Verizon Patent and Licensing Inc.

Ryan M O'donnell

Myron Eugene Bagwell

Brent D Segner

Christopher A Rollins

Disclosed are embodiments for profiling active workloads in a cloud platform and downstream applications for improving cloud infrastructure based on the profiling. In one embodiment, a method comprises receiving telemetry vectors from a computing device in a cloud platform, each of the telemetry vectors including a plurality of measured values associated with a workload executing on the computing device; clustering the telemetry vectors using an unsupervised learning algorithm, the unsupervised learning algorithm outputting parameters associated with a plurality of clusters; and generating a workload profile from the plurality of clusters by selecting at least one cluster from the plurality of clusters and storing corresponding parameters of the at least one cluster as the workload profile.

Cloud workload management using workload profiles

A predictive modeling approach to managing processing unit states is disclosed. In an embodiment, a method includes receiving data associated with a host device, including processor statistics. The method generates a feature vector for the host device which includes data derived from the processor statistics and classifies the feature vector using a predictive model. The predictive model can generate a processor state for the host device based on the feature vector. In response, the method can then issue a processor state change message to the host device based on the processor state. The processor state change message causes the host device to reduce power consumption of one or more processing units based on the processor state.

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Phoenix, MD, United States of America

Ryan M O'donnell