SaaS software performance issue identification using HMRF-MAP framework
SaaS software performance issue identification using HMRF-MAP framework
复制标题
使用 HMRF-MAP 框架识别 SaaS 软件性能问题
DOI：
10.1002/spe.2607
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Shi Ying
中科院分区：
文献类型：
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作者：
Rui Wang;Shi Ying
软件即服务 (SaaS) 软件的性能通常通过云计算平台中监控的性能指标的组合来表征。由于应用软件的复杂性和部署环境的动态特性，基于指标数据的性能问题手动诊断通常既昂贵又费力。为了解决上述问题，我们提出了一种自动性能问题识别方法。该方法根据监测的指标值构建隐马尔可夫随机场最大后验（HMRF-MAP）模型。该模型通过分析系统的历史状态来计算系统当前的性能状态。在本文中，我们通过部署在云计算平台上的生产系统的案例研究来评估我们的方法。评估结果表明，我们的方法（1）系统开销小，（2）能够准确识别性能问题发生的时间范围，（3）确实有用，可以帮助运维经理恢复SaaS软件的服务能力，（4）比其他方法更能识别系统中的性能问题。
The performance of the software‐as‐a‐service (SaaS) software is often characterized by combinations of performance metrics monitored in a cloud computing platform. Due to the complexity of the application software and the dynamic nature of the deployment environment, manual diagnosis for performance issues based on metric data is typically expensive and laborious. In order to solve the above problems, we propose an automatic performance issue identification method. This approach constructs the hidden Markov random field maximum a posteriori (HMRF‐MAP) model based on the monitored metric values. The model calculates the current performance state of the system by analyzing the historical states of the system. In this paper, we evaluate our approach in a case study of a production system deployed on the cloud computing platform. The evaluation results show that our approach (1) has small system overhead, (2) is accurate in identifying the time frame during which a performance issue occurs, (3) is indeed useful and assists an operation and maintenance manager in recovering the service capability of SaaS software, and (4) is better than other approaches for identifying the performance issues in the system.