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

A computer-implemented method comprising:

• obtaining, by one or more computing devices, time series data corresponding to performance metrics of a workload deployed by a user to a cloud provider network, wherein the workload executes using at least one virtual machine (VM) instance provided by the cloud provider network;
• generating a plurality of sets of features based on the time series data, wherein at least one set of features is generated based on applying multiple different convolutional kernels to the time series data;
• providing the plurality of sets of features based on the time series data to an ensemble of heterogeneous classifiers that were independently trained using different feature extraction processes to obtain a plurality of preliminary result values, wherein each preliminary result value indicates a first probability that the workload is a network-intensive workload;
• providing the plurality of preliminary result values to a meta-classifier configured to combine the preliminary result values to obtain a final result value, wherein the final result value indicates a second probability that the workload is a network-intensive workload based on the plurality of preliminary result values;
• determining, based on the second probability, that the workload is a network-intensive workload;
• identifying, based on the determination that the workload is a network-intensive workload, a different type of VM instance that is more suitable for the workload;
• and
• causing display of a user interface providing a recommendation identifying the different type of VM instance.

A system comprising:

• a first one or more electronic devices to implement a compute optimizer service in a multi-tenant provider network, wherein the compute optimizer service includes instructions that upon execution cause the compute optimizer service to:
• obtain, from a monitoring service of the multi-tenant provider network, time series data corresponding to performance metrics of a workload deployed by a user to a cloud provider network, wherein the workload executes using at least one virtual machine (VM) instance provided by the cloud provider network,
• generate a plurality of sets of features based on the time series data, wherein at least one set of features is generated based on applying multiple different convolutional kernels to the time series data;
• provide the plurality of sets of features based on the time series data to an ensemble of heterogeneous classifiers trained using different feature extraction processes to obtain a plurality of preliminary result values, wherein each preliminary result value indicates a first probability that the workload is a network-intensive workload,
• provide the plurality of preliminary result values to a meta-classifier configured to combine the preliminary result values to obtain a final result value, wherein the final result value indicates a second probability that the workload is a network-intensive workload based on the plurality of preliminary result values,
• determine, based on the second probability, that the workload is a network-intensive workload,
• identify, based on the determination that the workload is a network-intensive workload, a different type of VM instance that is more suitable for the workload, and
• cause display of a user interface providing a recommendation identifying the different type of VM instance;
• and
• a second one or more electronic devices to implement the monitoring service in the multi-tenant provider network, wherein the monitoring service includes instructions that upon execution cause the monitoring service to:
• monitor execution of the workload, and
• collect the time series data corresponding to performance metrics of the workload.