A framework for migrating a customer tenancy from a first identity and access management (TAM) system to a second IAM system. A first snapshot of the customer tenancy is obtained from a first data storage. The first snapshot is processed and migrated to the second IAM system. A second snapshot of the customer tenancy is obtained from a second data storage and migrated to the second IAM system. A state of a lock associated with the second data storage is modified, where after a third snapshot of the customer tenancy is obtained from the second data storage and migrated to the second IAM system. Responsive to the third snapshot being migrated, directing a request regarding the customer tenancy to the second IAM system.

A fog service layer architecture is disclosed using hierarchical fog node deployment including the co-existence and interactions of the fog node with a cloud node. The architecture also includes a list of functions, capabilities or services that are hosted in each fog node. One or more fog management procedures may be run between fog nodes (or between fogs and the cloud) and may comprise a fog capability discovery procedure, a fog connection verification procedure, and a fog capability status report procedure. In addition, fog nodes may be configured to interact with each other to get particular services using one or more fog service procedures described herein.

Deterministic replay of events between software entities. In current frameworks, replays of events (e.g., data communications) between software entities are non-deterministic and unreproducible. In an embodiment, as events are replayed, software entities, stimulated by those events, are queued according to a queuing strategy and executed from the queue. In an alternative embodiment, as software entities are executed, the events, output by those software entities, are queued according to a queuing strategy and played from the queue. Both embodiments ensure determinism and reproducibility across replays.

Systems, software, and methods for evaluating the scope of computer system changes related to automatic migration from one set of computing hardware to another provide methods and techniques that include evaluations for compliance with one or more policies prior to implementation, and then sequence and automate the migration tasks. A domain-specific language describes activity specifications and asset metadata, which is then used to generate interdependent activities in a project workstream on the basis of stored expert knowledge embedded in knowledge templates. Disaster recovery and “what-if” migration scenarios are tested in order to test and compare options of one or more proposed infrastructure changes.

A method of migrating a user profile to a virtual desktop infrastructure (VDI) system includes enumerating applications installed at an endpoint of a user, retrieving a list of application settings files, determining file and registry locations of user profile data relating to the applications installed at the endpoint from the application settings files, and retrieving the user profile data from the determined file and registry locations and storing the user profile data in a shared storage. When a user logs in to a virtual desktop of the VDI system, the user profile data is retrieved from the shared storage and imported into file and registry locations specified by the application settings files of applications that are installed in the virtual desktop.

Systems and methods are presented herein for implementing reinforcement learning in a task-facilitation service. The task-facilitation service may receive a request to delegate an execution of a task. The request can may include a user identifier that corresponds to the task. The task-facilitation service may generate a proposal using a machine-learning process. The proposal may include an implementation of the task and facilitate execution of the task by one or more third-party service providers. The task-facilitation service may facilitate the execution of the task by the one or more third-party service providers according to the proposal. In response to receiving an execution status of the task, the task-facilitation service may train the machine-learning process using the proposal and the execution status to improve subsequent proposals generated by the machine-learning process.

A computer system that includes a plurality of compute clusters that are located at different geographical locations. Each compute cluster is powered by a local energy source at a geographical location of that compute cluster. Each local energy source has a pattern of energy supply that is variable over time based on an environmental factor. The computer system further includes a server system that executes a global scheduler that distributes virtual machines that perform compute tasks for server-executed software programs to the plurality of compute clusters of the distributed compute platform. To distribute virtual machines for a target server-executed software program, the global scheduler is configured to select a subset of compute clusters that have different complementary patterns of energy supply such that the subset of compute clusters aggregately provide a target compute resource availability for virtual machines for the target server-executed software program.

One example method includes discovering computing workloads that are available to migrate from a current platform to a target platform, and the workloads are controlled by a user, determining that the computing workloads are migratable from the current platform to the target platform, ordering the computing workloads according to a respective measurable aspect, such as SLA (Service Level Agreement) for example, of each of the computing workloads, and generating a recommendation to the user that one of the computing workloads be migrated to the target platform, and the recommendation is generated based on a microstep score that has been assigned to the user.

System and computer-implemented method for migrating partial tree structures of virtual disks for virtual computing instances between sites in a computer system uses a compressed trie, which is created from target tree structures of virtual disks at a plurality of target sites in the computer system. For a virtual computing instance selected, the compressed trie is used to find candidate target sites based on a disk chain string of the virtual computing instance. For each candidate target site, a cost value for migrating the virtual computing instance along with a partial source tree structure of virtual disks corresponding to the virtual computing instance from the source site to the candidate target site is calculated to select a target site with a lowest cost value as a migration option to reduce storage resource usage in the computer system.

Techniques are provided for migrating data of sequential workloads to zoned storage devices. One method comprises obtaining a sequentiality classification of at least one workload of an application associated with a storage system comprising a plurality of zoned storage devices; and migrating data from one or more non-zoned storage devices that store data of the at least one workload to one or more zoned storage devices in response to the at least one workload being classified as a sequential workload. A sequentiality classification of a workload (e.g., as a sequential workload or a random workload) can be determined by: (i) evaluating the application name and/or application type of an application, (ii) learning input-output workload patterns, such as sequential read/write operations or random read/write operations, and/or (iii) detecting the application access mode to persistent volumes, such as a sequential write access mode.