Vmware, Inc.

Emc Corporation

Junping Du

Ying Jian He

Jun Xiao

Da Wan

Michael Nelson

Yonghua Lin

Ziye Yang

Lei Chang

Tariq Magdon-Ismail

Yunshan Luke Lu

Jayanth Gummaraju

Richard McDougall

Wenbo Mao

Rean Griffith

Guang Lu

Razvan Cheveresan

Xiaoding Bian

Qiuhui Li

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.

Container virtual machines for hadoop

A method receives physical location information for racks in which application running environments are located. Each rack includes multiple host computing devices in a cluster of host computing devices. Application running environment-rack associations are generated using the physical location information for the cluster where an application running environment-rack association maps an application running environment to a rack. The application running environment-rack associations are provided to the cluster. Then, the method provides a data set for storing in the cluster of hosts where the data set is associated with a placement strategy. The cluster uses the placement strategy to store a data block in the data set for a first application running environment and store a replica data block for a second application running environment at a location in the cluster based on the first application running environment being associated with a first rack from the application running environment-rack associations.

Rack awareness data storage in a cluster of host computing devices

A virtualized computing system for executing a distributed computing application, such as Hadoop, is discussed. The virtualized computing system stores data in a distributed filesystem, such as Hadoop Distributed File System, and processes data using a topology awareness that takes into account the virtualization layer of the virtualized computing system. The virtualized computing system employs locality-related policies, including replica placement policies, replica choosing policies, balancer policies, and task scheduling policies that take advantage of the awareness of the virtualization topology.

Virtualization-aware data locality in distributed data processing

A rapid virtual machine (VM) cloning technique is provided that creates cloned VMs on hosts from multiple source VMs, rather than a single source VM that may otherwise be a bottleneck. The described technique selects particular hosts, disposed in particular racks, on which to create VM clones in a dynamic manner that reduces total deployment time for the plurality of VMs. A rapid VM reconfiguration technique is also provided that reduces the time spent reconfiguring the provisioned VMs for use in a distributed computing application.

Rapid creation and reconfiguration of virtual machines on hosts

Embodiments perform automated provisioning of a cluster for a distributed computing platform. Target host computing devices are selected from a plurality of host computing devices based on configuration information, such as a desired cluster size, a data set, code for processing the data set and, optionally, a placement strategy. One or more virtual machines (VMs) are instantiated on each target host computing device. Each VM is configured to access a virtual disk that is preconfigured with code for executing functionality of the distributed computing platform and serves as a node of the cluster. The data set is stored in a distributed file system accessible by at least a subset of the VMs. The code for processing the data set is provided to at least a subset of the VMs, and execution of the code is initiated to obtain processing results.

Provisioning a cluster of distributed computing platform based on placement strategy

Methods and apparatus are disclosed for managing access to data in a data storage system. For example, an apparatus comprises at least one processing platform associated with a distributed virtual infrastructure. The processing platform comprises at least one processing device having a processor coupled to a memory. The processing platform is operative to instantiate a meta data management process that is configured to provide at least one client process with information to allow the client process to perform one or more operations in accordance with one or more data storage devices through a storage area network. The information provided to the client process may comprise one or more data block descriptors. Each of the one or more data block descriptors may comprise path information for at least one of the data storage devices and an offset address in the at least one data storage device. The one or more data block descriptors may correspond to one or more data blocks of a given data file that is stored or storable across the one or more data storage devices.

File system for storage area network

Growing community of inventors

Beijing, China

Junping Du