Abstract:
Techniques for managing software licensing cost information are disclosed. In one embodiment, license data including licensing cost information associated with a product may be obtained. A license key for the product may be generated by encrypting the license data using an encryption key. The license key and a decryption key may be provided to a management tool associated with a client device. The management tool may be enabled to decrypt the license key using the decryption key to track the licensing cost information associated with the product.
Abstract:
Techniques for optimizing resource claims for containers is described. In one example, resource utilization data associated with at least one container may be obtained for a period. A set of forecasting models may be trained based on the resource utilization data associated with a portion of the period. Resource utilization of the at least one container may be predicted for a remaining portion of the period using the set of trained forecasting models. The predicted resource utilization may be compared with the obtained resource utilization data for the remaining portion of the period. A forecasting model may be determined from the set of trained forecasting models based on the comparison to optimize resource claims for the at least one container.
Abstract:
This disclosure presents computational systems and methods that allocate cost of resources of a cluster of server computers used by virtual machines in a virtual data center. In one aspect, a fair unit rate is computed based on the larger of a measured average utilization or an expected utilization of a cluster resource of server computers within a physical data center by virtual machines. The fair unit rate is a cost per unit of resource used over a period of time and is used to compute an allocated cost of the virtual machine usage of the cluster resource.
Abstract:
Various examples are disclosed for generating a prediction of server requirements needed to deploy an application. The application can be deployed in virtualized environment in which virtual machines can execute the application. The predicted server requirements can be generated based upon data from other deployments of the application in other virtualized environments.
Abstract:
The present disclosure is related to predicting application response lime based on metrics. An example machine-readable medium may store instructions executable by a processing resource to determine a particular response time and an average response time of an application based on a plurality of relevant performance metrics associated with the application during a first period of time, classify the particular response time into a group based on the average response time, and determine a relationship between the plurality of relevant performance metrics and the particular response time of the application. The example machine-readable medium may further store instructions executable by the processing resource to determine whether a response time of the application is likely to change sufficiently to change the classification to a different group during a second period of time based on the relationship.
Abstract:
A process of obtaining, in effect, a multi-virtual-machine snapshot by taking a single-virtual-machine snapshot begins with creating, by a host hypervisor, a host virtual machine and a guest hypervisor. The guest hypervisor executes on the host virtual machine. Virtual machines to be included together in an effective multi-virtual-machine snapshot are migrated to the guest hypervisor. A single-virtual-machine snapshot is taken, by the host hypervisor, of the host virtual machine. The snapshot contains the state data for the virtual machines migrated to the guest hypervisor.
Abstract:
Systems and techniques are described for generating test cases. A described technique includes monitoring a manual test of a code portion. The monitoring can include identifying one or more performed operations performed during the manual test. A seed file can be generated that includes data describing the one or more performed operations. A mock test can be generated for the code portion using the seed file. The mock test can include one or more mock operations that match the one or more performed operations. The mock test can be performed on the code portion, including performing the one or more mock operations using the code portion.
Abstract:
This disclosure presents computational systems and methods for calculating the cost of vCPUs from the cost of CPU computing cycles. In one aspect, a total number of computing cycles used by one or more virtual machines (“VMs”) is calculated based on utilization measurements of a multi-core processor for each VM over a period of time. The method also calculates a total number of virtual CPUs (“vCPUs”) used by the one or more VMs based on vCPU counts for each VM over the period of time. A cost per vCPU is calculated based on the total number of computing cycles, the total number of vCPUs, and cost per computing cycle. The cost per vCPU is stored in a data-storage device. The cost per vCPU can be used to calculate the cost of a VM that uses one or more of the vCPUs.