OnTimeDetect: Dynamic Network Anomaly Notification in perfSONAR Deployments
OnTimeDetect: Dynamic Network Anomaly Notification in perfSONAR Deployments
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OnTimeDetect：perfSONAR 部署中的动态网络异常通知
DOI：
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发表时间：
2010
期刊：
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通讯作者：
A. Krishnamurthy
中科院分区：
文献类型：
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作者：
P. Calyam;Jialu Pu;W. Mandrawa;A. Krishnamurthy
为了监测和诊断用于大规模数据传输的网络路径上的瓶颈，部署诸如perfSONAR之类的测量框架的趋势越来越明显。这些部署使用Web服务来公开当前和历史测量的大量数据档案，这些数据档案可以跨端到端的多域网络路径进行查询。因此，出现了开发自动化技术和直观工具的需求，这些技术和直观工具帮助分析这些测量结果，以实时和离线方式检测和通知突出的网络异常，例如高原。在本文中，我们提出了一个动态自适应的高原检测（APD）计划和它的实现在我们的“OnTimeDetect”工具，使消费者的perfSONAR测量数据密集型的科学界克服其现有的网络异常检测和通知的限制。我们经验性地评估我们的APD计划的准确性，敏捷性和可扩展性，通过使用测量跟踪收集的OnTimeDetect工具从世界各地的perfSONAR部署在HPC社区。
To monitor and diagnose bottlenecks on network paths used for large-scale data transfers, there is an increasing trend to deploy measurement frameworks such as perfSONAR. These deployments use web-services to expose vast data archives of current and historic measurements, which can be queried across end-to-end multi-domain network paths. Consequently, there has arisen a need to develop automated techniques and intuitive tools that help analyze these measurements for detecting and notifying prominent network anomalies such as plateaus in both real-time and offline manner. In this paper, we present a dynamically adaptive plateau-detection (APD) scheme and its implementation in our “OnTimeDetect” tool to enable consumers of perfSONAR measurements within the data-intensive scientific communities in overcoming their existing limitations of network anomaly detection and notification. We empirically evaluate our APD scheme in terms of accuracy, agility and scalability by using measurement traces collected by OnTimeDetect tool from worldwide perfSONAR deployments in HPC communities.