Abstract: A system and method for allocating a resource among clients running on host computers using capacity distribution uses lower and upper bounds with respect to a capacity to be distributed to each of the clients. Each client is allocated a portion of the capacity that corresponds to the lower bound for that client. Any excess amount of the capacity is then allocated to the clients based at least partly on the lower bound and the upper bound of each of the clients.

Abstract: In one embodiment, a method for placing virtual machines in a collection is provided. A plurality of equivalence sets of compatible hosts is determined prior to placing virtual machines in the collection. The hosts in an equivalence set of hosts are considered similar. An equivalence set of hosts in the plurality of equivalence sets is selected to place the virtual machines in the collection. The method then places at least a portion of the virtual machines in the collection on one or more hosts in the selected equivalence set of hosts.

Abstract: One or more embodiments of the present invention provide a technique for effectively managing virtualized computing systems with an unlimited number of hardware resources. Host systems included in a virtualized computer system are organized into a scalable, peer-to-peer (P2P) network in which host systems arrange themselves into a network overlay to communicate with one another. The network overlay enables the host systems to perform a variety of operations, which include dividing computing resources of the host systems among a plurality of virtual machines (VMs), load balancing VMs across the host systems, and performing an initial placement of a VM in one of the host systems.

Abstract: A system and method for placing a client in a computer network system uses continuously variable weights to resource utilization metrics for each candidate device, e.g., a host computer. The weighted resource utilization metrics are used to compute selection scores for various candidate devices to select a target candidate device for placement of the client.

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: A system and method for providing quality of service (QoS) for clients running on host computers to access a common resource uses a resource pool module and a local scheduler in at least one of the host computers. The resource pool module operates to compute an entitlement of each client for the common resource based on a current capacity for the common resource and demands of the clients for the common resource. In addition, the resource pool module operates to assign a portion of the computed current capacity for the common resource to a particular host computer using the computed entitlement of each client running on the particular host computer. The local scheduler operates to allocate the portion of the computed current capacity among the clients running on the particular host computer.

Abstract: Systems and methods described herein manage a computing device. A method includes receiving a threshold for an operating condition of a first computing device. An expected resource utilization of a computer program is determined. In addition, the method determines whether the computer program may be executed within the first computing device based on the operating condition threshold and the expected resource utilization of the computer program.

Abstract: One or more embodiments of the present invention provide a technique for effectively managing virtualized computing systems with an unlimited number of hardware resources. Host systems included in a virtualized computer system are organized into a scalable, peer-to-peer (P2P) network in which host systems arrange themselves into a network overlay to communicate with one another. The network overlay enables the host systems to perform a variety of operations, which include dividing computing resources of the host systems among a plurality of virtual machines (VMs), load balancing VMs across the host systems, and performing an initial placement of a VM in one of the host systems.

Abstract: One or more embodiments of the present invention provide a method for performing initial placement and load balancing of a data objects in a distributed system. The distributed system includes hardware resources, e.g., host systems and storage arrays, which are configured to execute and/or store data objects. A data object is initially placed into the distributed system by creating a virtual cluster of hardware resources that are compatible to execute and/or host the data object, and then selecting from the virtual cluster a hardware resource that is optimal for executing and/or hosting the data object. The data object is placed into the selected hardware resource, whereupon a load balancing operation is optionally performed across the virtual cluster. The virtual cluster is subsequently released, and the distributed system is returned to its original state with the data object included therein.

Abstract: One or more embodiments of the present invention provide a technique for effectively managing virtualized computing systems with an unlimited number of hardware resources. Host systems included in a virtualized computer system are organized into a scalable, peer-to-peer (P2P) network in which host systems arrange themselves into a network overlay to communicate with one another. The network overlay enables the host systems to perform a variety of operations, which include dividing computing resources of the host systems among a plurality of virtual machines (VMs), load balancing VMs across the host systems, and performing an initial placement of a VM in one of the host systems.

Abstract: In one embodiment, a method for placing virtual machines in a collection is provided. A plurality of equivalence sets of hosts is determined prior to placing virtual machines in the collection. The hosts in an equivalence set of hosts are considered similar. An equivalence set of hosts in the plurality of equivalence sets is selected to place the virtual machines in the collection. The method then places at least a portion of the virtual machines in the collection on one or more hosts in the selected equivalence set of hosts.

Abstract: Maximum throughput of a storage unit, and workload and latency values of the storage unit corresponding to a predefined fraction of the maximum throughput are estimated based on workloads and latencies that are monitored on the storage unit. The computed metrics are usable in a variety of different applications including admission control, storage load balancing, and enforcing quality of service in a shared storage environment.

Abstract: Methods and systems for allocating resources in a virtual desktop resource environment are provided. A method includes making a prediction on the future demand for processes running on a distributed environment with several hosts. The prediction is based on the process demand history and includes the removal of historic process demand glitches. Further, the prediction is used to perform a cost and benefit analysis for moving a candidate process from one host to another, and the candidate process is moved to a different host when the cost and benefit analysis recommends such move. In another embodiment, the predictions on future process demand are used for distributed power management by putting hosts in stand-by mode when the overall demand decreases or by adding hosts to the distributed environment when the load increases.

Abstract: Methods and systems for allocating resources in a virtual desktop resource environment are provided. A method includes making a prediction on the future demand for processes running on a distributed environment with several hosts. The prediction is based on the process demand history and includes the removal of historic process demand glitches. Further, the prediction is used to perform a cost and benefit analysis for moving a candidate process from one host to another, and the candidate process is moved to a different host when the cost and benefit analysis recommends such move. In another embodiment, the predictions on future process demand are used for distributed power management by putting hosts in stand-by mode when the overall demand decreases or by adding hosts to the distributed environment when the load increases.

Abstract: Methods and systems for allocating resources in a virtual desktop resource environment are provided. A method includes making a prediction on the future demand for processes running on a distributed environment with several hosts. The prediction is based on the process demand history and includes the removal of historic process demand glitches. Further, the prediction is used to perform a cost and benefit analysis for moving a candidate process from one host to another, and the candidate process is moved to a different host when the cost and benefit analysis recommends such move. In another embodiment, the predictions on future process demand are used for distributed power management by putting hosts in stand-by mode when the overall demand decreases or by adding hosts to the distributed environment when the load increases.

Abstract: A method of reducing power consumption of a server cluster of host systems with virtual machines executing on the host systems is disclosed. The method includes recommending host system power-on when there is a host system whose utilization is above a target utilization, and recommending host system power-off when there is a host system whose utilization is below the target utilization. Recommending host system power-on includes calculating impact of powering on a standby host system with respect to reducing the number of highly-utilized host systems in the server cluster. The impact of powering on is calculated by simulating moving some virtual machines from highly utilized host systems to the standby host system being recommended to be powered on. Recommending host system power-off includes calculating impact of powering off a host system with respect to decreasing the number of less-utilized host systems in the server cluster.