Solving Big Data Challenges for Enterprise Application Performance Management
Solving Big Data Challenges for Enterprise Application Performance Management
复制标题
DOI：
10.14778/2367502.2367512
复制
发表时间：
2012-08
期刊：
影响因子：
--
通讯作者：
T. Rabl;Mohammad Sadoghi;H. Jacobsen;S. Gómez-Villamor;V. Muntés-Mulero;Serge Mankowskii
中科院分区：
文献类型：
--
作者：
T. Rabl;Mohammad Sadoghi;H. Jacobsen;S. Gómez-Villamor;V. Muntés-Mulero;Serge Mankowskii
随着企业系统复杂性的增加，监控和分析此类系统的需求也在增长。许多公司已经构建了复杂的监控工具，远远超出了简单的资源利用报告。例如，基于插装和专用API，现在可以跨地理分布的系统监视单个方法调用和跟踪单个事务。这种高级别的细节支持更精确的分析和预测形式，但代价是高数据率(即大数据)。为了最大限度地发挥数据监控的优势，必须将数据存储更长时间，以便进行其他分析。这一新的大数据分析浪潮给应用程序性能监控系统带来了新的挑战。监测数据必须存储在一个系统中，该系统可以维持高数据速率，同时支持对底层基础设施的最新查看。随着现代键值存储的出现，出现了各种数据存储系统，它们的构建侧重于可伸缩性和高数据速率，在此监控用例中占主导地位。在这项工作中，作为CA Technologies计划的一部分，我们在应用程序性能监控的环境中展示了我们的经验和对六个现代(开源)数据存储的全面性能评估。我们使用可在应用程序性能监控以及在线广告、电源监控和许多其他使用案例中找到的数据和工作负载对这些系统进行了评估。我们不仅将我们的见解作为性能结果，还将其作为经验教训，以及与行业环境中这些数据存储的设置和配置复杂性相关的经验。
As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For example, based on instrumentation and specialized APIs, it is now possible to monitor single method invocations and trace individual transactions across geographically distributed systems. This high-level of detail enables more precise forms of analysis and prediction but comes at the price of high data rates (i.e., big data). To maximize the benefit of data monitoring, the data has to be stored for an extended period of time for ulterior analysis. This new wave of big data analytics imposes new challenges especially for the application performance monitoring systems. The monitoring data has to be stored in a system that can sustain the high data rates and at the same time enable an up-to-date view of the underlying infrastructure. With the advent of modern key-value stores, a variety of data storage systems have emerged that are built with a focus on scalability and high data rates as predominant in this monitoring use case. In this work, we present our experience and a comprehensive performance evaluation of six modern (open-source) data stores in the context of application performance monitoring as part of CA Technologies initiative. We evaluated these systems with data and workloads that can be found in application performance monitoring, as well as, on-line advertisement, power monitoring, and many other use cases. We present our insights not only as performance results but also as lessons learned and our experience relating to the setup and configuration complexity of these data stores in an industry setting.