Towards Systematic and Cost-Effective Monitoring of Large-Scale Software Systems

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Failures of large-scale software systems can have significant impacts on our lives and safety, as well as on the economy and security of our society. For example, failures of the Canada Revenue Agency (CRA) website in March 2017 and March 2019 blocked many Canadians from filing their taxes for days. To detect and address such failures quickly and reduce their impact on users, it is crucial to gain an understanding of the internal state and runtime behavior of these systems. Software observability (i.e., the extent to which the internal state of a system can be inferred) allows software engineers to gain such an understanding, to verify if a system behaves as expected, and to detect and diagnose runtime failures. Software monitoring (e.g., logging and tracing) is the key to ensure the observability of large-scale software systems. Despite the importance of software monitoring, several challenges complicate the monitoring of large-scale software systems. First, large-scale software systems are usually composed of many components (e.g., microservices) that may be developed by different organizations using different programming languages. Furthermore, these components are often evolving. The heterogeneous and evolving nature makes it challenging to ensure consistent and up-to-date monitoring of these systems. Second, as the scale and complexity of software increases, so do the requirements on the computing resources needed to produce and manage the monitoring data, which could introduce significant overhead (e.g., performance and storage overhead) to the operations of large-scale software systems. Third, large-scale software systems usually generate a very large amount of monitoring data from multiple sources (e.g., from different web services and applications), which poses challenges for the analysis and utilization of these data. My long-term research goal is to discover, design, and develop a comprehensive and systematic solution to improve software monitoring and increase the observability of large-scale software systems. To achieve this long-term goal, the proposed research program will tackle the challenges of software monitoring from three mutually complementary perspectives: software development, software execution, and analysis of monitoring data. First, we aim to improve the quality of software monitoring by systematically considering the monitoring aspect across the entire software development lifecycle. Second, we aim to increase the cost-effectiveness of software monitoring by dynamically optimizing the monitoring intensity during software execution. Third, we will develop generic approaches to simplify common workflows of analyzing monitoring data. The proposed research program will be a pioneering attempt to systematically improve the monitoring and observability of large-scale software systems, which will benefit the research and practices in Canada in producing high-quality software.