Ca, Inc.

John Wiley Ashby, Jr

Paul David Peterson

Francois Marie Bruno Cattoen

Peter James Gilbert

James Mercer

Lara Virginia Greden

Salvatore Pilo

Richard John Wcislicki Lankester

Dhesikan Ananchaperumal

Balaji Varadaraju

A method includes retrieving capacity utilization data for a plurality of resources and applying a linear regression analysis on the capacity utilization data. The method further includes projecting, using a processor, the capacity utilization data through a future time based on results of the linear regression analysis. The method additionally includes determining a deviation from a predetermined threshold range in the projected capacity utilization data for a first resource, and, in response to determining the deviation, determining, for each of a plurality of resource configurations, future capacity utilization of the first resource based on a non-linear capacity consumption model corresponding to the resource configuration. The method also includes applying a selected resource configuration from the plurality of resource configurations to the first resource to prevent the first resource from deviating from the predetermined threshold range.

Selecting resources for automatic modeling using forecast thresholds

A method includes receiving an expected growth rate for modeling future capacity utilization for a resource hosting a plurality of processes, each process being associated with a respective workload. The method also includes modifying, in a non-linear capacity utilization model for the resource, the respective workload for a particular process in the plurality of processes based on the expected growth rate, and, in response to modifying the respective workload, determining a change in total capacity utilization for the resource using the non-linear capacity utilization model. The method further includes determining a ratio between the change in the total capacity utilization for the resource and the modification to the respective workload. The method additionally includes modifying, based on the ratio, a configuration of the resource with respect to the particular process in anticipation of the expected growth rate.

Modeling real capacity consumption changes using process-level data

A resource management node includes a processor and a memory coupled to the processor. The memory includes computer readable program code that when executed by the processor causes the processor to perform operations. The operations can include, for each of a plurality of guest virtual machines (VMs), determining operational resources of physical host machines available in a distributed computing system that are needed to provide the guest VM. A placement scenario for placing the guest VMs on the physical host machines and placing the physical host machines in cabinets of a distributed computing system is generated. An amount of infrastructure of the distributed computing system used by the placement scenario is determined. A determination is made whether the placement scenario satisfies a defined rule for how much infrastructure of the distributed computing system can be used. Placement of the physical host machines in the cabinets and placement of the guest VMs on the physical host machines is initiated based on the placement scenario satisfying the defined rule.

Deployment of virtual machines to physical host machines based on infrastructure utilization decisions

A method includes accessing a database comprising a respective non-linear scalability model for each of a plurality of physical resources and each of a plurality of virtual resources in a virtualization environment. The method also includes generating a respective capacity consumption model for each of the plurality of virtual resources based on the non-linear scalability models. The method further includes determining a deviation from a predetermined threshold range in the respective capacity consumption model for a first virtual resource in the plurality of virtual resources. The method additionally includes determining a slope of the respective capacity consumption model for the first virtual resource, and determining a sizing recommendation for components of the first virtual resource based on the slope and the deviation. The method also includes modifying at least one of the components of the first virtual resource based on the sizing recommendation.

Automatic modeling of virtual resources with sizing recommendations

A resource management node includes a processor and a memory coupled to the processor. The memory includes computer readable program code that when executed by the processor causes the processor to perform operations. The operations can include, for each of a plurality of guest virtual machines (VMs), determining operational resources of physical host machines available in a distributed computing system that are needed to provide the guest VM. The operations can further include determining an amount of infrastructure of a distributed computing system used by a defined placement of physical host machines in racks of the distributed computing system.

Controlling placement of virtual machines on physical host machines and placement of physical host machines in cabinets

A resource management node includes a processor and a memory coupled to the processor. The memory includes computer readable program code that when executed by the processor causes the processor to perform operations. The operations can include identifying an unproductive group of guest virtual machines (VMs) that satisfy a defined rule for being unproductive on an first group of physical host machines based on infrastructure of a distributed computing system used by the first group of the physical host machines to host the guest VMs. A relocation scenario is generated for relocating the unproductive group of the guest VMs from the first group of the physical host machines to a second group of the physical host machines. An effect of the relocation scenario on infrastructure usage of the distributed computing system is estimated. The unproductive group of the guest VMs is relocated from the first group of the physical host machines to the second group of the physical host machines based on the relocation scenario.

Decommissioning physical host machines after relocating unproductive virtual machines therefrom

Some embodiments disclosed herein are directed to a resource management node having a processor and memory coupled to the processor. The memory includes computer readable program code that when executed by the processor causes the processor to perform operations. The operations include, for each of a plurality of guest virtual machines (VMs) of a VM cluster provided by computer operations of a physical machine among a plurality of physical machines within a distributed computing system, determining a productivity metric for the guest VM based on resources of the physical machine that are used by the guest VM. The operations further include for each of the guest VMs, determining, based on the productivity metric for the guest VM, an infrastructure value that indicates how much infrastructure of the distributed computing system is attributed to providing the guest VM.

Growing community of inventors

Austin, TX, United States of America

John Wiley Ashby, Jr