Using sequential drift detection to test the API economy
This addresses the need for monitoring API microservices to warn analysts of novel usage patterns, though it appears incremental as it builds on existing drift detection techniques.
The paper tackled the problem of detecting shifts in API usage patterns to ensure uninterrupted system operation, by analyzing histograms and call graphs and comparing nonparametric statistical and Bayesian sequential analysis methods, proving effective in simulations with various scenarios.
The API economy refers to the widespread integration of API (advanced programming interface) microservices, where software applications can communicate with each other, as a crucial element in business models and functions. The number of possible ways in which such a system could be used is huge. It is thus desirable to monitor the usage patterns and identify when the system is used in a way that was never used before. This provides a warning to the system analysts and they can ensure uninterrupted operation of the system. In this work we analyze both histograms and call graph of API usage to determine if the usage patterns of the system has shifted. We compare the application of nonparametric statistical and Bayesian sequential analysis to the problem. This is done in a way that overcomes the issue of repeated statistical tests and insures statistical significance of the alerts. The technique was simulated and tested and proven effective in detecting the drift in various scenarios. We also mention modifications to the technique to decrease its memory so that it can respond more quickly when the distribution drift occurs at a delay from when monitoring begins.