Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis. (16th November 2021)

• Record Type: Journal Article
• Authors: Ezzati-Jivan, Naser; Daoud, Houssem; Dagenais, Michel R.
• Other Names: Zhang Yong Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-11-16
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/8478076
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Root cause identification of performance degradation within distributed systems is often a difficult and time-consuming task, yet it is crucial for maintaining high performance. In this paper, we present an execution trace-driven solution that reduces the efforts required to investigate, debug, and solve performance problems found in multinode distributed systems. The proposed approach employs a unified analysis method to represent trace data collected from the user-space level to the hardware level of involved nodes, allowing for efficient and effective root cause analysis. This solution works by extracting performance metrics and state information from trace data collected at user-space, kernel, and network levels. The multisource trace data is then synchronized and structured in a multidimensional data store, which is designed specifically for this kind of data. A posteriori analysis using a top-down approach is then used to investigate performance problems and detect their root causes. In this paper, we apply this generic framework to analyze trace data collected from the execution of the web server, database server, and application servers in a distributed LAMP (Linux, Apache, MySQL, and PHP) Stack. Using industrial level use cases, we show that the proposed approach is capable of investigating the root cause of performance issues, addressing unusual latency, and improving base latency by 70%. This is achieved with minimal tracing overhead that does not significantly impact performance, as well as O log n query response times for efficient analysis.