Engineering topics

Critchlow, Terence J

Publications and source records attributed to Critchlow, Terence J.

4 records

DOE OSTI2011

Data Intensive Architecture for Scalable Cyber Analytics

Cyber analysts are tasked with the identification and mitigation of network exploits and threats. These compromises are difficult to identify due to the characteristics of cyber communication, the volume of traffic, and the duration of possible attack. In this paper, we describe a prototype implementation designed to provide cyber analysts an environment where they can interactively explore a month’s worth of cyber security data. This prototype utilized On-Line Analytical Processing (OLAP) techniques to present a data cube to the analysts. The cube provides a summary of the data, allowing trends to be easily identified as well as the ability to easily pull up the original records comprising an event of interest. The cube was built using SQL Server Analysis Services (SSAS), with the interface to the cube provided by Tableau. This software infrastructure was supported by a novel hardware architecture comprising a Netezza TwinFin® for the underlying data warehouse and a cube server with a FusionIO drive hosting the data cube. We evaluated this environment on a month’s worth of artificial, but realistic, data using multiple queries provided by our cyber analysts. As our results indicate, OLAP technology has progressed to the point where it is in a unique position to provide novel insights to cyber analysts, as long as it is supported by an appropriate data intensive architecture.

Olsen, Bryan K.↗

DOE OSTI2011

The Scientific Data Management Center: Available Technologies and Highlights

Managing scientific data has been identified by the scientific community as one of the most important emerging needs because of the sheer volume and increasing complexity of data being collected. Effectively generating, managing, and analyzing this information requires a comprehensive, end-to-end approach to data management that encompasses all of the stages from the initial data acquisition to the final analysis of the data. Based on community input, we have identified three significant requirements. First, more efficient access to storage systems is needed. In particular, parallel file system and I/O system improvements are needed to write and read large volumes of data without slowing a simulation. Second, scientists require technologies to facilitate better understanding of their data, in particular the ability to effectively perform complex data analysis and searches over extremely large data sets. Furthermore, exploratory analysis requires techniques for efficiently selecting subsets of the data. Third, generating the data, collecting and storing the results, keeping track of data provenance, data post-processing, and analysis of results is a tedious, fragmented process. Tools for automation of this process in a robust, tractable, and recoverable fashion are required to enhance scientific exploration.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Supercomputing and Scientific Workflows Gaps and Requirements

Over the past decade, workflows have been successfully applied to a number of scientific domains with great success. Workflow engines are now commonly used across scientific disciplines to automate mundane tasks, collect provenance, and orchestrate complex processes. However, workflows have not yet made significant strides managing fine-grain, concurrent tasks directly on supercomputing platforms. As scientific computing becomes an increasingly important discovery method and high performance computing environments become more complex, addressing this gap becomes critical. Using a simple use case as motivation, this paper describes the current barriers to using workflow engines in a supercomputing environment and outlines the new capabilities that must be provided if workflows are to be successfully applied in this context.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Kepler Scientific Workflow Design and Execution with Contexts

A context-aware scientific workflow is a typical scientific workflow that is enhanced with context binding and awareness mechanisms. Context facilitates further configuration of the scientific workflow at runtime such that it is tuned to its environment during execution and responds intelligently based on such awareness without customized coding of the workflow. In this paper, we present a context annotation framework, which supports rapid development of context-aware scientific workflows. Context annotation enables a diverse type of actor in Kepler that may bind with different sensed environmental information as part of the actor’s regular data. Context-aware actors simplify the construction of scientific workflows that require intricate knowledge in initializing and configuring a large number of parameters to cover all different execution conditions. This paper presents the motivation, system design, implementation, and usage of context annotation in relation to the Kepler scientific workflow system.

99 GENERAL AND MISCELLANEOUS↗