Vmware, Inc.

Hewlett-packard Development Company, L.p.

Vmare, Inc.

Ajay Gulati

Irfan Ahmad

Ganesha Shanmuganathan

Carl Alan Waldspurger

Pradeep Padala

Parth Shah

Jyothir Ramanan

Peter Varman

Mustafa Uysal

Anil Rao

Abhishek Chaturvedi

Krishna Raj Raja

Chethan Kumar

Arif Abdulhusein Merchant

Anne Holler

Aastha Bhardwaj

Mukil Kesavan

Sachin Manpathak

Luis Useche

Gregory T Harm

Jean-Pascal Billaud

Gregory T Harmon

Peter Joseph Varman

A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Automatic placement of clients in a distributed computer system satisfying constraints

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.

Distributed demand-based storage quality of service management using resource pooling

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.

Application service level objective aware demand estimation

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.

Saturation detection and admission control for storage devices

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.

Decentralized management of virtualized hosts

Systems and methods are disclosed that allow for transparently recovering from an uncorrected multi-bit error of arbitrary length located at a memory address. Storing one or more parity pages, for a set of pages in system memory, such that a page in the set of pages may be reconstructed using one of the parity pages is disclosed. Storing an indication of one or more page'disk location such that the one or more pages may be reconstructed by refilling the page from disk is also disclosed.

Transparent and lightweight recovery from hardware memory errors

A shared input/output (IO) resource is managed in a decentralized manner. Each of multiple hosts having IO access to the shared resource, computes an average latency value that is normalized with respect to average IO request sizes, and stores the computed normalized latency value for later use. The normalized latency values thus computed and stored may be used for a variety of different applications, including enforcing a quality of service (QoS) policy that is applied to the hosts, detecting a condition known as an anomaly where a host that is not bound by a QoS policy accesses the shared resource at a rate that impacts the level of service received by the plurality of hosts that are bound by the QoS policy, and migrating workloads between storage arrays to achieve load balancing across the storage arrays.

Decentralized input/output resource management

In an example, a method for performing initial placement of a data object in a distributed system that includes a plurality of hardware resources includes receiving a request to create an instance of a data object; determining, in response to the request, a list of hardware resources that satisfy one or more criteria of the data object; creating, in response to the request, a virtual cluster that includes a subset of the hardware resources included in the list of hardware resources; selecting a hardware resource from the virtual cluster into which the data object is to be placed; placing the data object into the hardware resource; and releasing the virtual cluster.

Managing resources in a distributed system using dynamic clusters

A system and method for performing automatic remediation in a distributed computer system with multiple clusters of host computers uses the same placement selection algorithm for initial placements and for remediation placements of clients. The placement selection algorithm is executed to generate a placement solution when a remediation request in response to a remediation-requiring condition in the distributed computer system for at least one client running in one of the multiple clusters of host computers is detected and a remediation placement problem for the client is constructed. The placement solution is then implemented for the client for remediation.

Automatic remediation in a distributed computer system with multiple clusters of host computers

Embodiments perform centralized input/output (I/O) path selection for hosts accessing storage devices in distributed resource sharing environments. The path selection accommodates loads along the paths through the fabric and at the storage devices. Topology changes may also be identified and automatically initiated. Some embodiments contemplate the hosts executing a plurality of virtual machines (VMs) accessing logical unit numbers (LUNs) in a storage area network (SAN).

Fabric distributed resource scheduling

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.

Growing community of inventors

Palo Alto, CA, United States of America

Ajay Gulati