Abstract:
A distributed computing application is described that provides a highly elastic and multi-tenant platform for Hadoop applications and other workloads running in a virtualized environment. Multiple instances of a distributed computing framework, such as Hadoop, may be executed concurrently. A centralized manager detects when contention for computing resources, such as memory and CPU, causes tasks to run slower on VMs executing on a given host, and scales up or scales down a cluster based on the detected resource contention.
Abstract:
A system and method for allocating power resources among host computers in a cluster uses lower and upper bounds with respect to a power budget to be distributed to each of the hosts. Each host is allocated a portion of the cluster power capacity. Any excess amount of the capacity is then allocated to the hosts based at least partly on the lower bound (reserve capacity) and the upper bound (host power limit) of each of the clients.
Abstract:
Application resource scheduler module is provided to achieve cooperative application workload scheduling for a consolidated virtual environment. The application resource scheduler aids an application workload scheduler that is part of a distributed computing application, such as Hadoop, to achieve a specified relative priority of the application workload virtual machines to other virtual machines in the virtual environment. The application resource scheduler assists in achieving cooperative workload scheduling by revising the amount of resources that the application workload scheduler sees as available and by setting resource controls for the virtual machines of the distributed computing application to influence the resources the virtual machines receive from the underlying consolidated virtual environment.
Abstract:
In a virtualized system running one or more virtual machines on a first hypervisor, a second hypervisor is installed and control of the hardware resources of the physical computer supporting the virtualized system is migrated from the first hypervisor to the second hypervisor without interrupting the operation of the first hypervisor and the virtual machines. Initially a minimal set of hardware resources is hot-removed from control by the first hypervisor, and the second hypervisor is launched on the minimal set of hardware resources. Both the remaining hardware resources and the virtual machines are then migrated from the first hypervisor to the second hypervisor until all the virtual machines have been migrated over to the second hypervisor, while the virtual machines and the first hypervisor continue running largely unaffected by the migration process.
Abstract:
A system and method for allocating power resources among host computers in a cluster uses lower and upper bounds with respect to a power budget to be distributed to each of the hosts. Each host is allocated a portion of the cluster power capacity. Any excess amount of the capacity is then allocated to the hosts based at least partly on the lower bound (reserve capacity) and the upper bound (host power limit) of each of the clients.
Abstract:
A method of determining compatibility of a virtual machine or virtual machine disk file with a host, including a storage host, is disclosed. A lookup matrix is created to provide a fast compatibility lookup. To create the lookup matrix, computing object properties are retrieved from each of the plurality of computing objects. The computing object properties include resource requirements of each of the plurality of computing objects. Further, host properties are retrieved from each of the plurality of hosts. The method further includes creating host logical groups of a subset of the plurality of hosts having substantially same host properties and creating a plurality of computing object logical groups of a subject of the plurality of computing objects having substantially same computing object properties. The lookup matrix answers whether each member of a selected computing object logical group can be transferred to a selected host logical group.
Abstract:
Application resource scheduler module is provided to achieve cooperative application workload scheduling for a consolidated virtual environment. The application resource scheduler aids an application workload scheduler that is part of a distributed computing application, such as Hadoop, to achieve a specified relative priority of the application workload virtual machines to other virtual machines in the virtual environment. The application resource scheduler assists in achieving cooperative workload scheduling by revising the amount of resources that the application workload scheduler sees as available and by setting resource controls for the virtual machines of the distributed computing application to influence the resources the virtual machines receive from the underlying consolidated virtual environment.