Hewlett-packard Development Company, L.P.

Vmware, Inc.

Google Inc.

Hewlett Packard Enterprise Development LP

Mustafa Uysal

Arif Abdulhusein Merchant

Anne Holler

Guillermo Alvarez

Ram Swaminathan

Irfan Ahmad

Pradeep Padala

John Wilkes

Kimberly Keeton

Aashish Parikh

Sachin Manpathak

Luis Useche

Marcos Kawazoe Aguilera

Ajay Gulati

Eric A Anderson

Puneet Zaroo

Ralph Attila Becker-Szendy

Mahesh Kallahalla

Joshua P Robinson

Rohit Bhoj

Parthasarathy Ranganathan

Sharad Singhal

Vanish Talwar

Xiaoyun Zhu

Zhikui Wang

Alistair Veitch

Sumanesh Samanta

Satish Kumar Mopur

Anna Povzner

Dinkar Sitaram

Xiaozhou Li

Parth Shah

Kishore Kumar Muppirala

Joseph Andrew Tucek

Ayman Abouelwafa

Susan Spence

John Johnson Wylie

Lluis Pamies-Juarez

Charles B Morrey, Iii

David Trastour

Ricardo Koller

Kenneth Salem

Junwen Lai

Frederic Gittler

Ifran Ahmad

Kiran-Kumar Muniswamy-Reddy

Yann Hodique

Michael Hobbs

Jai Jaswani

Tzu-Wei Yang

Samit Sawant

Francisco Maturana Sanguineti

A method for optimizing data storage includes obtaining a data object for storage at memory hardware in communication with data processing hardware. The memory hardware includes a plurality of storage devices, each storage device of the plurality of storage devices including storage parameters different from each other storage device of the plurality of storage devices. The method also includes determining one or more data object parameters associated with the data object and predicting, using a model and the data object parameters and the storage parameters, an object temperature representative of a frequency of access for the data object and an object lifetime representative of an amount of time the data object is to be stored. The method further includes selecting, using the predicted object temperature and object lifetime, one of the storage devices, and storing the data object at the selected one of the storage devices.

Optimizing data placement based on data temperature and lifetime prediction

The technology is directed to cache admission control. One or more processors may categorize access requests for data items in a cache storage into a plurality of categories and collect information on the access requests over time. Based on the collected information, a utility value for caching data items in each category of the plurality of categories may be determined. Newly requested data items may be admitted into the cache storage in an order according to the corresponding utility values of their respective categories.

Optimal admission control for caches

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

In an example, a method of placing a virtual computing instance among a plurality of datacenters includes comparing constraints specified for the virtual computing instance against resource metrics obtained from the plurality of datacenters, and a budget specified for the virtual computing instance against admission prices for a plurality of tiers of the plurality of datacenters, to generate a candidate set of datacenters. The method further includes scoring the candidate set of datacenters. The method further includes deploying the virtual computing instance to a target datacenter selected from the candidate set of datacenters that satisfies a threshold score.

Hybrid cloud resource scheduling

A universal resource management system and method for performing resource management operations for different computing environments uses a universal snapshot of the different computing environments to perform a resource management analysis to produce at least one recommended action for the different computing environments. The universal snapshot is created using state information collected from the different computing environments. The recommended action is then implemented in at least one of the different computing environments.

System and method for performing universal resource management operations for different virtualized computing environments

A system and method for performing customized remote resource allocation analyzes on distributed computer systems utilizes a snapshot of a distributed computer system, which is received at a remote resource allocation module, to perform a resource allocation analysis using a resource allocation algorithm. The resource allocation algorithm is selected from a plurality of resource allocation algorithms based on at least one user-provided parameter associated with the distributed computer system.

System and method for performing customized resource allocation analyses for distributed computer systems

Exemplary methods, apparatuses, and systems determine a miss-rate at various amounts of memory allocation for each of a plurality of workloads running within a computer. A value representing an estimated change in miss-rate for each of the workloads based upon an increase in a current allocation of memory to the workload is determined. The workload with a value representing a greatest improvement in hit rate is selected. Additional memory is allocated to the selected workload.

Data reuse tracking and memory allocation management

Exemplary methods, apparatuses, and systems receive a first request for a storage address at a first access time. Entries are added to first and second data structures. Each entry includes the storage address and the first access time. The first data structure is sorted in an order of storage addresses. The second data structure is sorted in an order of access times. A second request for the storage address is received at a second access time. The first access time is determined by looking up the entry in first data structure using the storage address received in the second request. The entry in the second data structure is looked up using the determined first access time. A number of entries in second data structure that were subsequent to the second entry is determined. A hit count for a reuse distance corresponding to the determined number of entries is incremented.

A system and method for performing a hypothetical power management analysis on a distributed computer system uses chronologically consecutive snapshots of the distributed computer system. The snapshots are used to extract demands of clients running in the distributed computer system for a resource for different time intervals, which are then stitched together to produce a workload trace. The snapshots and the workload trace are used to construct modeling scenarios for the distributed computer system. The modeling scenarios are used to perform analyzes to simulate the operation of the distributed computer system during which the power management module is enabled to compute potential power savings.

Power management analysis and modeling for distributed computer systems

Systems and techniques are described for allocating data store queues to virtual machines. A described technique includes allocating a respective queue to each of a plurality of threads, wherein the queue is configured to queue data requests from the respective thread and for a first data store, determining, for each of a plurality of threads, a respective maximum quantity of pending requests for the thread, wherein a quantity of pending requests sent from the respective queue to the first data store is equal to the maximum quantity of pending requests determined for the thread, determining, for each of the threads, a respective current quantity of operations per second, determining, for each of one or more first threads in the plurality of threads, a respective updated quantity of pending requests, and adjusting, for each first thread, the quantity of pending requests of the first thread sent to the first data store.

Virtual machine data store queue allocation

Embodiments associate software applications with computing resource containers based on placement rules. 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 and second placement rule indicates that the first software application is to be separated from the second software application when the second placement rule is violated by enforcing the first placement rule. 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 and the second placement rule indicates the first software application is to be co-located with the second software application when the second placement rule is violated by enforcing the first placement rule.

A system includes disk storage to provide differentiated storage QoS for a plurality of IO classes. Each IO class has a plurality of applications to it. A QoS controller collects IO statistics for each application and each class. The QoS controller adaptively determines an IO class assignment for each application based at least in part on the collected IO statistics.

Differentiated storage QoS

A network file system includes at least one unmodified client and at least one unmodified physical file server (PFS). Each PFS has at least one file that is identified by an associated physical file handle (PFH). The network file system includes virtual file servers (VFSs) in a data path between the client(s) and the PFS(s). The network file system also includes a virtualized name space that is mapped to the PFS(s) and is provided to the client(s) by the VFS(s). The network file system further includes a virtualized ID space that is established in response to a request from a client. Within the ID space, each file is identified by a physical file handle (PFH) on a PFS. The PFH and PFS pair are mapped to provide a virtual file handle (VFH), and the map of the VFHs is allocated among the VFSs.

Protocol virtualization for a network file system

A system and method for allocating resources on a shared storage system are disclosed. The systemcan include a shared storage deviceand a plurality of port schedulersassociated with a plurality of I/O portsthat are in communication with the shared storage device. Each port scheduleris configured to enforce a concurrency level and a proportional share of storage resources of the shared storage devicefor each applicationutilizing the associated port. The systemcan also include a resource controllerthat is configured to both monitor performance characteristics of the applicationsutilizing at least one of the I/O ports, and to adjust the concurrency level and the proportional share of storage resources parameters of the port schedulersfor at least a portion of the applicationsin order to vary allocation of the resources of the shared storage device

System and methods for allocating shared storage resources

Growing community of inventors

Fremont, CA, United States of America

Mustafa Uysal