Cloud computing provides an efficient way to expose computing capabilities as sophisticated services that can be accessed remotely. It eliminates the need for organizations to maintain extensive infrastructures. Parallel and distributed simulation over cloud will bring us not only the increased productivity and efficiency but also the lower barrier for usage. This paper introduces a private cloud system based on BOINC which supports parallel and distributed simulation. The design idea and implementation method of each component for our system is discussed in detail.

Communications between individuals can be represented by (weighted, multi-) graphs. Many applications operate on communication graphs associated with telephone calls, emails, Instant Messages (IM), blogs, web forums, e-business relationships and so on. These applications include identifying repetitive fraudsters, message board aliases, multiusage of IP addresses, etc. Tracking electronic identities in communication networks can be achieved if we have a reliable “signature ” for nodes and activities. While many examples of ad hoc signatures can be proposed for particular tasks, what is needed is a systematic study of the principles behind the usage of signatures for any task. We develop a formal framework for the use of signatures in communication graphs and identify three fundamental properties that are natural to signature schemes: persistence, uniqueness and robustness. We argue for the importance of these properties by showing how they impact a set of applications. We then explore several signature schemes — previously defined and new — in our framework and evaluate them on real data in terms of these properties. This provides insights into suitable signature schemes for desired applications. Finally, as case studies, we focus on two concrete applications in enterprise network traffic. We apply signature schemes to these problems and demonstrate their effectiveness.

This paper describes how to fit fractal models, online, on IP traffic data streams. Our approach relies on maintaining a sketch of the data stream and fitting straight lines: it yields algorithms that are fast, space-efficient, and accurate. We implemented our methods in AT&T’s Gigascope data stream management system, to demonstrate their practicality at streaming line speeds.

Data stream applications have made use of statistical summaries to reason about the data using nonparametric tools such as histograms, heavy hitters, and join sizes. However, relatively little attention has been paid to modeling stream data parametrically, despite the potential this approach has for mining the data. The challenges to do model fitting at streaming speeds are both technical -- how to continually find fast and reliable parameter estimates on high speed streams of skewed data using small space -- and conceptual -- how to validate the goodness-of-fit and stability of the model online.In this paper, we show how to fit hierarchical (binomial multifractal) and non-hierarchical (Pareto) power-law models on a data stream. We address the technical challenges using an approach that maintains a sketch of the data stream and fits least-squares straight lines; it yields algorithms that are fast, space-efficient, and provide approximations of parameter value estimates with a priori quality guarantees relative to those obtained offline. We address the conceptual challenge by designing fast methods for online goodness-of-fit measurements on a data stream; we adapt the statistical testing technique of examining the quantile-quantile (q-q) plot, to perform online model validation at streaming speeds.As a concrete application of our techniques, we focus on network traffic data which has been shown to exhibit skewed distributions. We complement our analytic and algorithmic results with experiments on IP traffic streams in AT&T's Gigascope® data stream management system, to demonstrate practicality of our methods at line speeds. We measured the stability and robustness of these models over weeks of operational packet data in an IP network. In addition, we study an intrusion detection application, and demonstrate the potential of online parametric modeling.