一种资源敏感的Web应用性能诊断方法

提出一种资源敏感的性能诊断方法.对于Web应用事务,该方法利用资源服务时间对于不同负载特征相对稳定的特点建立性能特征链,并依据运行时资源服务时间异常实现性能异常的有效检测、定位和诊断.实验结果表明,该方法可适应系统负载特征变化,诊断各种资源使用相关的性能异常.     

软件交互性能异常现象分析

近年来,得益于个人计算机乃至移动设备的普及,以及现代操作系统的发展,应用软件开发被推向一个前所未有的热潮。为了提高应用软件的用户体验,多线程程序开发技术已经广泛应用于软件开发的各个环节。多线程程序开发技术,一方面让计算机硬件资源得以充分利用,提高了软件的响应速度,另一方面也增加了程序开发的难度,以及增加了应用软件出现性能异常之后的分析调试难度。考察了现有软件性能异常现象分析的工作,通过对现有的交互性能问题进行扩展,以及对分析模型提出改进,以满足Linux操作系统上的应用软件性能异常场景的分析需要,并在Linux平台上设计交互性能异常分析系统,以Google Chrome和GNOME Nautilus等流行软件为分析对象,分析实际场景中的性能异常现象。     

复杂路面小尺度行人检测综述

针对云平台中对应用程序的性能监控方法存在全流程收集分析异常能力不足的问题, 提出一种基于云平台服务组件的应用程序异常检测和瓶颈识别系统(AAD-PSC), 可对多层架构云平台上的应用程序提供可自定义指标值的监控分析能力. 系统首先在前端应用服务层收集云平台服务调用数据并与异常事件相关联; 然后为应用程序适配定制化的异常检测方法, 达到最优检测效果; 最后查明由非工作负载变化引起的性能异常, 并对其进行瓶颈识别. 实验结果表明, 监控系统可快速准确检测不同类别的异常事件并识别性能瓶颈, 能够满足云平台下对应用程序的性能监控需求.     

MapReduce Workload Modeling with Statistical Approach

Large-scale data-intensive cloud computing with the MapReduce framework is becoming pervasive for the core business of many academic, government, and industrial organizations. Hadoop, a state-of-the-art open source project, is by far the most successful realization of MapReduce framework. While MapReduce is easy- to-use, efficient and reliable for data-intensive computations, the excessive configuration parameters in Hadoop impose unexpected challenges on running various workloads with a Hadoop cluster effectively. Consequently, developers who have less experience with the Hadoop configuration system may devote a significant effort to write an application with poor performance, either because they have no idea how these configurations would influence the performance, or because they are not even aware that these configurations exist. There is a pressing need for comprehensive analysis and performance modeling to ease MapReduce application development and guide performance optimization under different Hadoop configurations. In this paper, we propose a statistical analysis approach to identify the relationships among workload characteristics, Hadoop configurations and workload performance. We apply principal component analysis and cluster analysis to 45 different metrics, which derive relationships between workload characteristics and corresponding performance under different Hadoop configurations. Regression models are also constructed that attempt to predict the performance of various workloads under different Hadoop configurations. Several non-intuitive relationships between workload characteristics and performance are revealed through our analysis and the experimental results demonstrate that our regression models accurately predict the performance of MapReduce workloads under different Hadoop configurations.     

HA2:层次化的物联网感知设备固件异常分析技术

物联网底层一般包含大量的感知终端,这些设备是物联网应用与服务的基础。然而,由于在计算、存储、传输带宽等资源上的限制,感知设备固件程序运行时可获得状态非常有限,一旦这些设备出现异常,相关人员往往缺乏足够的手段对其开展分析。针对这一问题,提出一种层次化的物联网感知设备固件异常分析技术（Hierarchical Anomaly Analysis,HA2）。该方法以物联网感知节点程序静态结构及动态运行轨迹特征为基础,借助一分类支持向量机和统计推断方法,可以实现区间、任务和函数三个层次的异常检测,并生成相应的异常分析报告。实验表明该方法与现有方法相比,在收集异常分析特征方面具有较小的存储及运行开销。开源代码库中的缺陷实例分析表明,与现有方法相比HA2的层次化异常分析报告可以大大缩小异常分析范围,为用户分析、修复异常提供有效帮助。     

Application kernels: HPC resources performance monitoring and variance analysis

Application kernels are computationally lightweight benchmarks or applications run repeatedly on high performance computing (HPC) clusters in order to track the Quality of Service (QoS) provided to the users. They have been successful in detecting a variety of hardware and software issues, some severe, that have subsequently been corrected, resulting in improved system performance and throughput. In this work, the application kernels performance monitoring module of eXtreme Data Metrics on Demand (XDMoD) is described. Through the XDMoD framework, the application kernels have been run repetitively on the Texas Advanced Computing Center's Stampede and Lonestar4 clusters for a total of over 14,000 jobs. This provides a body of data on the HPC clusters operation that can be used to statistically analyze how the application performance, as measured by metrics such as execution time and communication bandwidth, is affected by the cluster's workload. We discuss metric distributions, carry out regression and correlation analyses, and use a PCA study to describe the variance and relate the variance to factors such as the spatial distribution of the application in the cluster. Ultimately, these types of analyses can be used to improve the application kernel mechanism, which in turn results in improved QoS of the HPC infrastructure that is delivered to the end users. Copyright © 2015 John Wiley & Sons, Ltd.     

轨迹异常检测研究综述

传感器技术的飞速发展催生大量交通轨迹数据,轨迹异常检测在智慧交通、自动驾驶、视频监控等领域具有重要的应用价值.不同于分类、聚类和预测等轨迹挖掘任务,轨迹异常检测旨在发现小概率、不确定和罕见的轨迹行为.轨迹异常检测中一些常见的挑战与异常值类型、轨迹数据标签、检测准确率以及计算复杂度有关.针对上述问题,全面综述近20年来轨迹异常检测技术的研究现状和最新进展.首先,对轨迹异常检测问题的特点与目前存在的研究挑战进行剖析.然后,基于轨迹标签的可用性、异常检测算法原理、离线或在线算法工作方式等分类标准,对现有轨迹异常检测算法进行对比分析.对于每一类异常检测技术,从算法原理、代表性方法、复杂度分析以及算法优缺点等方面进行详细总结与剖析.接着,讨论开源的轨迹数据集、常用的异常检测评估方法以及异常检测工具.在此基础上,给出轨迹异常检测系统架构,形成从轨迹数据采集到异常检测应用等一系列相对完备的轨迹挖掘流程.最后,总结轨迹异常检测领域关键的开放性问题,并展望未来的研究趋势和解决思路.     

Consistency anomalies in multi-tier architectures: automatic detection and prevention

Modern transaction systems, consisting of an application server tier and a database tier, offer several levels of isolation providing a trade-off between performance and consistency. While it is fairly well known how to identify qualitatively the anomalies that are possible under a certain isolation level, it is much more difficult to detect and quantify such anomalies during run-time of a given application. In this paper, we present a new approach to detect and quantify consistency anomalies for arbitrary multi-tier application running under any isolation levels ensuring at least read committed. In fact, the application can run even under a mixture of isolation levels. Our detection approach can be online or off-line and for each detected anomaly, we identify exactly the transactions and data items involved. Furthermore, we classify the detected anomalies into patterns showing the business methods involved as well as analyzing the types of cycles that occur. Our approach can help designers to either choose an isolation level where the anomalies do not occur or to change the transaction design to avoid the anomalies. Furthermore, we provide an option in which the occurrence of anomalies can be automatically reduced during run-time. To test the effectiveness and efficiency of our approach, we have conducted a set of experiments using a wide range of benchmarks.     

Digital Twin-driven online anomaly detection for an automation system based on edge intelligence

Accurate anomaly detection is critical to the early detection of potential failures of industrial systems and proactive maintenance schedule management. There are some existing challenges to achieve efficient and reliable anomaly detection of an automation system: (1) transmitting large amounts of data collected from the system to data processing components; (2) applying both historical data and real-time data for anomaly detection. This paper proposes a novel Digital Twin-driven anomaly detection framework that enables real-time health monitoring of industrial systems and anomaly prediction. Our framework, adopting the visionary edge AI or edge intelligence (EI) philosophy, provides a feasible approach to ensuring high-performance anomaly detection via implementing Digital Twin technologies in a dynamic industrial edge/cloud network. Edge-based Digital Twin allows efficient data processing by providing computing and storage capabilities on edge devices. A proof-of-concept prototype is developed on a LiBr absorption chiller to demonstrate the framework and technologies' feasibility. The case study shows that the proposed method can detect anomalies at an early stage.     

面向图的异常检测研究综述

近年来，随着web2.0的普及，使用图挖掘技术进行异常检测受到人们越来越多的关注.图异常检测在欺诈检测、入侵检测、虚假投票、僵尸粉丝分析等领域发挥着重要作用.本文在广泛调研国内外大量文献以及最新科研成果的基础上，按照数据表示形式将面向图的异常检测划分成静态图上的异常检测与动态图上的异常检测两大类，进一步按照异常类型将静态图上的异常分为孤立个体异常和群组异常检测两种类别，动态图上的异常分为孤立个体异常、群体异常以及事件异常三种类型.对每一类异常检测方法当前的研究进展加以介绍，对每种异常检测算法的基本思想、优缺点进行分析、对比，总结面向图的异常检测的关键技术、常用框架、应用领域、常用数据集以及性能评估方法，并对未来可能的发展趋势进行展望.     

Online anomaly detection for multi‐source VMware using a distributed streaming framework

Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non‐conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data. This paper presents a novel, generic real‐time distributed anomaly detection framework for multi‐source stream data. As a case study, we investigate anomaly detection for a multi‐source VMware‐based cloud data center, which maintains a large number of virtual machines (VMs). This framework continuously monitors VMware performance stream data related to CPU statistics (e.g., load and usage). It collects data simultaneously from all of the VMs connected to the network and notifies the resource manager to reschedule its CPU resources dynamically when it identifies any abnormal behavior from its collected data. A semi‐supervised clustering technique is used to build a model from benign training data only. During testing, if a data instance deviates significantly from the model, then it is flagged as an anomaly. Effective anomaly detection in this case demands a distributed framework with high throughput and low latency. Distributed streaming frameworks like Apache Storm, Apache Spark, S4, and others are designed for a lower data processing time and a higher throughput than standard centralized frameworks. We have experimentally compared the average processing latency of a tuple during clustering and prediction in both Spark and Storm and demonstrated that Spark processes a tuple much quicker than storm on average. Copyright © 2016 John Wiley & Sons, Ltd.     

Self-adaptive statistical process control for anomaly detection in time series

Anomaly detection in time series has become a widespread problem in the areas such as intrusion detection and industrial process monitoring. Major challenges in anomaly detection systems include unknown data distribution, control limit determination, multiple parameters, training data and fuzziness of ‘anomaly’. Motivated by these considerations, a novel model is developed, whose salient feature is a synergistic combination of statistical and fuzzy set-based techniques. We view anomaly detection problem as a certain statistical hypothesis testing. Meanwhile, ‘anomaly’ itself includes fuzziness, therefore, can be described with fuzzy sets, which bring a facet of robustness to the overall scheme. Intensive fuzzification is engaged and plays an important role in the successive step of hypothesis testing. Because of intensive fuzzification, the proposed algorithm is distribution-free and self-adaptive, which solves the limitation of control limit and multiple parameters. The framework is realized in an unsupervised mode, leading to great portability and scalability. The performance is assessed in terms of ROC curve on university of California Riverside repository. A series of experiments show that the proposed approach can significantly increase the AUC, while the false alarm rate is improved. In particular, it is capable of detecting anomalies at the earliest possible time.     

Finding anomalous periodic time series

Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single continuous time series or a set of time series whose periods are aligned. Light-curve data precludes the use of these methods as the periods of any given pair of light-curves may be out of sync. One may use an existing anomaly detection method if, prior to similarity calculation, one performs the costly act of aligning two light-curves, an operation that scales poorly to massive data sets. This paper presents PCAD, an unsupervised anomaly detection method for large sets of unsynchronized periodic time-series data, that outputs a ranked list of both global and local anomalies. It calculates its anomaly score for each light-curve in relation to a set of centroids produced by a modified k-means clustering algorithm. Our method is able to scale to large data sets through the use of sampling. We validate our method on both light-curve data and other time series data sets. We demonstrate its effectiveness at finding known anomalies, and discuss the effect of sample size and number of centroids on our results. We compare our method to naive solutions and existing time series anomaly detection methods for unphased data, and show that PCAD’s reported anomalies are comparable to or better than all other methods. Finally, astrophysicists on our team have verified that PCAD finds true anomalies that might be indicative of novel astrophysical phenomena.     

Temporal vertical profiling

Modern systems are enormously complex; many applications today comprise millions of lines of code, make extensive use of software frameworks, and run on complex, multi‐tiered, run‐time systems. Understanding the performance of these applications is challenging because it depends on the interactions between the many software and the hardware components. This paper describes and evaluates an interactive and iterative methodology, temporal vertical profiling, for understanding the performance of applications. There are two key insights behind temporal vertical profiling. First, we need to collect and reason across information from multiple layers of the system before we can understand an application's performance. Second, application performance changes over time and thus we must consider the time‐varying behavior of the application instead of aggregate statistics. We have developed temporal vertical profiling from our own experience of analyzing performance anomalies and have found it very helpful for methodically exploring the space of hardware and software components. By representing an application's behavior as a set of metrics, where each metric is represented as a time series, temporal vertical profiling provides a way to reason about performance across system layers, regardless of their level of abstraction, and independent of their semantics. Temporal vertical profiling provides a methodology to explore a large space of metrics, hundreds of metrics even for small benchmarks, in a systematic way. Copyright © 2010 John Wiley & Sons, Ltd.     

基于LabVIEW的控制阀性能测试评估系统设计

控制阀在工业控制领域扮演十分重要的角色,但目前国内对控制阀性能测试评估系统的研究十分薄弱,并且常规的仪器仪表很难精确测试控制阀的性能;针对这种情况,基于NI compactRIO硬件、各类传感器以及LabVIEW软件开发设计了一套控制阀性能测试评估系统,该系统能实时检测、采集处理数据,能够把阀门性能测试过程中的数据自动保存与分析处理,实现了控制阀的在线测试和功能评估,并且可将控制阀各项性能指标评估结果汇集于一张类似体检单的报表显示,给阀门厂商的出厂测试、维修检测以及日常维护提供参考依据;采用该系统对某公司的直通气开式薄膜调节阀S9044进行了性能测试评估,实验结果表明,该系统能够全面、精确地测试出调节阀的各项性能指标,且系统软件交互界面良好、扩展性强、精度高。     

指令级工作负载特征模型

工作负载特征模型在计算机体系结构设计和系统性能评价中具有重要作用,有效的工作负载行为特征分析有助于体系结构性能评价和设计改进。通过基于指令级的工作负载特征描述,提取影响测试程序运行时间的关键因素。设计一个负载特征模型,该模型可以根据不同的参数设置生成不同合成工作负载。     

Smart anomaly detection in sensor systems: A multi-perspective review

Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behavior. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, fault prevention, and industrial automation. Herein, we review state-of-the-art methods that may be employed to detect anomalies in the specific area of sensor systems, which poses hard challenges in terms of information fusion, data volumes, data speed, and network/energy efficiency, to mention but the most pressing ones. In this context, anomaly detection is a particularly hard problem, given the need to find computing-energy-accuracy trade-offs in a constrained environment. We taxonomize methods ranging from conventional techniques (statistical methods, time-series analysis, signal processing, etc.) to data-driven techniques (supervised learning, reinforcement learning, deep learning, etc.). We also look at the impact that different architectural environments (Cloud, Fog, Edge) can have on the sensors ecosystem. The review points to the most promising intelligent-sensing methods, and pinpoints a set of interesting open issues and challenges.     

A compensation-based approach for view maintenance in distributed environments

Data integration over multiple heterogeneous data sources has become increasingly important for modern applications. The integrated data is usually stored as materialized views to allow better access, performance, and high availability. In loosely coupled environments, such as the data grid, the data sources are autonomous. Hence, tie source updates can be concurrent and cause erroneous results during view maintenance. State-of-the-art maintenance strategies apply compensating queries to correct such errors, making the restricting assumption that all source schemata remain static over time. However, in such dynamic environments, the data sources may change not only their data but also their schema. Consequently, either the maintenance queres or the compensating queries may fail. In this paper, we propose a novel framework called DyDa that overcomes these limitations and handles both source data updates and schema changes. We identify three types of maintenance anomalies, caused by either source data updates, data-preserving schema changes, or non-data-preserving schema changes. We propose a compensation algorithm to solve the first two types of anomalies. We show that the third type of anomaly is caused by the violation of dependencies between maintenance processes. Then, we propose dependency detection and correction algorithms to identify and resolve the violations. Put together, DyDa extends prior maintenance solutions to solve all types of view maintenance anomalies. The experimental results show that DyDa imposes a minimal overhead on data update processing while allowing for the extended functionality to handle concurrent schema changes.     

Anomaly detection for construction vibration signals using unsupervised deep learning and cloud computing

In-operation construction vibration monitoring records inevitably contain various anomalies caused by sensor faults, system errors, or environmental influence. An accurate and efficient anomaly detection technique is essential for vibration impact assessment. Identifying anomalies using visualization tools is computationally expensive, time-consuming, and labor-intensive. In this study, an unsupervised approach for detecting anomalies in construction vibration monitoring data was proposed based on a temporal convolutional network and autoencoder. The anomalies were autonomously detected on the basis of the reconstruction errors between the original and reconstructed signals. Considering the false and missed detections caused by great variability in vibration signals, an adaptive threshold method was applied to achieve the best identification performance. This method used the log-likelihood of the reconstruction errors to search for an optimal coefficient for anomalies. A distributed training strategy was implemented on a cloud platform to speed up training and perform anomaly detection without significant time delay. Construction-induced accelerations measured by a real vibration monitoring system were used to evaluate the proposed method. Experimental results show that the proposed approach can successfully detect anomalies with high accuracy; and the distributed training can remarkably save training time, thereby realizing anomaly detection for online monitoring systems with accumulated massive data.