Abstract: A system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.

Abstract: Examples provide a network-utilization based scheduler that considers network utilization when load balancing virtual computing instances (VCIs) in a cluster. The scheduler also periodically evaluates proposed migrations based on a current cluster snapshot including network utilization data of the hosts. Proposed migrations are removed and/or prioritized to prevent network saturation in the cluster.

Abstract: Examples provide two-tiered scheduling within a cluster. A coarse-grained analysis is performed on a candidate set of hosts to select a host for a virtual computing instance based on optimization of at least one resource. A host is selected based on the analysis results. The identified virtual computing instance is placed on the selected host. A fine-grained analysis is performed on a set of communication graphs for a plurality of virtual computing instances to generate a set of penalty scores. A set of communicating virtual computing instances are selected based on the set of penalty scores. A first virtual computing instance from a first host is relocated to a second host to minimize a distance between the first virtual computing instance and a second virtual computing instance. Relocating the first virtual computing instance reduces at least one penalty score for the set of communicating virtual computing instances.

Abstract: A system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.

Abstract: Examples provide a network-utilization based scheduler that considers network utilization when load balancing virtual computing instances (VCIs) in a cluster. The scheduler also periodically evaluates proposed migrations based on a current cluster snapshot including network utilization data of the hosts. Proposed migrations are removed and/or prioritized to prevent network saturation in the cluster.

Abstract: A system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.

Abstract: Examples provide a network-utilization based scheduler that considers network utilization when load balancing virtual computing instances (VCIs) in a cluster. The scheduler also periodically evaluates proposed migrations based on a current cluster snapshot including network utilization data of the hosts. Proposed migrations are removed and/or prioritized to prevent network saturation in the cluster.

Abstract: Examples provide two-tiered scheduling within a cluster. A coarse-grained analysis is performed on a candidate set of hosts to select a host for a virtual computing instance based on optimization of at least one resource. A host is selected based on the analysis results. The identified virtual computing instance is placed on the selected host. A fine-grained analysis is performed on a set of communication graphs for a plurality of virtual computing instances to generate a set of penalty scores. A set of communicating virtual computing instances are selected based on the set of penalty scores. A first virtual computing instance from a first host is relocated to a second host to minimize a distance between the first virtual computing instance and a second virtual computing instance. Relocating the first virtual computing instance reduces at least one penalty score for the set of communicating virtual computing instances.

Abstract: Examples provide two-tiered scheduling within a cluster. A coarse-grained analysis is performed on a candidate set of hosts to select a host for a virtual computing instance based on optimization of at least one resource. A host is selected based on the analysis results. The identified virtual computing instance is placed on the selected host. A fine-grained analysis is performed on a set of communication graphs for a plurality of virtual computing instances to generate a set of penalty scores. A set of communicating virtual computing instances are selected based on the set of penalty scores. A first virtual computing instance from a first host is relocated to a second host to minimize a distance between the first virtual computing instance and a second virtual computing instance. Relocating the first virtual computing instance reduces at least one penalty score for the set of communicating virtual computing instances.

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 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. Data and compute nodes are separated into different virtual machines (VM). Compute VMs are used to launch containers from different tenants. Compute VMs are organized in pools of hot spare VMs that are immediately available for launching a container and executing a task, and pools of cold spare VMs. Each compute VM may include a mounted network filesystem provided by a node manager to share intermediate outputs across VMs executing on the same host.

Abstract: Examples provide a network-utilization based scheduler that considers network utilization when load balancing virtual computing instances (VCIs) in a cluster. The scheduler also periodically evaluates proposed migrations based on a current cluster snapshot including network utilization data of the hosts. Proposed migrations are removed and/or prioritized to prevent network saturation in the cluster.

Abstract: Examples provide two-tiered scheduling within a cluster. A coarse-grained analysis is performed on a candidate set of hosts to select a host for a virtual computing instance based on optimization of at least one resource. A host is selected based on the analysis results. The identified virtual computing instance is placed on the selected host. A fine-grained analysis is performed on a set of communication graphs for a plurality of virtual computing instances to generate a set of penalty scores. A set of communicating virtual computing instances are selected based on the set of penalty scores. A first virtual computing instance from a first host is relocated to a second host to minimize a distance between the first virtual computing instance and a second virtual computing instance. Relocating the first virtual computing instance reduces at least one penalty score for the set of communicating virtual computing instances.

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 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 system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.

Abstract: This disclosure is directed to systems and methods for mining streams of telemetry data in order to identify virtual machines (“VMs”), discover relationships between groups of VMs, and evaluate VM performance problems. The systems and methods transform streams of raw telemetry data consisting of resource usage and VM-related metrics into information that may be used to identify each VM, determine which VMs are similar based on their telemetry data patterns, and determine which VMs are similar based on their patterns of resource consumption. The similarity patterns can be used to group VMs that run the same applications and diagnose and debug VM performance.

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. Data and compute nodes are separated into different virtual machines (VM). Compute VMs are used to launch containers from different tenants. Compute VMs are organized in pools of hot spare VMs that are immediately available for launching a container and executing a task, and pools of cold spare VMs. Each compute VM may include a mounted network filesystem provided by a node manager to share intermediate outputs across VMs executing on the same host.

Abstract: Embodiments perform opportunistically proactive resource scheduling for a plurality of resource-consuming entities. The scheduling is based on both current entitlement (or demand) by the entities and predicted future entitlement (or demand) by the entities. Resources are allocated based on the current demands, while any remaining resource capacity is further allocated to entities based on predicted demands. In some embodiments, the scheduling is performed on a cluster of hosts executing a plurality of virtual machines (VMs) in a virtualized datacenter to implement load balancing.

Abstract: This disclosure is directed to systems and methods for mining streams of telemetry data in order to identify virtual machines (“VMs”), discover relationships between groups of VMs, and evaluate VM performance problems. The systems and methods transform streams of raw telemetry data consisting of resource usage and VM-related metrics into information that may be used to identify each VM, determine which VMs are similar based on their telemetry data patterns, and determine which VMs are similar based on their patterns of resource consumption. The similarity patterns can be used to group VMs that run the same applications and diagnose and debug VM performance.