The finalization of the 100 Gbps Ethernet Specification has been a tremendous increase in these rates arriving into data centers creating the need to perform security monitoring at 100 Gbps no longer simply an academic exercise. We show that by leveraging the 'heavy tail flow effect' on the IDS infrastructure, it is possible to perform security analysis at such speeds within the HPC environment. Additionally, we examine the nature of current traffic characteristics, how to scale an IDS infrastructure to 100Gbps.

Emerging grid applications desire not only high bandwidth but also the ability to control the topology and traffic engineering of the underlying networks, through Web service interfaces. To achieve that goal, we present an advanced user controlled lightpath provisioning (UCLP) system, where network resources and grid resources are both modeled as Web services and are seamlessly integrated into workflows.

A network management system software is prototyped using a service-oriented architecture with its core built using Jini and JavaSpaces technologies. The management system allows users to provision connections across multiple management domains. The system's functional modules are distributed to different management domains. Since the new management system is a distributed system, it imposes special requirements on system reliability. System reliability is improved by using lease-based resource and service sharing, transaction-based resource, concurrent access and persistent storage of resource information, and federation clustering. With these system reliability enhancements, the system is able to maintain its integrity even in the event of partial system failures.

Scientific collaboration is critical in high performance computational (HPC) environments. Computer security incidents, however, are a constant threat in today's interconnected computational environments. Many sites struggle with finding a balance between the needs of users and the threat from unauthorized usage or access. Understanding the constantly changing battlefield is critical for effective and optimal deployment of computer security resources.This tutorial will provide an underlying knowledge of the current state of computer security, its effects on HPC environments and mitigation strategies. Topics we will address include:1.HPC Security Fundamentals2.HPC Security Protection Fundamentals3.Intrusion Detection Using Bro4.The Changing Computer Protection Environment5.Cross-site Implications of Collaborative Environments6.Keeping One Step Ahead7.Risk Assessment, Mitigation and Compliance8.Components of a Good Protection ProgramWe will also show how described techniques are in use at SC06 to protect SCinet.

Modern forensic analytics applications, like network traffic analysis, perform high-performance hypothesis testing, knowledge discovery and data mining on very large datasets. One essential strategy to reduce the time required for these operations is to select only the most relevant data records for a given computation. In this paper, we present a set of parallel algorithms that demonstrate how an efficient selection mechanism -- bitmap indexing -- significantly speeds up a common analysis task, namely, computing conditional histogram on very large datasets. We present a thorough study of the performance characteristics of the parallel conditional histogram algorithms. As a case study, we compute conditional histograms for detecting distributed scans hidden in a dataset consisting of approximately 2.5 billion network connection records. We show that these conditional histograms can be computed on interactive time scale (i.e., in seconds). We also show how to progressively modify the selection criteria to narrow the analysis and find the sources of the distributed scans.

Our analytics task is to identify, characterize, and visualize anomalous subsets of as large of a collection of network connection data as possible. We use a combination of HPC resources, advanced algorithms, and visualization techniques. To effectively and efficiently identify the salient portions of the data, we rely on a multistage workflow that includes data acquisition, summarization (feature extraction), novelty detection, and classification. Once these subsets of interest have been identified and automatically characterized, we use a stateof- the-art high-dimensional query system to extract this data for interactive visualization. Our approach is equally useful for other large-data analysis problems where it is more practical to identify interesting subsets of the data for visualization than it is to render all data elements. By reducing the size of the rendering workload, we enable highly interactive and useful visualizations.

Crosstalk noise, due to the finite dimension of the recording medium, is a fundamental limitation to storage capacity in volume holographic memory. We present a general theoretical analysis on the crosstalk noise in angle- and wavelength-multiplexed volume holographic memory systems. Results on storage capacity and its dependence on hologram separation and required signal-to-noise ratio are obtained and discussed. Crosstalk noise may also arise from the expansion or shrinkage of the storage medium during and after the hologram recording process. Its effects, including Bragg mismatch, pixel displacement and impulse broadening, are analyzed for Fourier-plane holographic memories. Complete compensation and optimization to eliminate or reduce these effects are presented and discussed.