International Business Machines Corporation

Globalfoundries Inc.

Sandeep Gopisetty

Aameek Singh

Ramani Ranjan Routray

Kavita Chavda

Kaladhar Voruganti

Seshashayee Sankarshana Murthy

Kang-Won Lee

Sandip Agarwala

Chung-hao Tan

David P Goodman

Dakshi Agrawal

Sandeep Madhav Uttamchandani

Dinesh C Verma

Eric Kevin Butler

Madhukar R Korupolu

Richard Ayala

Cheryl Ann Kieliszewski

David D Weck

Gauri Sudhir Shah

Gregory E McBride

Murthy V Devarakonda

David Michael Shackelford

Robert Beverley Basham

Prasenjit Sarkar

Norbert George Vogl

Eser Kandogan

Hovey R Strong, Jr

Brent William Yardley

Khalid Filali-Adib

David Alson George

Raphael I Arar

Cuong Minh Le

Andreas Dieberger

Konstantinos Magoutis

James Mitchell Ratliff

Michael D Cardosa

Yang Song

Seraphin Bernard Calo

David Lynn Merbach

James E Olson

Gabriel Alatorre

Sumant Padbidri

Shripad Jayant Nadgowda

Stefan Jaquet

Roberto C Pineiro

Larry Steven McGimsey

Richard J Ayala, Jr

Daniel Gruhl

Divyesh Jadav

Pawan Raghunath Chowdhary

Nagapramod S Mandagere

Soumitra Sarkar

Heiko Hary Ludwig

Chatschik Bisdikian

Steve Richard White

Alfredo Alba

Nathalie Baracaldo Angel

David M Ungar

Christian Benjamin Kau

Shripad Nadgowda

Linda Ha Kato

Steven R Welch

Rakesh Jain

Chad Eric DeLuca

Glen Hattrup

Anna Lisa Gentile

Shubhi Asthana

Eben Merriam Haber

Norman Jerome Pass

Kevin P Roche

Pallavi Vyankatesh Galgali

Dushmanta Mohapatra

Ashmi Ashokkumar Chokshi

Manuel Hernandez

Joseph D Ryan

Amit Sunil Modi

Sudhir Koka

A method, computer system, and a computer program for quick disaster recovery of cloud-native environments is provided. The present invention may include replicating at a secondary server site software executing in a cloud-native environment on a primary server site. The present invention may also include detecting a failure associated with the software executing in the cloud-native environment. The present invention may then include whether the detected failure is causing down time for the software executing in the cloud environment. The present invention may further include deploying the replicated software on the secondary server site in response to determining that the detected failure is causing down time.

Quick disaster recovery in distributed computing environment

Embodiments relate to a computer system, computer program product, and method to prevent unauthorized file dissemination and replication. A file parameter is defined, with the defined file parameter including a file dissemination characteristic. The file is encoded with the defined file parameter as file metadata. Dissemination and replication of the file is managed responsive to the encoded file parameter. The defined parameter is assessed along with a physical replication destination. The file is selectively replicated or transmitted responsive to the file parameter and the destination assessment.

Geography aware file dissemination

A computer-implemented method, according to one embodiment, includes: setting a target power demand corresponding to a consumer, and performing a process. The process includes: determining an actual power demand presented to a utility by the consumer, and determining a current error. The current error is the difference between the actual power demand and the target power demand. A determination is also made as to whether the actual power demand is adjustable in a direction that reduces the current error. In response to determining that the actual power demand is adjustable in the direction that reduces the current error, the current error is reduced by adjusting the actual power demand. Moreover, in response to determining that the actual power demand is not adjustable in the direction that reduces the current error, the target power demand is modified.

Computer-implemented method, system, and computer program product for setting a target power demand corresponding to a consumer

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

A computer-implemented method, according to one embodiment, includes: receiving an energy consumption profile which spans multiple intervals in a period of time, and predicting a net energy demand of a consumer system over the period of time. Moreover, a first multiple is determined which, when applied to the received energy consumption profile, produces an updated energy consumption profile which corresponds to an amount of energy that is capable of satisfying the predicted net energy demand of the consumer system. A greatest amount of underprediction is estimated. A greatest amount of overprediction is also estimated. Furthermore, an initial state of an energy storage device electrically coupled to the consumer system is computed according to the updated energy consumption profile. The initial state of the energy storage device is also based on a second multiple applied to each of the greatest amount of underprediction, and the greatest amount of overprediction.

Managing consumer energy demand

One embodiment provides a method comprising receiving a request to release a utility holder detachably mounted to a surface mount. The utility holder maintains a utility payload. A set of rotatable holding brackets is coupled to the surface mount in a pivotable fashion to engage the holding brackets with the utility holder in order to mount the utility holder to the surface mount. The method further comprises, in response to receiving the request, powering on a drone attached to the utility holder, and releasing the utility holder from the surface mount by actuating rotation of the holding brackets to disengage the holding brackets from the utility holder, enabling the drone to transport the utility holder from the surface mount to a desired location.

Drone-based smoke detector

A computer-implemented method, according to one embodiment, includes: setting a target power demand corresponding to a consumer, and performing a process. The process includes: determining an actual power demand presented to the utility by the consumer based on a reward table, determining a current error, determining whether the actual power demand is adjustable in a direction that reduces the current error, reducing the current error by adjusting the actual power demand in response to determining that the actual power demand is adjustable in the direction that reduces the current error, and modifying the target power demand in response to determining that the actual power demand is not adjustable in the direction that reduces the current error. Determining the actual power demand includes measuring the power demand over a period of time in accordance with a process for measuring stability of power demand received from the utility.

Stabilizing consumer energy demand

Embodiments relate to cross-domain service request placement in a software defined environment (SDE). An aspect includes receiving a service request corresponding to a job to be completed in the SDE. Another aspect includes determining a first computer device in a first domain, and a second computer device in a second domain, that are capable of performing the service request. Another aspect includes determining, for the first and second computer devices, first and second pluralities of available service classes. Another aspect includes determining, for the first and second computer devices, a first and second plurality of costs of performing the service request, wherein each of the first and second plurality of costs corresponds to a single respective service class. Yet another aspect includes selecting one of the first computer device and the second computer device to perform the service request based on the first and second plurality of costs.

Cross-domain service request placement in a software defined environment (SDE)

Protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

Remote data protection in a networked storage computing environment

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

A method and system for integrated server-storage deployment planning for virtual appliances is provided. One implementation involves determining a performance cost in deploying the virtual appliance to different pairings of candidate host and storage subsystems. A host and storage subsystem pair is preferentially selected among the candidate pairings, a pairing of a host and storage subsystem with certain performance cost to satisfy performance requirements of the virtual appliance. Deployment planning may further involve deploying a virtual appliance on one or more appropriate spatially proximate hosts and storage subsystems that exhibit certain preferable connectivity and path capacity to satisfy the performance requirements of the virtual appliance.

Method and system for integrated deployment planning for virtual appliances

Embodiments of the present invention provide approaches (e.g., online methods) to analyze end-to-end performance issues in a multi-tier enterprise storage system (ESS), such as a storage cloud, where data may be distributed across multiple storage components. Specifically, performance and configuration data from different storage components (e.g., nodes) is collected and analyzed to identify nodes that are becoming (or may become) performance bottlenecks. In a typical embodiment, a set of components distributed among a set of tiers of an ESS is identified. For each component, a total capacity and a current load are determined. Based on these values, a utilization of each component is determined. Comparison of the utilization with a predetermined threshold and/or analysis of historical data allows one or more components causing a bottleneck to be identified.

Comprehensive bottleneck detection in a multi-tier enterprise storage system

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

Growing community of inventors

Morgan Hill, CA, United States of America

Sandeep Gopisetty