Abstract: An example system includes memory, programmable circuitry, and machine readable instructions to program the programmable circuitry to: obtain utilization metric information corresponding to utilization metrics collected over a time interval, the utilization metrics corresponding to allocated resources utilized by containers, the containers associated with a cluster, obtain a request to generate priority classes for the containers in the cluster, the priority classes indicative of which containers have a greater priority in the cluster, and generate the priority classes for the containers based on the utilization metric information and a count of network interactions corresponding to the containers for the time interval.

Abstract: In an example, a cloud service management node includes a knowledge base having a plurality of billing rules for a cloud computing environment, a processor, and a memory coupled to the processor. The memory may include a discount predictor module to receive an actual bill related to consumption of a cloud service in the cloud computing environment. Further, the discount predictor module may determine a variation between the actual bill and an expected cost from a public rate card by comparing the actual bill with the expected cost. Furthermore, the discount predictor module may evaluate the plurality of billing rules to predict a discount type and a discount associated with the discount type that matches the variation between the actual bill and the expected cost from the public rate card. Further, the discount predictor module may output the discount type and the discount on an interactive user interface.

Abstract: An example system includes memory, programmable circuitry, and machine readable instructions to program the programmable circuitry to: obtain utilization metric information corresponding to utilization metrics collected over a time interval, the utilization metrics corresponding to allocated resources utilized by containers, the containers associated with a cluster, obtain a request to generate priority classes for the containers in the cluster, the priority classes indicative of which containers have a greater priority in the cluster, and generate the priority classes for the containers based on the utilization metric information and a count of network interactions corresponding to the containers for the time interval.

Abstract: An example apparatus includes memory, and at least one processor to execute instructions to assign first containers to a first cluster and second containers to a second cluster based on the first containers including first allocated resources that satisfy a first threshold number of allocated resources and the second containers including second allocated resources that satisfy a second threshold number of allocated resources, determine a representative interaction count value for a first one of the first containers, the representative interaction count value based on a first network interaction metric corresponding to an interaction between the first one of the first containers and a combination of at least one of the first containers and at least one of the second containers, and generate a priority class for the first one of the first containers based on the representative interaction count value.

Abstract: Disclosed are various embodiments for generating recommended replacement host machines for a datacenter. The recommendations can be generated based upon an analysis of historical workload usage across the datacenter. Clusters can be generated that cluster workloads together that are similar. Purchase plans can be generated based upon the identified clusters and benchmark data regarding servers.

Abstract: Disclosed are various embodiments for generating recommended replacement host machines for a datacenter. The recommendations can be generated based upon an analysis of historical workload usage across the datacenter. Clusters can be generated that cluster workloads together that are similar. Purchase plans can be generated based upon the identified clusters and benchmark data regarding servers.

Abstract: A system and method for sampling application programming interface (API) execution traces in a computer system uses feature vectors of the API execution traces that are generated using trace-context information. The feature vectors are then used to group the API execution traces into clusters. For the cluster, sampling rates are generated so that a sampling rate is assigned to each of the clusters. The sampling rates are then applied to the API execution traces to adaptively sample the API execution traces based on the clusters to which the API execution traces belong.

Abstract: Various examples are disclosed for generating metrics for quantifying computing resource usage. A computing environment can identify a computing function that utilizes a plurality of computing services hosted in at least one virtual machine. The computing environment can determine a first cost metric for the at least one virtual machine based on hardware resources used by the at least one virtual machine and determine a second cost metric for individual ones of the computing services based on virtual machine resources used by the individual ones of the computing services and the first cost metric. A third cost metric can be determined for the computing function as a function of the second cost metric and a utilization ratio.

Abstract: Disclosed are various embodiments for generating recommended replacement host machines for a datacenter. The recommendations can be generated based upon an analysis of historical workload usage across the datacenter. Clusters can be generated that cluster workloads together that are similar. Purchase plans can be generated based upon the identified clusters and benchmark data regarding servers.

Abstract: An example apparatus includes memory, and at least one processor to execute instructions to assign first containers to a first cluster and second containers to a second cluster based on the first containers including first allocated resources that satisfy a first threshold number of allocated resources and the second containers including second allocated resources that satisfy a second threshold number of allocated resources, determine a representative interaction count value for a first one of the first containers, the representative interaction count value based on a first network interaction metric corresponding to an interaction between the first one of the first containers and a combination of at least one of the first containers and at least one of the second containers, and generate a priority class for the first one of the first containers based on the representative interaction count value.

Abstract: Example methods and apparatus to determine container priorities in virtualized computing environments are disclosed herein. Examples include: a cluster controller to classify a first container into a cluster based on the first container having a number of distinct allocated resources within a threshold number of distinct allocated resources corresponding to a second container; a container ranking generator to: determine resource utilization rank values for a resource usage type of a number of distinct allocated resources, the resource utilization rank values indicative that the first container utilizes the resource usage type more than the second container; determine an aggregated resource utilization rank value for the first container based on aggregating ones of the resource utilization rank values corresponding to the first container; and a container priority controller to generate a priority class for the first container based on the aggregated resource utilization rank value.

Abstract: Example methods and apparatus to determine container priorities in virtualized computing environments are disclosed herein. Examples include: a cluster controller to classify a first container into a cluster based on the first container having a number of distinct allocated resources within a threshold number of distinct allocated resources corresponding to a second container; a container ranking generator to: determine resource utilization rank values for a resource usage type of a number of distinct allocated resources, the resource utilization rank values indicative that the first container utilizes the resource usage type more than the second container; determine an aggregated resource utilization rank value for the first container based on aggregating ones of the resource utilization rank values corresponding to the first container; and a container priority controller to generate a priority class for the first container based on the aggregated resource utilization rank value.

Abstract: A system and method for sampling application programming interface (API) execution traces in a computer system uses feature vectors of the API execution traces that are generated using trace-context information. The feature vectors are then used to group the API execution traces into clusters. For the cluster, sampling rates are generated so that a sampling rate is assigned to each of the clusters. The sampling rates are then applied to the API execution traces to adaptively sample the API execution traces based on the clusters to which the API execution traces belong.

Abstract: Various aspects are disclosed for optimization of dependent systems for serverless frameworks. In some examples, a load test executes instances of a function on a dependent system to generate datapoints. The datapoints are organized, using a clustering algorithm, into an acceptable group and at least one unacceptable group. A maximum number of concurrent instances of the function is determined based on a number of instances specified by at least one datapoint selected from the acceptable group. A live workload is performed on the dependent system. The live workload includes instances of the function that are assigned to the dependent system according to the maximum number of concurrent instances.

Abstract: Various aspects are disclosed for optimization of dependent systems for serverless frameworks. In some examples, a load test executes instances of a function on a dependent system to generate datapoints. The datapoints are organized, using a clustering algorithm, into an acceptable group and at least one unacceptable group. A maximum number of concurrent instances of the function is determined based on a number of instances specified by at least one datapoint selected from the acceptable group. A live workload is performed on the dependent system. The live workload includes instances of the function that are assigned to the dependent system according to the maximum number of concurrent instances.

Abstract: Various examples are disclosed for generating metrics for quantifying computing resource usage. A computing environment can identify a computing function that utilizes a plurality of computing services hosted in at least one virtual machine. The computing environment can determine a first cost metric for the at least one virtual machine based on hardware resources used by the at least one virtual machine and determine a second cost metric for individual ones of the computing services based on virtual machine resources used by the individual ones of the computing services and the first cost metric. A third cost metric can be determined for the computing function as a function of the second cost metric and a utilization ratio.