Abstract:
A system and method for performing a hypothetical power management analysis on a distributed computer system uses chronologically consecutive snapshots of the distributed computer system. The snapshots are used to extract demands of clients running in the distributed computer system for a resource for different time intervals, which are then stitched together to produce a workload trace. The snapshots and the workload trace are used to construct modeling scenarios for the distributed computer system. The modeling scenarios are used to perform analyzes to simulate the operation of the distributed computer system during which the power management module is enabled to compute potential power savings.
Abstract:
A management system and method for remediating poor-performing clients running in a distributed computer system uses a machine learning technique to automatically detect one or more poor-performing clients among a plurality of clients running in the distributed computer based on at least performance data and resource usage data of the clients. An action is then initiated to mitigate the effects of the poor-performing clients.
Abstract:
A management server and method for performing resource management operations in a distributed computer system uses at least one sampling parameter to estimate demand of a client for a resource. The sampling parameter has a correlation with at least one target performance goal of an application that the client is running. The demand estimation can then be used to make at least one decision in a resource management operation.
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 system and method for performing a resource allocation diagnosis on a distributed computer system includes obtaining a target resource allocation and a snapshot of the distributed computer system, where the snapshot includes configurations and resource usage information of at least some components of the distributed computer system, and generating a resource allocation recommendation based on the target resource allocation and the snapshot by iteratively traversing a resource hierarchy in the distributed computer system. The resource allocation recommendation specifies at least one resource configuration action or at least one capacity expansion action for the distributed computer system to meet the target resource allocation.
Abstract:
Disclosed are aspects of proactive high availability that proactively identify and predict hardware failure scenarios and migrate virtual resources to healthy hardware resources. In some aspects, a mapping that maps virtual resources to hardware resources. A plurality of hardware events are identified in association with a hardware resource. A hardware failure scenario is predicted based on a health score of a first hardware resource. A health score is determined based on the hardware events, and a fault model that indicates a level of severity of the hardware events. A particular virtual resource is migrated from the hardware resource to another hardware that has a greater health score.
Abstract:
A method for scheduling computing resources without container migration includes determining a resource availability for one or more hosts, a resource allocation for one or more virtual machines (VMs), and a resource usage for one or more containers. The method further includes calculating a target resource configuration for one or more VMs, wherein calculating a target resource configuration comprises determining an upper limit of resource demand on a VM from one or more containers allocated on the VM, based at least in part on the resource usage. The method also includes removing or adding resources to each of the one or more VMs for which a target resource configuration was calculated to achieve the target resource configuration for each VM. The method further includes allocating the one or more VMs on the one or more hosts based on the resource availability of the one or more hosts.
Abstract:
System and method for performing resource allocation for a host computer cluster use resource allocation weight scores for resource nodes in a cluster resource allocation hierarchy of the host computer cluster based on the number of powered-on clients in the resource nodes.
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:
System and method for performing resource allocation for a host computer cluster use resource allocation weight scores for resource nodes in a cluster resource allocation hierarchy of the host computer cluster based on the number of powered-on clients in the resource nodes.