Engineering topics
Publications and source records attributed to Tierney, Brian.
Intelligent Network Deployment Intent Renderer Application an interactive network assistant that will help us configure a data path between two scientific end-point groups (EPGs) to optimize the transfer of elephant data flows. This tool is able to talk to scientists gather network requirements and then convert to network programming code and automate the end-to-end provisioning.
Many scientific workflows require large data transfers between distributed instrument facilities, storage and computing resources. To ensure that these resources are maximally utilized, R&E networks connecting these resources must ensure that an inherently unpredictable network behaves predictably. In practice, this amounts to the per-application over-provisioning of network resources in an attempt to guarantee that adequate throughput is provided to users. This often results in resource under-utilization over time. One promising solution is the use of deadlines and bandwidth calendaring. In this approach, "fair" resource allocation is replaced with deadline-based resource allocation. However, these approaches often suffer from issues in efficiently regulating resource allocation and failure modes. Therefore, our solution, Calibers, approaches bandwidth calendaring and deadline-awareness in a different way. Calibers uses shaping, metering, and pacing at the edge of the network and end-system to provide participating clients the ability to schedule bandwidth reservations without having to worry about network noise from non-participating clients. Calibers can also fail back to the fair resource allocation of underlying transport protocols if necessary. For example, if a non-participating flow somehow enters the core of the network, or a sudden network change causes the available bandwidth to be exceeded, the underlying transport protocol congestion avoidance implementation will be able to handle the congestion as it normally would. Furthermore, Calibers provides a novel simulation method and resource allocation algorithm.
Traffic analysis is a core element in network operations and management for various purposes including change detection, traffic prediction, and anomaly detection. In this paper, we introduce a new approach to online traffic analysis based on a pattern-based representation for high-level summarization of the traffic measurement data. Unlike the past online analysis techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the observed patterns from previous time windows enabling intuitive analysis. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility. Our extensive experimental results with public traces and collected monitoring measurements from ESnet traffic traces show that our pattern-based approach is effective for multivariate analysis of online network traffic with visual and quantitative tools.
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We have developed and deployed a distributed-parallel storage system (DPSS) in several high speed asynchronous transfer mode (ATM) wide area networks (WAN) testbeds to support several different types of data-intensive applications. Architecturally, the DPSS is a network striped disk array, but is fairly unique in that its implementation allows applications complete freedom to determine optimal data layout, replication and/or coding redundancy strategy, security policy, and dynamic reconfiguration. In conjunction with the DPSS, we have developed a 'top-to-bottom, end-to-end' performance monitoring and analysis methodology that has allowed us to characterize all aspects of the DPSS operating in high speed ATM networks. In particular, we have run a variety of performance monitoring experiments involving the DPSS in the MAGIC testbed, which is a large scale, high speed, ATM network and we describe our experience using the monitoring methodology to identify and correct problems that limit the performance of high speed distributed applications. Finally, the DPSS is part of an overall architecture for using high speed, WAN's for enabling the routine, location independent use of large data-objects. Since this is part of the motivation for a distributed storage system, we describe this architecture.
We describe the architecture, implementation, use of a scalable, high performance, distributed-parallel data storage system developed in the ARPA funded MAGIC gigabit testbed. A collection of wide area distributed disk servers operate in parallel to provide logical block level access to large data sets. Operated primarily as a network-based cache, the architecture supports cooperation among independently owned resources to provide fast, large-scale, on-demand storage to support data handling, simulation, and computation.