modeled by Lognormal distribution, Weibull or Pareto distribution theoretically. However, it is rather difficult to directly apply these models in traffic analysis and performance evaluation studies due to their complex representations and theoretical properties. This paper proposes a Hyper-Erlang Model (Mixed Erlang distribution) for such longtailed network traffic approximation. It fits network traffic with long-tailed characteristic into a mixed Erlang distribution directly to facilitate our further analysis. Compared with the wellknown hyperexponential based method, the mixed Erlang model is more accurate in fitting the tail behavior and also computationally efficient. Further investigations on the M/G/1 queueing behavior also prove the efficiency of the Mixed Erlang based approximation.

Collecting network traffic is becoming a more challenging task in passive network measurement due to the rapid growth of link speed. Flow-based network traffic capture and storage provides an efficient way for high-speed network measurement. The paper concentrates on the flow detection issue which is also the premise for further flow-based traffic analysis and modeling in such challenging environment. Based on the statistical investigation of the correlations between flow size and the maximum packet interarrival time within a flow, we obtain the empirical conditional distribution functions for some popular TCP protocol-based application flows, and then propose a Probability-Guaranteed Adaptive Timeout algorithm (PGAT) for flow termination decision. The assessment criteria for flow termination decision algorithm is systematically developed. Comparisons on flow generation ratio, flow intact ratio, and mean flow extra retaining time metrics indicate that the PGAT algorithm can obtain more attractive performance than other related works.

Clock synchronization is a crucial issue for scalable and accurate network performance measurements, especially when no external time sources are introduced. The paper presents a clustering based efficient and robust algorithm Optimized Top-Down Time series Segmentation (OTDTS) for clock synchronization between end-to-end systems. The computational complexity of OTDTS is of order OðKN2Þ. Based on the one-way probe delay traces, the algorithm segments the delay time series at proper points, at which clock dynamics occur. End systems could achieve relative clock synchronization by estimating and removing the clock skew of each segment. Simulations on artificial data set and practical Internet measurement illustrate the availability and efficiency of OTDTS.

The long-tailed distribution characterizes many properties of Internet traffic. The property is often modeled by Lognormal distribution, Weibull or Pareto distribution theoretically. However, it hinders us in traffic analysis and evaluation studies directly from these models due to their complex representations and theoretical properties. This paper proposes a Hyper-Erlang Model (Mixed Erlang distribution) for such long-tailed network traffic approximation. It fits network traffic with long-tailed characteristic into a mixed Erlang distribution directly to facilitate our further analysis. Compared with the well-known hyperexponential based method, the mixed Erlang model is more accurate in fitting the tail behavior and also computationally efficient.