Data logging is helpful for the operation and maintenance manager of SaaS-based solutions to diagnose performance issues. However, long-running SaaS software may generate huge amounts of log data which is difficult to analyze, and it lacks a systematic approach to collect the running log and lacks a unified data structure to normalize the performance-related data. All these threaten the timeliness of SaaS performance issue diagnosis. In this paper, we propose an architecture for log collection and analysis to support the assessment of performance and diagnosis of performance issues of SaaS-based application in cloud computing. The architecture has the three-tier structure and includes a pivot data model to integrate heterogeneous log. The two high-level metrics in the model of Average Response Time (ART) and Request Timeout Rate (RTR) are calculated by statistical measurement and the lower-level metrics are monitored in real-time. Operation and maintenance managers can evaluate the performance of SaaS software based on the high-level metrics, then timely locate the issues from the low-level metrics and take appropriate measures. Thereupon, this study presents the general-purpose technique for the architecture to support real-time big log data collection, access, computation, storage. The proposal has been implemented and validated in a case study.

中文翻译：

---

支持SaaS软件性能问题诊断的日志数据建模与采集

数据记录有助于基于 SaaS 的解决方案的运维经理诊断性能问题。然而，长时间运行的 SaaS 软件可能会产生大量难以分析的日志数据，并且缺乏系统的方法来收集运行日志，也缺乏统一的数据结构来规范与性能相关的数据。这些都威胁到 SaaS 性能问题诊断的及时性。在本文中，我们提出了一种用于日志收集和分析的架构，以支持云计算中基于 SaaS 的应用程序的性能评估和性能问题诊断。该架构具有三层结构，并包括一个枢轴数据模型来集成异构日志。平均响应时间 (ART) 和请求超时率 (RTR) 模型中的两个高级指标是通过统计测量计算的，而低级指标是实时监控的。运维管理人员可以根据高层指标评估SaaS软件的性能，然后及时从低层指标中定位问题并采取相应措施。因此，本研究提出了该体系结构的通用技术，以支持实时大日志数据的收集、访问、计算和存储。该提案已在案例研究中得到实施和验证。然后及时从底层指标中定位问题并采取相应措施。因此，本研究提出了该体系结构的通用技术，以支持实时大日志数据的收集、访问、计算和存储。该提案已在案例研究中得到实施和验证。然后及时从底层指标中定位问题并采取相应措施。因此，本研究提出了该体系结构的通用技术，以支持实时大日志数据的收集、访问、计算和存储。该提案已在案例研究中得到实施和验证。