Service Load Prediction based on User Knowledge Level Evolution for Software Development Knowledge Base

When using the microservice architecture to build a software development knowledge base, due to the differences in the user's knowledge level, different users have different needs for knowledge, which causes the problem of unpredictable system load. This paper proposes a load prediction model based on User Knowledge Level Evolution (UKLE). We use user historical access data and user private data to build user portraits, learn the evolution law of user knowledge levels, and thus describe the diverse knowledge growth paths of users. Finally, we predict the future knowledge access trends of users. And we use a hybrid prediction model based on linear and non-linear methods to perform load prediction for service load. The final load prediction model combines the characteristics of user knowledge level evolution and a hybrid prediction model based on service historical load, which improves the model's prediction accuracy and robustness.