Abstract: Operational characteristic I/O patterns of each storage volume of a storage volume group, and at least one operational characteristic of each storage volume associated with an application, are determined. Unsupervised learning is used to form clusters of storage volumes of similar characteristics. Labels are generated, assigned, and aggregated for each characteristic of each storage volume. Templates are received that includes labels of storage volume characteristics of known application types. An application type associated with the storage volume group is inferred, based on a best-fit match of the aggregate labels of the storage volumes of the storage volume group to the labels included in the templates of storage volume characteristics of known application types.

Abstract: A computer-implemented method according to one embodiment includes identifying a storage volume comprising a plurality of files, calculating a file level input/output operations per second (IOPS) value for each of a subset of the plurality of files within the storage volume, creating a predictive model for the storage volume, using metadata determined for the subset of the plurality of files and the TOPS values calculated for each of the subset of the plurality of files within the storage volume, estimating file level TOPS values for each of the plurality of files in the storage volume, utilizing the predictive model, combining the estimated and calculated file level TOPS values and comparing the combined values to a calculated volume level TOPS value for the storage volume, conditionally adjusting one or more of the estimated file level TOPS values, based on the comparing, and returning the estimated file level IOPS values.

Abstract: A computer identifies a plurality of data retrieval requests that may be serviced using a plurality of unique data chunks. The computer services the data retrieval requests by utilizing at least one of the unique data chunks. At least one of the unique data chunks can be utilized for servicing two or more of the data retrieval requests. The computer determines a servicing sequence for the plurality of data retrieval requests such that the two or more of the data retrieval requests that can be serviced utilizing the at least one of the unique data chunks are serviced consecutively. The computer services the plurality of data retrieval requests according to the servicing sequence.

Abstract: An approach for managing and optimizing available resources utilized in a networked computing environment (e.g., a cloud computing environment) is provided. In one aspect, a plurality of resources in a plurality of storage pools within the networked computing environment are monitored. Each storage pool is running at least one application workload using a corresponding subset of the plurality of resources. The storage pools are ranked based on the application workloads. In response to an over-utilized storage pool and/or an under-utilized storage pool, the plurality of resources are dynamically reconfigured.

Abstract: A method for distributing data among storage devices. The method comprising one or more processors receiving a first graph workload that executes within a networked computing environment. The method further includes identifying data from the first graph workload that is utilized during the execution of the first graph workload that includes a plurality of data packets. The method further includes creating a first graph workload model representative of the graph structure of the first graph workload and determining two or more partitions that are representative of a distribution of the identified data utilized by the first graph workload based, at least in part, on the first graph workload model. The method further includes allocating a plurality of network accessible storage devices among the two or more partitions and copying a first set of data packets of the plurality of data packets to a network accessible storage device.

Abstract: A method includes analyzing, by a computer device, data associated with tickets generated in a managed environment. The method also includes generating, by the computer device, a suggested rule change based on the analyzing. The method further includes receiving, by the computer device, an acceptance or denial of the suggested rule change.

Abstract: Embodiments of the invention relate to recovering from a disaster associated with an information technology environment. An information technology environment is replicated to a service provider. A recovery plan is generated for the environment. The recovery plan includes two processes. In response to the service provider receiving a disaster recovery request associated with the environment, the service provider executes a disaster recovery protocol. The protocol includes simultaneously executes the first and second processes. The first process operates a workload in the form of one or more containers, and the second process is a background process that creates a replica of the environment. After completion of the replica creation, the workload is migrated to the replica.

Abstract: An autonomous system for x-by-wire control includes processing nodes distributed and connected to one another. Sensors are connected to the processing nodes. Actuators are configured to directly control the autonomous system driven by and connected to the processing nodes for x-by-wire control. The processing nodes are configured to: partition and map processing tasks between the processing nodes, and merge and reduce results of individual processing tasks into an actionable table that specifies objectives for the autonomous system.

Abstract: A method for performing efficient data sampling across a storage stack for training machine learning (ML) models. The method includes obtaining, by a processor, data. The processor clusters the data into clusters based on similarities of the obtained data across an entire storage stack including: storage infrastructure metrics, file metrics and application dependency taxonomy. The processor performs a random sampling process to sample representative data from each cluster. The sampled representative data are combined to generate training data for processing predictive analytics.

Abstract: Software that performs the following steps is provided: (i) running an application on a first virtual machine on a first physical server, with the application including a first plurality of independently migratable elements, including a first independently migratable element that utilizes a first computing resource on the first virtual machine and a second independently migratable element that utilizes a second computing resource on the first virtual machine; and (ii) on condition that a first migration condition exists, migrating the first independently migratable element to a second virtual machine on a second physical server, such that the first independently migratable element is able to utilize a computing resource that is similar to the first computing resource on the second virtual machine on the second physical server while the second independently migratable element remains able to utilize the second computing resource on the first virtual machine on the first physical server.

Abstract: A computer-implemented method is provided for predicting cloud enablement from storage and data metrics harnessed from across stack. The computer-implemented method includes identifying a corpus of data to be classified, and configuring at least one access threshold and at least one sensitivity threshold. The computer-implemented method also includes classifying at least a portion the data within the corpus based on the at least one access threshold and the at least one sensitivity threshold. Finally, the computer-implemented method includes outputting a model, based on the classification, that identifies at least a portion of the data for migration for enabling a hybrid cloud environment.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for data recovery for use with a computing device. The embodiment may determine an amount of time to transfer a first file from a first location, on a first device, to a second location, on a second device. The embodiment may include receiving historical operational information associated with the first device and the second device. The embodiment may create a transfer model correlating the amount of time to transfer the first file with the historical operational information associated with each device. The embodiment may determine an estimated amount of time to transfer a second file based on the transfer model and a current operational information about each device. The embodiment may determine whether the estimated amount of time to transfer a second file is above a first threshold amount.

Abstract: An aspect includes storage stack analysis. A processor determines a client configuration of a client storage stack that includes a plurality of layers with configurable parameters to control storage and retrieval of data between an uppermost layer and a lowest layer. The processor initiates creation of a replica of the client storage stack based on the client configuration. Input/output characteristics are collected of a workload executed on the client storage stack using the client configuration. A synthesized workload is applied to the replica of the client storage stack, where the synthesized workload is based on the input/output characteristics of the workload. The processor analyzes input/output performance through the layers of the replica of the client storage stack in response to applying the synthesized workload. One or more configuration issues are identified with the client configuration of the client storage stack based on a result of the analysis.

Abstract: An aspect includes input/output (I/O) stack modeling. A processor determines a client configuration of a client I/O stack that includes layers with configurable parameters to control storage and retrieval of data between an uppermost layer and lowest layer. A model of the client I/O stack is built on a layer basis that defines input workload characteristics, output workload characteristics, and layer configuration parameters for the layers of the model based on the client configuration. Workload characteristics of the uppermost layer of the client I/O stack are fed to the model. The processor determines a statistical distribution of workload characteristics associated with each of the layers of the client I/O stack. I/O performance results are captured for layers of the model based on feeding the workload characteristics of the uppermost layer.

Abstract: Software that performs the following steps: (i) running a first customer application on a first set of virtual machine(s), with the first customer application including a first plurality of independently migratable elements, including a first independently migratable element and a second independently migratable element; (ii) dynamically checking a status of the first set of virtual machine(s) to determine whether a first migration condition exists; and (iii) on condition that the first migration condition exists, migrating the first independently migratable element to a second set of virtual machine(s) without migrating the second independently migratable element to the second set of virtual machine(s).

Abstract: In one embodiment, a method includes determining a first set of similarity metrics for a first storage environment, obtaining one or more second sets of similarity metrics from second storage environments that correspond with the first storage environment, calculating a similarity measurement score between the first storage environment and each of the second storage environments, and determining a set of similar storage environments from amongst the second storage environments based on the similarity measurement scores. Other methods, systems, and computer program products are presented according to more embodiments.

Abstract: Embodiments of the present invention provide an approach for 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. Under embodiments of the present invention, 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.

Abstract: A method for inferring an application type, based on an operational characteristic I/O pattern of a storage volume. One or more processors determine at least one operational characteristic of each storage volume of a storage volume group associated with an application. One or more labels are assigned for each storage volume, based on the operational characteristics of each storage volume. At least one template is received that includes labels of storage volume characteristics of known application types. One or more processors infer a type of application associated with the storage volume group, based on a best-fit match of the aggregate labels of the storage volumes of the storage volume group to the labels included in the templates of storage volume characteristics of known application types.

Abstract: Embodiments of the present invention provide an approach for 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. Under embodiments of the present invention, 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.

Abstract: A method is implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable storage medium having programming instructions. The programming instructions are operable to determine one or more optimal mappings between a server layer and a storage layer through a network layer based on performance metrics of one or more ports of at least one of the server layer, the storage layer and the network layer.