Multivariate time series machine learning model for classifying network-intensive cloud workloads

Abstract

Techniques are described for a compute optimizer service of a cloud provider network that uses a multivariate time series machine learning (ML) model to identify network-intensive cloud workloads and to provide recommendations for optimizing such workloads. A network-intensive workload, for example, broadly represents any computing workload that uses relatively more network resources than typical workloads (e.g., such as load balancers, web servers, and the like). The ML model combines a set of weak learners and a meta-model on top of the weak learners to infer the final predictions of whether workloads are network-intensive workloads. The ML model identifies network-intensive workloads in part by recognizing the patterns within the time series data reflecting certain resource usage patterns rather than only deriving embedded statistical information, thereby enabling the model to more accurately identify network-intensive workloads.