International Business Machines Corporation

Globalfoundries Inc.

Aameek Singh

Sandeep Gopisetty

Gabriel Alatorre

Kavita Chavda

Eric Kevin Butler

Seshashayee Sankarshana Murthy

James E Olson

Sandeep Madhav Uttamchandani

Nagapramod S Mandagere

Divyesh Jadav

Madhukar R Korupolu

Thomas D Griffin

Laura A Richardson

Dean Hildebrand

Renu Tewari

Ann Corrao

David P Goodman

Yang Song

Kaladhar Voruganti

Arup Acharya

Richard Ayala

Michael D Cardosa

Ramani Ranjan Routray

Prasenjit Sarkar

Mihail Corneliu Constantinescu

Reshu Jain

Patrick B Heywood

David D Weck

Larry Steven McGimsey

Zon-Yin Shae

Dilip Dinkar Kandlur

Prashant Pandey

Yin Wang

Christian Bolik

Dietmar Noll

Mark V Chitti

Gauri Sudhir Shah

Norman Jerome Pass

Mirza S Baig

Bhuvan Bamba

Paul Lawrence Bradshaw

Bryan S Langston

Richard J Ayala, Jr

Dushmanta Mohapatra

Priya Mahadevan

Barry B Becker, Jr

Barry Becker

Balaji Palanisamy

Sudhir V R Koka

Steven P Gronefeld

William Anthony Sherrill

A virtual machine is assigned to a target physical server based on virtualization parameters for maximizing utility of a multiple virtual machines and physical servers. Resource allocation is performed for and deployment of the virtual machine to the target physical server based on capabilities of the target physical server and multiple virtual machine resource requirements. The virtualization parameters include a minimum parameter, a maximum parameter and a shares parameter. Processing resources are allocated based on utility priority of applications operating on the virtual machine using the shares parameter of contending virtual machines to determine a processing cycle ratio for distributing processing cycles between different utility priority applications operating on the contending virtual machines.

Server consolidation using virtual machine resource tradeoffs

One embodiment provides a method, including: generating, for each of a plurality of storage volumes, an actual used storage capacity model and identifying a potential storage capacity savings using the actual used capacity model, wherein each of the plurality of storage volumes has been identified as a candidate for migration to a thin provisioned volume; generating, for each of the plurality of storage volumes, an input/output profile model and identifying a potential change in performance of an application accessing the storage volume using the input/output profile model; generating, for each of the plurality of storage volumes, a growth profile and identifying a potential change in capacity using the growth profile; and determining, using an optimization algorithm, a subset of the plurality of storage volumes to be migrated to thin provisioned volumes based upon the volume capacity model, the performance model, and the volume growth profile.

Identification of volumes for thin provisioning

Embodiments of the invention relate to dynamic application migration in a shared pool of configurable computer resources with disaster recovery support. Write operations are executed by an application at a first data site. Data is generated and stored in first data storage local to the first data site. The stored data is replicated from the first data storage to a backup data storage, and a consistency point of the data is created in both the first data storage and the backup data storage. In response to a local hardware failure at the first data site, an unplanned migration protocol is initialized to migrate the application to a second data site remote from the first data site, which includes re-launching the application at the second data site. Data is transferred from the backup data storage to the second data storage local to the second data site from the consistency point.

Consistent data retrieval in a multi-site computing infrastructure

The invention provides a method and system for continuous optimization of a data center. The method includes monitoring loads of storage modules, server modules and switch modules in the data center, detecting an overload condition upon a load exceeding a load threshold, combining server and storage virtualization to address storage overloads by planning allocation migration between the storage modules, to address server overloads by planning allocation migration between the server modules, to address switch overloads by planning allocation migration mix between server modules and storage modules for overload reduction, and orchestrating the planned allocation migration to reduce the overload condition in the data center.

Method and system for continuous optimization of data centers by combining server and storage virtualization

One embodiment provides a method of determining a revised firmware upgrade plan for one or more devices, said method including: utilizing at least one processor to execute computer code that performs the steps of: selecting, in view of a predetermined change window and risk associated with the firmware upgrade plan, a firmware upgrade plan; detecting a failure during execution of the firmware upgrade plan; and responsive to the detecting, identifying a revised firmware upgrade plan based on time remaining in the predetermined change window and risk associated with the revised firmware upgrade plan. Other aspects are described and claimed.

Managing firmware upgrade failures

Data files can be backed up by copying, in response to a backup request, files from a client device to a backup archive and recording the status of the files. Transformed copies of files in the backup archive can be created by automatically applying a transform to the files received from the client device. Upon receiving a subsequent backup request, differences can be identified between transformed files in the backup archive and files on the client device, and in response to identified differences in the files, the transformed files can be copied back to the client device.

Backup service with managed file transformation

An approach for reactive throttling of heterogeneous migration sessions running concurrently in a virtualized cloud environment is disclosed. The heterogeneous migration sessions are characterized by several attributes such as the type of function performed by the migration session and the value that is attained by performing the migration session. An input/output (I/O) path to any resource in the virtualized cloud environment computing that is experiencing excessive resource congestion is identified. Bottlenecks along the I/O path are then determined. Virtual-to-physical resource mapping information of resources in the virtualized cloud computing environment are used to obtain a list of the heterogeneous migration sessions contributing to the bottlenecks along the identified I/O path.

Reactive throttling of heterogeneous migration sessions in a virtualized cloud environment

The present invention proactively identifies hotspots in a cloud computing environment through cloud resource usage models that use workload parameters as inputs. In some embodiments the cloud resource usage models are based upon performance data from cloud resources and time series based workload trend models. Hotspots may occur and can be detected at any layer of the cloud computing environment, including the server, storage, and network level. In a typical embodiment, parameters for a workload are identified in the cloud computing environment and inputted into a cloud resource usage model. The model is run with the inputted workload parameters to identify potential hotspots, and resources are then provisioned for the workload so as to avoid these hotspots.

Proactive identification of hotspots in a cloud computing environment

Methods, systems, and articles of manufacture for identifying compatible system configurations are provided herein. A method includes generating a second graph from a first graph of multiple devices in a network and a set of one or more network compatibility rules, wherein said generating comprises dividing each device in the first graph into multiple nodes in the second graph, and wherein each node in the second graph represents a valid configuration of a device in the first graph; identifying a sub-graph of two or more linked nodes in the second graph that is isomorphic to at least a portion of the first graph, wherein the two or more linked nodes in the second graph represent two or more configurations that are compatible based on the set of one or more network compatibility rules; and determining each of one or more changes needed to convert a current configuration in the network to a target configuration specified by the sub-graph.

Identifying compatible system configurations

Embodiments of the present invention provide an approach for adapting an information extraction middleware for a clustered computing environment (e.g., a cloud environment) by creating and managing a set of statistical models generated from performance statistics of operating devices within the clustered computing environment. This approach takes into account the required accuracy in modeling, including computation cost of modeling, to pick the best modeling solution at a given point in time. When higher accuracy is desired (e.g., nearing workload saturation), the approach adapts to use an appropriate modeling algorithm. Adapting statistical models to the data characteristics ensures optimal accuracy with minimal computation time and resources for modeling. This approach provides intelligent selective refinement of models using accuracy-based and operating probability-based triggers to optimize the clustered computing environment, i.e., maximize accuracy and minimize computation time.

Optimizing storage cloud environments through adaptive statistical modeling

A method and system for intelligent tiering is provided. The method includes receiving a request for enabling a tiering process with respect to data. The computer processor retrieves a migration list indicating migration engines associated with the data. Additionally, an entity list of migration entities is retrieved and each migration entity is compared to associated policy conditions. In response, it is determined if matches exist between the migration entities and the associated policy conditions and a consolidated entity list is generated.

Resource allocation for migration within a multi-tiered system

Embodiments of the present invention provide an approach for migrating virtual machines across network (e.g., WAN) separated data centers (e.g., storage clouds). Specifically, under embodiments of the present invention, a first storage system associated with a first data center is synchronized with a second storage system associated with a second data center via a storage system link. Then, a minimal state of a virtual machine is migrated from a first computer in the first data center to a second computer in the second data center via a WAN link. Using the minimal state, the virtual machine is stored in the second computer. Thereafter, the storage system link is terminated. In addition, as updated pages are received in memory of the first computer, they are migrated to the second computer via the WAN link. Once this migration is complete, the WAN link can be terminated. Therefore, embodiments of the present invention provide at least two forms of synchronization: computational synchronization and storage synchronization.

Migrating virtual machines across network separated data centers

A method and system. In response to a request for a proposed additional data transfer from a source storage system to a target storage system, an estimate is made of utilization of one or more data transfer components of the source storage system and the target storage system that would occur during the proposed additional data transfer combined with other scheduled data transfers involving the source storage system and the target storage system, based in part on (i) an I/O request rate for recent actual data transfers involving the source storage system and the target storage system combined with (ii) an estimated I/O request rate for the proposed additional data transfer. It is ascertained whether the estimated utilization exceeds a corresponding utilization threshold. If so, execution of the proposed additional data transfer is postponed. If not, execution of the proposed additional data transfer is initiated.

Scheduling transfer of data

An incompatible software level of an information technology infrastructure component is determined by comparing collected inventory information to a minimum recommended software level. If a knowledge base search finds that the incompatible software level is associated with a prior infrastructure outage event, an outage count score is determined for the incompatible software level by applying an outage rule to a historic count of outages caused by a similar incompatible software level, and combined with an average outage severity score assigned to the incompatible software level based on a level of severity of an actual historic failure of the component within a context of the infrastructure to generate a normalized historical affinity risk score. The normalized historical affinity risk score is provided for prioritizing the correction of the incompatible software level in the context of other normalized historical risk level scores of other determined incompatible software levels.

Proactive risk analysis and governance of upgrade process

Growing community of inventors

University Place, WA, United States of America

Aameek Singh