Abstract: 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.

Abstract: 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.

Abstract: Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. Different configurations may be generated based on the simulation of alternate placements of containers onto nodes, where the placement of a particular container onto a particular node may serve as a root for several branches which may themselves simulate the placement of additional containers on the node (in addition to the container(s) indicated in the root). Once a set of configurations are generated, a particular configuration may be selected according to determined selection parameters and/or intelligent selection techniques.

Abstract: Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. Different configurations may be generated based on the simulation of alternate placements of containers onto nodes, where the placement of a particular container onto a particular node may serve as a root for several branches which may themselves simulate the placement of additional containers on the node (in addition to the container(s) indicated in the root). Once a set of configurations are generated, a particular configuration may be selected according to determined selection parameters and/or intelligent selection techniques.

Abstract: 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.

Abstract: Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. As described, systems described herein may generate configurations based on received parameters for utilization to configure (e.g., install, instantiate, etc.) virtualized resources. Once generated, a configuration may be selected according to determined selection parameters and/or intelligent selection techniques.

Abstract: Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. Different configurations may be generated based on the simulation of alternate placements of containers onto nodes, where the placement of a particular container onto a particular node may serve as a root for several branches which may themselves simulate the placement of additional containers on the node (in addition to the container(s) indicated in the root). Once a set of configurations are generated, a particular configuration may be selected according to determined selection parameters and/or intelligent selection techniques.

Abstract: 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.

Abstract: Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. Different configurations may be generated based on the simulation of alternate placements of containers onto nodes, where the placement of a particular container onto a particular node may serve as a root for several branches which may themselves simulate the placement of additional containers on the node (in addition to the container(s) indicated in the root). Once a set of configurations are generated, a particular configuration may be selected according to determined selection parameters and/or intelligent selection techniques.

Abstract: Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. As described, systems described herein may generate configurations based on received parameters for utilization to configure (e.g., install, instantiate, etc.) virtualized resources.

Abstract: Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. As described, systems described herein may generate configurations based on received parameters for utilization to configure (e.g., install, instantiate, etc.) virtualized resources. Once generated, a configuration may be selected according to determined selection parameters and/or intelligent selection techniques.

Abstract: Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. Different configurations may be generated based on the simulation of alternate placements of containers onto nodes, where the placement of a particular container onto a particular node may serve as a root for several branches which may themselves simulate the placement of additional containers on the node (in addition to the container(s) indicated in the root). Once a set of configurations are generated, a particular configuration may be selected according to determined selection parameters and/or intelligent selection techniques.