International Business Machines Corporation

Kyndryl, Inc.

Anthony M Hunt

Clea Anne Zolotow

John Valentine Delaney

Maeve M O'Reilly

Sean Thomas Fornash

Daniel P Toulan

Florian Graf

Laxmikantha Sai Nanduru

Andrew Paul Barnes

Claus Schroeder-Hansen

Claus Schrøder-Hansen

Maeve O'Reilly

Niamh Phelan

David O'Grady

Richard M Cocchiara

Russell G Conner

Philippe H V M Spaas

Johan Van Kessel

Tedrick Neal Northway

Dennis Anthony Perpetua, Jr

Gerhard A Widmayer

Simon J Brady

Gavin Charles O'Reilly

Rudolph C Baron

Srinivasa M Raghavan

Arvind Viswanathan

Zheng Xie

James S Bennett

Ricky L Montagna

Dennis M Carden

Pawel J Wieczorek

Vladimir V Danilevich

Sean T Fomash

Computer implemented systems and methods for migrating datacenter data include providing a quantity of carriers having a data storage capacity, receiving, by the quantity of carriers, a quantity of data stored in a first data storage system having a first location and including erasure coded data blocks. The quantity of carriers migrates to a second data storage system having a second location; and transmits the quantity of data to the second data storage system.

Datacenter relocation utilizing storage carriers and erasure coding

Data migrations are not able to be identified or traced through a switched network to their originating hub, because the corresponding TCP/IP data stream goes through a plurality of optimized differential switches and the originating IP in the packet header gets replaced at each switch. The present invention provides a mechanism to introduce a lag or a jitter into the IP to label the migrated data. The labeled data are able to be traced and identified through multiple managed hubs and/or switches.

Managing data traffic according to data stream analysis

A set of internal information technology (IT) architecture is received and the internal IT architecture is controlled by a service provider. A set of exogenous IT architecture is received and the exogenous IT architecture is not controlled by the service provider. A set of service level agreement (SLA) requirements, agreed upon by the service provider and a service user, are received. A set of sensor data, from sensors monitoring environmental conditions which may affect at least one of the internal IT architecture or the exogenous IT architecture, is received. A set of heuristic data relating to the internal IT architecture and the exogenous IT architecture is received. A risk metric based on the set of internal IT architecture, the set of exogenous IT architecture, the set of SLA requirements, the set of sensor data, and the set of heuristic data is determined.

Service level agreement risk analysis with exogenous architecture

Methods and systems for datacenter migrations are disclosed. A method includes: virtualizing, by a computing device, servers in a source environment; installing, by the computing device, an isolation firewall to isolate a target environment from the source environment; installing, by the computing device, shared services in the target environment; installing, by the computing device, monitoring and management tools in the virtualized servers in the source environment; replicating, by the computing device, between the source environment and the target environment; and cutting over, by the computing device, from the source environment to the target environment by switching a route advertisement from the source datacenter to the target datacenter.

Big bang approach in datacenter migrations

A computer-implemented method includes accessing a user database, accessing a transport database, and generating a global recommended travel plan. The user database includes travel preferences and a metric preference for each user. The travel preferences include an origin, and a destination. The transport database includes transport data. The global recommended travel plan includes, for each user, a recommended route from the user's origin to the user's destination, a recommended mode, and a recommended start time. Generating the global recommended travel plan includes simultaneously optimizing each user's metric preference based on the transport data and the travel preferences. The computer-implemented method further includes returning each user's recommended route, recommended mode, and recommended start time. A corresponding computer program product and computer system are also disclosed.

Journey planning

A computer-implemented method includes: monitoring, by a computing device, computing resource utilization by a plurality of virtual computing components sharing a same kernel; monitoring, by the computing device, transaction counts produced by the plurality of virtual computing components; determining, by the computing device, that the transaction counts increase by less than a threshold number as the computing resource utilization increases; and creating, by the computing device, an additional virtual computing component on a separate kernel based on determining that the transaction counts increase by less than a threshold number as the computing resource utilization increases.

Detecting and counteracting a multiprocessor effect in a virtual computing environment

A computer-implemented method includes: receiving, by a computing device, information identifying a user's activity; determining, by the computing device, the user's tasks based on the information identifying the user's activity; determining, by the computing device, the user's context switches based on the user's tasks; receiving, by the computing device, biometrics data associated with the user via an application programming interface (API); determining, by the computing device, the user's stress levels at various times based on the biometrics data; storing, by the computing device, information linking the user's stress level with the user's context switches; and outputting, by the computing device, the information linking the user's stress level with the user's context switches.

Identifying stress levels associated with context switches

A computer-implemented method includes: monitoring, by a computing device, resource usage data for a hypervisor; monitoring, by the computing device, resource usage data for a guest machine; identifying, by the computing device, outliers in the resource usage data for the hypervisor and outliers in the resource usage data for the guest machine; executing, by the computing device, a root cause model based on the resource usage data for the hypervisor and the resource usage data for the guest machine; generating, by the computing device, a root cause report identifying results of the executed root cause model; and providing, by the computing device, the root cause report for display.

Outlier and root cause determination of excessive resource usage in a virtual machine environment

Methods, computer program products, and systems are presented. The methods include, for instance: input data from the source environment, including application hosting data of each server in the source environment and one or more cloud type of the source environment. Candidate cloud types for target platform are listed and servers of the source environment are screened for eligibility for the migration. The target platform is selected by applying preconfigured selection rules on the application hosting data of each eligible server in the source environment. Migration recommendations for each eligible server in the source environment, including selected cloud type corresponding to the target platform, are produced.

Cognitive cloud migration optimizer

A set of workloads to be migrated is identified. A constraint on optimization of the set of workloads is determined. A subset of workloads to be migrated is selected from the set of workloads. A model is constructed, using traffic information corresponding to the set of workloads. The model includes a representation of a relationship between a first workload and a second workload in the subset of workloads. The model is solved to cause generation of a set of optimal flow values. A schedule for a migration wave is constructed. The schedule complies with the constraint on optimization of the set of workloads.

Cloud and datacenter migration wave planning using analytics and flow modeling

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining radio signal encoded data from one or more radio signal emitting device disposed on one or more fabric item used for thermal insulation by a user; performing data processing using an obtained environmental temperature of the user and one or more thermal characteristic, wherein the one or more thermal characteristic is determined using the radio signal encoded data; and providing one or more output based on the data processing.

Utilizing radio signal emitting devices on fabric items to control temperature

A method for off-board data migration. Responsive to receiving a request to migrate a virtual machine image, a memory location of the source virtual machine is identified. Using a generalized pathing interface, a range of logical blocks is accessed for both the source and destination virtual machines. The memory location of the source virtual machine is copied to a memory location of the destination virtual machine. The destination virtual machine is started, and; and the source virtual machine is stopped.

Hardware independent interface for cognitive data migration

A computer-implemented method includes identifying a storage migration. The storage migration is associated with a storage area network. The storage migration has a storage migration rate associated therewith. The method includes identifying an input/output throughput. The input/output throughput is associated with the storage area network. The input/output throughput stores a throughput rate for the storage area network. The method includes identifying a service level agreement rate for the input/output throughput. The method includes identifying a non-essential workload. The non-essential workload stores a non-essential workload rate associated therewith. The non-essential workload includes that portion of said input/output throughput that is for one or more background processes. The method includes determining an analyzed rate based on the throughput rate, the service level agreement rate, and the non-essential workload rate. The method includes updating the storage migration rate based on the analyzed rate.

Updating storage migration rates

One or more processors determine a throughput of hardware devices operatively coupled to a target Storage Area Network (SAN) prior to a SAN migration from a source SAN to the target SAN. Buffers within a processor core in the source SAN contain intermediate execution outputs of components within the processor core. The processor(s) determine an estimated time duration required to retrieve the soft state from a central processing unit (CPU) queue in the source SAN processor and to migrate the soft state of the processor core from the source SAN to the target SAN, in order to determine an estimated size and time of the SAN migration from the source SAN to the target SAN. The processor(s) then configure transmission resources available to the source SAN to comport with the estimated size and time of the SAN migration from the source SAN to the target SAN.

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Hopewell Junction, NY, United States of America

Anthony M Hunt