International Business Machines Corporation

Amir Epstein

David Breitgand

Danny Harnik

Michael Edward Factor

Elliot Karl Kolodner

Dmitry Sotnikov

Ronen Itshak Kat

Alexander Glikson

Norbert George Vogl

Vijay Krishnarao Naik

Yih-Shin Tan

Kirk Alan Beaty

Moshe Weiss

Ety Khaitzin

Kenneth Nagin

Kyung D Ryu

Yosef Moatti

Michael R Hines

Dilma Menezes Da Silva

Vladimir Shalikashvili

Yoni Raveh

Marcio Augusto Silva

Oshrit Feder

Inbar Shapira

Giovanni Toffetti-Carughi

Zvi Dubitzky

Pavel Kravchenco

Daniel Marom

A computer-implemented method according to one embodiment includes identifying a plurality of storage systems within a storage environment, determining characteristics of each of the plurality of storage systems, the characteristics including one or more data reduction techniques implemented by each of the plurality of storage systems, performing a plurality of storage simulations of one or more data volumes, utilizing the characteristics of each of the plurality of storage systems, and determining one of the plurality of storage systems to store the one or more data volumes, based on results of the plurality of storage simulations.

Distributing data across a mixed data storage center

A method for a data placement that attempts to predict the most suitable placement, in terms of data reduction, of a newly created storage volume based on the volumes known attributes and the current placement of volumes to deduplication domains is disclosed. The system uses machine learning to perform improved deduplication-aware placement. The system attempts to predict the deduplication domain where a newly created volume would eventually have the best content sharing. The system does this by using the known attributes of the volume at the time of creation, such as owner, volume name, initial size, creation time, and the history of data already in the system and its placement.

Cognitive deduplication-aware data placement in large scale storage systems

A method for storage systems improvement includes collecting information that indicates one or more failure correlations for disks in a storage system. The disks are then separated into a plurality of virtual failure domains based on the indicated one or more failure correlations. The method then determines that all data objects of a set of redundant data objects are included in a first virtual failure domain. Responsive to determining that all data objects of the set of redundant data objects are included in the first virtual failure domain, the method then migrates at least one data object of the set of redundant data objects from a first disk in the first virtual failure domain to a second disk in a second virtual failure domain.

Virtual failure domains for storage systems

Machines, systems and methods for managing quality of service in a virtualized computing environment, the method comprising: provisioning one or more active virtual machines (VMs) over one or more hosts in a virtualized computing network, wherein one or more resources are allocated to the active VMs before the active VMs service one or more requests; monitoring information associated with quality of service defined for servicing of the requests; and designating at least an active VM as a shadow VMs, in response to results of the monitoring, wherein at least one resource remains allocated to the shadow VM, while the shadow VM enters a dormant state and no longer services any requests.

Efficient provisioning and deployment of virtual machines

A method for providing dynamic resizing of at least one virtual machine in a cloud infrastructure includes steps of: obtaining a measure of resource usage for the virtual machine and/or application performance of an application running on the virtual machine; determining, as a function of prescribed rules relating to resource usage and the measure of resource usage and/or application performance, whether the virtual machine requires resizing; and resizing the virtual machine to dynamically change resource availability for the virtual machine so as to maintain resource usage and/or application performance within prescribed limits defined by the prescribed rules.

Dynamic virtual machine resizing in a cloud computing infrastructure

Embodiments include evaluating durability and availability of a distributed storage system. Aspects include receiving a configuration of the distributed storage system, identifying a failure model for each component of the distributed storage system. Aspects also include generating a series of failure events for each component of the distributed storage system based on the failure model and calculating a recovery time for each failed component based on a network recovery bandwidth, a disk recovery bandwidth, a total capacity of simultaneous failed storage devices and a resiliency scheme used by the in the distributed storage system. Aspects further include collecting data regarding the series of failures and the recovery times, calculating an observed distribution of component failures from the collected data and calculating the availability and durability of the distributed storage system based on the observed distribution of component failures and using probabilistic durability and availability models.

Durability and availability evaluation for distributed storage systems

Machines, systems and methods for allocating resources to in a virtualized computing environment, the method comprising detecting one or more host machines with resources allocated to one or more virtual machines (VMs) that are in an idle state; reducing resource entitlements for at least one of the VMs that is detected to be in the idle state to make more resources available for allocation to VMs that are not in the idle state; and increasing resource entitlements for at least one of the VMs with reduced entitlement, in response to determining that the VM with reduced entitlement is no longer in the idle state.

Optimized resource allocation and management in a virtualized computing environment

Systems and methods for managing capacity over-commitment in a resource provisioning environment. The method comprising collecting historical data about capacity usage in a resource provisioning environment and failure statistics from an operational database, in addition to prior service commitments due to previously contracted service level agreements (SLAs) stored in a SLA repository; calculating equivalent capacity for the resource provisioning environment based on a defined residual benefit goal, and other business goals instrumented by way of a policy engine; and determining whether placing requested virtual resources on one or more hosts is feasible taking into account anti-collocation constraints that prohibit placement of two or more virtual resources on same host.

Capacity over-commit management in resource provisioning environments

A system and method for correlating events in data processing resources processing a workload. An event correlation service comprising an event correlation service software framework is provided on a processor unit. The event correlation service is configured, using the event correlation service software framework, for correlating the events from monitored data for the workload, wherein the workload is processed using the data processing resources provided by a provider of the data processing resources. The event correlation service receives monitored data for the workload as the workload is being processed. The monitored data is processed by the event correlation service to identify an occurrence of an event of interest for the workload as the workload is being processed. An output is provided in response to identifying the occurrence of the event of interest.

Data processing environment event correlation

Machines, systems and methods for deciding whether a plurality of virtual machines (VMs) may be hosted on a computing system with resources to support at least one of the VMs, the method comprising determining maximum permissible probability of infringement (maxPermInfrProb(v)) for a VM(v), wherein 'v' is a value from 1 to n, such that n is the number of VMs in a set G, as stipulated by the VMs' SLA, wherein 'v' is a value from 1 to n, such that n is the number of VMs in a set G; partitioning the set G into a plurality of groups G(G, . . . G, . . . G), so that each group is homogenous with respect to maxPermInfrProb defined for members of the group G; and organizing the groups Gbased on the value of maxPermInfrProb for each G.

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Herzelia, Israel

Amir Epstein