Abstract:
Aspects of performing power management operations in a distributed computer system are described. In some aspects, predicted demand data is generated for clients executed in a cluster of host computers. The predicted demand data is based on observed resource demands of the clients. A power management setting for a time period is determined. The power management setting is based on the predicted demand data. A host computer is caused to power-down or power-up in order to apply the power management setting.
Abstract:
A resource management system and method for performing resource capacity management in a cluster of host computers uses a snapshot of the cluster with one or more ghost host computers added to the cluster to execute a power management analysis. A ghost host computer is a fictitious construct based on a physical host computer. The results of the power management analysis may then be used as a cluster capacity recommendation to increase resource capacity of the cluster of host computers.
Abstract:
A system and method for performing a resource allocation diagnosis on a distributed computer system includes computing current resource allocation of the distributed computer system using a current snapshot of the distributed computer system. The current snapshot includes configurations and resource usage information of at least some components of the distributed computer system. The system and method also includes computing improved resource allocation of the distributed computer system using a modified version of the current snapshot of the distributed computer system and outputting the current resource allocation and the improved resource allocation for the resource allocation diagnosis.
Abstract:
Embodiments of a non-transitory computer-readable storage medium and a computer system are disclosed. In an embodiment, a non-transitory computer-readable storage medium containing program instructions for managing host computers that run virtual machines into host-groups within a cluster is disclosed. When executed, the instructions cause one or more processors to perform steps including determining if a virtual machine entity needs additional resources and, if the virtual machine entity needs additional resources, mapping a host computer to a host-group with which the virtual machine entity is associated.
Abstract:
A system and method for performing customized remote resource allocation analyzes on distributed computer systems utilizes a snapshot of a distributed computer system, which is received at a remote resource allocation module, to perform a resource allocation analysis using a resource allocation algorithm. The resource allocation algorithm is selected from a plurality of resource allocation algorithms based on at least one user-provided parameter associated with the distributed computer system.
Abstract:
A resource management system and method for performing resource management operations in a distributed computer system uses a dispersion rule to try to uniformly disperse clients in a cluster of host computers in the distributed computer system. The dispersion rule is used to compute a dispersion score for at least one candidate distribution of the clients in the cluster of host computers, which is used to select a particular candidate distribution of the clients in the cluster of host computers to disperse the clients.
Abstract:
A system and method for managing resources in a distributed computer system that includes at least one resource pool for a set of virtual machines (VMs) utilizes a set of desired individual VM-level resource settings that corresponds to target resource allocations for observed performance of an application running in the distributed computer system. The set of desired individual VM-level resource settings are determined by constructing a model for the observed application performance as a function of current VM-level resource allocations and then inverting the function to compute the target resource allocations in order to meet at least one user-defined service level objective (SLO). The set of desired individual VM-level resource settings are used to determine final RP-level resource settings for a resource pool to which the application belongs and final VM-level resource settings for the VMs running under the resource pool, which are then selectively applied.
Abstract:
A method for supporting a change in state within a cluster of host computers that run virtual machines is disclosed. The method involves identifying a change in state within a cluster of host computers that run virtual machines, determining if predefined criteria for available resources within the cluster of host computers can be met by resources available in the cluster of host computers, and determining if predefined criteria for available resources within the cluster of host computers can be maintained after at least one different predefined change in state. In an embodiment, the steps of this method may be implemented in a non-transitory computer-readable storage medium having instructions that, when executed in a computing device, causes the computing device to carry out the steps.
Abstract:
Embodiments of a non-transitory computer-readable storage medium and a computer system are disclosed. In an embodiment, a non-transitory computer-readable storage medium containing program instructions for managing host computers that run virtual machines into host-groups within a cluster is disclosed. When executed, the instructions cause one or more processors to perform steps including determining if a virtual machine entity needs additional resources and, if the virtual machine entity needs additional resources, mapping a host computer to a host-group with which the virtual machine entity is associated.
Abstract:
A system and method for autoscaling a multi-tier application, that has components executing on a plurality of tiers of a virtual data center, allocates resources to each of the plurality of tiers based on cost and performance. An application performance is determined, and a new application performance is estimated based at least partially on an application reservation and an application limit. An optimized utility of the application is calculated based on the cost to execute the application, the application reservation, and the application limit. A scaling factor for each tier is then determined to scale up or down a number of virtual machines operating in each of the tiers.