Abstract: Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, hardware components may be bundled into hardware bundles. The hardware bundles may be used to satisfy subscriptions for the services, and limit use of the hardware bundles when subscription limits are reached. The hardware bundles may include direct management hardware components and indirect management hardware components. The performance of the hardware bundles may be characterized to facilitate future selection for use of the hardware bundles in various processes. Overtime, the performance of the hardware bundles may be checked.

Abstract: A method for managing VDI environments includes: obtaining a first resource related parameter (FRRP) for a first resource (FR) and a second resource related parameter (SRRP) for a second resource (SR) from a database; assembling a set of API combinations to generate a set of VDI environments; testing, for a per user average resource utilization value (PUARUV) of the FR and SR, each VDI environment across a range of users based on the FRRP and SRRP; providing the range of users and set of VDI environments to an analyzer, in which the analyzer is instructed to generate a model that ranks the set of VDI environments to obtain a VDI environment that provides the lowest PUARUV; generating a trained model by training the model using the range of users, set of VDI environments, PUARUVs, FRRP, and SRRP; and initiating notification of an administrator about the trained model.

Abstract: A method for managing a virtual desktop infrastructure (VDI) environment includes: obtaining a plurality of target resource specific pool specific configuration templates for a target resource, in which each of the plurality of target resource specific pool specific configuration templates is associated with one or a plurality of virtual desktop (VD) pools, in which the target resource is storage; obtaining a common configuration template set; generating a VD pool configuration for each of the plurality of VD pools using the plurality of target resource specific pool specific configuration templates and the common configuration template set to obtain a plurality of VD pool configurations; selecting a default VD pool from the plurality of VD pools; and deploying, based on the selection, a plurality of VDs into the default VD pool using a VD pool configuration associated with the default VD pool.

Abstract: A method for managing a virtual desktop infrastructure (VDI) environment includes: obtaining a plurality of target resource specific pool specific configuration templates for a target resource, in which each of the plurality of target resource specific pool specific configuration templates is associated with one or a plurality of virtual desktop (VD) pools, in which the target resource is memory; obtaining a common configuration template set; generating a VD pool configuration for each of the plurality of VD pools using the plurality of target resource specific pool specific configuration templates and the common configuration template set to obtain a plurality of VD pool configurations; selecting a default VD pool from the plurality of VD pools; and deploying, based on the selection, a plurality of VDs into the default VD pool using a VD pool configuration associated with the default VD pool.

Abstract: Methods and systems for managing a data processing system are disclosed. To manage the data processing system, a workload request may be obtained, and a type of the workload may be identified. Based on the type of the workload, an optimized frequency for a hardware component to perform the workload and a power overhead for configuring the hardware component based on the optimized frequency may be obtained. A first power consumption estimate may be obtained for performing the workload during an available time slot, and configuring the hardware component based on the optimized frequency. A second power consumption estimate may be obtained for performing the workload without configuring the hardware component. A preferred configuration for the available time slot to minimize power consumption may be identified. A data processing system and a time slot for performance of the workload may be selected in which the power consumption estimate is lowest.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To do so, a non-actionable description of a goal may be obtained for use of an artificial intelligence (AI) model in a workflow performed by the system. Key performance indicators (KPI) may also be obtained for this use. Based on the non-actionable description and/or the KPI, metrics may be obtained. An AI architecture may be selected for the AI model based on the metrics and/or the KPI, and, based at least on the KPI, a hardware system may be selected for the AI architecture. Based on what is selected, deployment of the system may then be initiated to facilitate the use of the AI model in the workflow.

Abstract: Methods, systems, and devices are provided for managing operation of a distributed system. To do so may be based on a request to update the operation to facilitate provisioning of artificial intelligence (AI) based computer implemented services. Based on the request, an AI architecture map may be obtained that defines logical components to provide the services. A plurality of likely use maps may be obtained that indicate how the services are likely to be used. A goal architecture may be obtained based on the AI architecture map and the plurality of likely use maps. Based on at least the goal architecture, a plurality of hardware components and a plurality of software components may be selected. These selections may be deployed to the distributed system, based on the goal architecture, to obtain an updated distributed system. The services may thereby be provided using the updated distributed system.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage the system, supplemental capabilities may be identified and added to the system. The supplemental capabilities may be based on inferred changes in use of the system over time. The supplemental capabilities may be managed over time to meet changes in use of services provided by the system over time, while limiting excess capabilities of the system and meeting existing expectations regarding use of the system.

Abstract: Methods and systems for managing operation of a system are disclosed. Architectural information for the system (e.g., indicating performance of the system) may be obtained while the system is providing a first computer-implemented service. A testing process may be performed using the architectural information and a knowledge base that includes architectural requirements for providing a second computer-implemented service. During the testing process, a performance limitation for the system that prevents the system from providing the second computer-implemented service in a desired manner may be identified. Based on the performance limitation, an action for updating the operation of the system may be identified. The action may be performed to update the operation of the system to increase a likelihood of the system providing at least the second computer-implemented service in the desired manner.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To manage operation of the system, limits on use of the system may be identified. Operation of the system may be compared to the limits. If the limits are exceeded, then updates to operation of the system may be made. The updates may change the usability of the system to meet various limits on the use of the system.

Abstract: Methods, systems, and devices are provided for managing operation of a system. To do so, a suspect signature may be obtained based on an artificial intelligence architecture used by a suspect system. A relevant signature may be obtained using information regarding managed artificial intelligence based systems, the suspect system not being any of the managed systems. A level of similarity may be identified between the suspect signature and the relevant signature, and if the level of similarity meets criteria, it may be concluded that the suspect system has an unauthorized configuration. Based on this conclusion, at least one action may be performed to initiate update of the suspect system's operation to prevent use of the unauthorized configuration in computer implemented services provided by the suspect system.

Abstract: Methods and systems for managing customer-encountered artificial intelligence (AI) model issues using service agents are disclosed. To manage the customer-encountered AI model issues, a service request may be obtained. A first categorization process may be performed for the service request to obtain a type of AI workload. A second categorization process may be performed to obtain a type of support service used to provide the type of AI workload. A third categorization process may be performed to obtain a type of hardware resource used to provide the support service. Based on at least the type of AI workload, the type of support service, the type of hardware resource, and a qualification ranking of the service agents, a service agent may be selected to remediate the customer-encountered AI model issue. The service request may be resolved by assigning the service agent to work the service request.

Abstract: Methods and systems for managing the operation of a deployment are provided. The deployment may be managed in accordance with a subscription model. The subscription model may use subscriptions to define the extent and limits on use of services provided by the deployment. The subscription services and limits may be enforced by management controllers of network devices of the deployment. The management controllers may operate independently from control planes and data planes of the network devices.

Abstract: Methods, systems, and devices for providing for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, hardware components may be bundled into hardware bundles. The hardware bundles may be used to satisfy subscriptions for the services, and limit use of the hardware bundles when subscription limits are reached. The hardware bundles may include direct management hardware components and indirect management hardware components. Limits on use of the hardware bundles may be enforced by analyzing workloads performed by the hardware bundles. Proactive action may be performed to reduce the likelihood of the limits being reached.

Abstract: Methods and systems for performing workloads are disclosed. To perform the workloads, operations may be performed by compute complexes. The compute complexes may perform some types of operations inefficiently. To accelerate completion of the workloads, operations to be performed by the compute complexes may be analyzed by other hardware components in a manner that is transparent to the compute complexes. Operations that may be performed more quickly by the other hardware components may be automatically and transparently offloaded.

Abstract: Methods and systems for managing service deployment are disclosed. To deploy services, dependencies and other characteristics (such as context data, capability data, placement rules, and workflow data) of services and the infrastructure boundaries in which the services are running may be dynamically collected, analyzed and updated. By dynamically analyzing dependencies and other characteristics (such as context data, capability data, placement rules, and workflow data) of services and the infrastructure boundaries, efficiency of use of computing resources may be improved by recycling existing workflow; statically defined operations and hard-coded conditional logic of services may also be avoided each time an environment in which the services are running has changed.

Abstract: Methods and systems for providing security management services are disclosed. To provide security management services in a manner that reduces the quantity of hardware resources necessary to provide the security management services, the security manager may prioritize security management services based on the likelihood of individuals posing a security risk. To identify the security risks of the individuals, the paths of the individuals may be tracked and compared to typical paths through environments that are followed by most individuals that traverse through the environment. The individuals following a typical path may not indicate a security risk and a resource prioritization may be retained or decreased for the individuals not posing a security risk. The individuals not following a typical path may indicate a security risk and a resource prioritization may be increased for the individuals posing a security risk.

Abstract: Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, hardware components may be bundled into hardware bundles. The hardware bundles may be used to satisfy subscriptions for the services, and limit use of the hardware bundles when subscription limits are reached. The hardware bundles may include direct management hardware components and indirect management hardware components. Limits on use of the hardware bundles may be enforced by a variety of control mechanisms.

Abstract: A model training recalibration method includes: receiving a notification including a recommendation for recalibrating an existing model training routine; making a first determination, in response to receiving the notification, that the existing model training routine should be recalibrated; in response to the first determination, instantiating a recalibration of the existing model training routine based on the recommendation.

Abstract: A system for managing of a security based inferencing workload placement includes a security a variant selection agent, production environments, and a front-end device that includes a security variant initial agent and a variant selection agent. The security variant initial agent is programmed to: obtain a request for an inferencing payload for an artificial intelligence (AI) model, wherein the request comprises a prompt for the AI model generated by the user, in response to the request: perform an initial classification analysis on the request to obtain a classification-eligible tag for the request, make a first determination, based on the classification-eligible tag, that the request is classification-eligible, based on the first determination, transmit the request to the variant selection agent of the front-end device for a variant selection operation.