Vmware, Inc.

Cloud Physics, Inc.

Irfan Ahmad

Ajay Gulati

Carl Alan Waldspurger

Ganesha Shanmuganathan

Anne Holler

Alexander T Garthwaite

Mustafa Uysal

Murali Vilayannur

Jyothir Ramanan

Jinyuan Li

Austin Clements

Kiran Tati

Orran Yaakov Krieger

Ali Mashtizadeh

Chirag Bhatt

Nohhyun Park

Umit Rencuzogullari

Eddie Ma

Mukil Kesavan

Xianan Zhang

Gabriel Tarasuk-Levin

Mallik Mahalingam

Maxime Austruy

Philip Peter Moltmann

Pin Lu

Chethan Kumar

Mukund Gunti

Davide Bergamasco

Vladimir L Kiriansky

Abhishek Chaturvedi

David Bergamasco

Vladimir Kiriansky

Decentralized deduplication operations in a computer system employ a hash index that is a variant of a B+ tree to support both efficient sequential updates as well as efficient random updates. Sequential update is selected when deduplication is infrequently performed, such as on the order of days, and random update is selected when deduplication is performed more frequently, such as on the order of seconds. More frequent deduplication may be beneficial during periods when large amounts of temporary duplicate data are created, and the system may not have enough storage space to accommodate the temporary spike in demand.

Computer storage deduplication

A resource management system for a virtual machine computing environment includes a software component that optimizes capacity between server clusters or groups by monitoring the capacity of server clusters or groups and automatically adding and removing host systems to and from server clusters or groups. The software component may be implemented at a server cluster management level to monitor and execute host system moves between server clusters and/or at a higher level in the resource management hierarchy. At the higher level, the software component is configured to monitor and execute host system moves between sets of server clusters being managed by different server cluster management agents.

System and method for automatically optimizing capacity between server clusters

A data center comprising plural computer hosts and a storage system external to said hosts is disclosed. The storage system includes storage blocks for storing tangibly encoded data blocks. Each of said hosts includes a deduplicating file system for identifying and merging identical data blocks stored in respective storage blocks into one of said storage blocks so that a first file exclusively accessed by a first host of said hosts and a second file accessed exclusively by a second host of said hosts concurrently refer to the same one of said storage blocks.

Decentralized deduplication operations in a virtualized computer environment having multiple storage devices employ a hash index for each storage device. When an image of a virtual machine is selected to be migrated from a source storage device to a destination storage device, hash values representing content of the storage blocks of the virtual machine are compared with hash entries of the destination hash index. Storage blocks of the virtual machine that have matching hash entries in the destination hash index are not transferred as part of the migration process so that the overall migration process can be accelerated.

A technique for managing distributed computing resources in a virtual computing environment is disclosed. In an embodiment, a method includes receiving a recommended change to a virtual architecture of a virtual computing environment; determining an impact on current workload in the virtual computing environment if the recommended change is performed; determining an impact on future workload in the virtual computing environment if the recommended change is performed; calculating a combined impact on current and future workload; determining if the combined impact is above or below a threshold; if the combined impact on current and future workload is below the threshold, do not perform the recommended change; and if the combined impact on current and future workload is above the threshold, perform the recommended change.

System and method for managing a virtualized computing environment

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

Embodiments associate software applications with computing resource containers based on a placement rule and a selected failure correlation. A placement rule indicates that a first software application is to be co-located with a second software application during execution of the first and second software applications. The placement rule also indicates that the first software application is to be separated from the second software application during execution of the first and second software applications. Failure correlations are determined for a plurality of computing resources associated with the first software application. A computing resource with a lowest failure correlation is selected from the plurality of computing resources, and the second software application is associated with the selected computing resource despite the association violating the placement rule.

Software application placement using computing resource containers

The configuration of a cache is adjusted within a computer system that includes at least one entity that submits a stream of references, each reference corresponding to a location identifier corresponding to data storage locations in a storage system. The reference stream is spatially sampled using reference hashing. Cache utility values are determined for each of a plurality of caching simulations and an optimal configuration is selected based on the results of the simulations.

Dynamic cache allocation adjustment using multiple cache simulations for efficient cache utility curve construction

Embodiments determine communication delay along a network path between a source and a destination in a network. A target injection rate is calculated based on the determined delay and a predetermined maximum queuing delay threshold. Data packets are transmitted along the network path using an inter-packet delay that is based on the target injection rate.

Network congestion management based on communication delay

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

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

A technique for predictive distributed resource scheduling and distributed power management includes analyzing patterns in the workload, predicting future workloads, and making recommendations for changes to the virtual computing environment. In addition, a cost-benefit analysis can be performed to determine whether the recommended change would likely result in improved performance.

Growing community of inventors

Mountain View, CA, United States of America

Irfan Ahmad