Abstract: For power management in a disaggregated computing system, upon detecting an electrical power reduction to the disaggregated computing system, respective workloads being performed by respective processors within a set of processors are prioritized according to a service level agreement (SLA) of the respective workloads. Backup electrical power is dynamically allocated to the respective processors performing the respective workloads based upon a priority of the SLA of the respective workloads.

Abstract: For measuring component utilization in a system having a plurality of subsystems, an energy consumption of each of the plurality of subsystems is monitored whether or not each subsystem performs at least a portion of an overall computation. Respective workloads are classified based upon an energy consumption pattern associated with the monitored energy consumption of each of the plurality of subsystems.

Abstract: For power management in a computing system, component utilization is dynamically managed within the computing system according to a calculated aggregate energy consumed by each one of a set of processors. Each of a plurality of energy factors are measured individually between each one of the set of processors to accumulate the calculated aggregate energy in real time.

Abstract: For measuring component utilization in a computing system, a server energy utilization reading of a statistical significant number of servers out of a total number of servers located in the datacenter is obtained by measuring, at predetermined intervals, a collective energy consumed by all processing components within each server. The collective energy is measured by virtually probing thereby monitoring an energy consumption of individual ones of all the processing components to each collect an individual energy utilization reading, where the individual energy utilization reading is aggregated over a predetermined time period to collect an energy consumption pattern associated with the server utilization reading.

Abstract: For power management in a disaggregated computing system, a set of initial electrical power levels are allocated to a set of processor cores according to a predicted desired workload, where the set of initial power levels aggregate to an initial collective contracted power level. Electrical power is dynamically allocated to respective processor cores within the set of processor cores to produce a capacity to execute a collective demanded workload while maintaining the electrical power to the set of processor cores to an approximately constant electrical power level within a threshold of the initial collective contracted electrical power level.

Abstract: For power management in a disaggregated computing system, initial electrical power levels are distributed thereby allocating a voltage and a clock speed to each one of a set of processor cores in the disaggregated computing system. The voltage and the clock speed of respective processor cores within the set of processor cores are adjusted according to a workload priority of respective workloads performed by each respective one of the processor cores, wherein the workload priority is assigned based upon a service level agreement (SLA).

Abstract: Various embodiments for agile component-level resource provisioning in a disaggregated cloud computing environment, by a processor device, are provided. Respective members of pools of hardware resources within the disaggregated cloud computing environment are allocated to each respective one of a plurality of tenants according to one of a plurality of service level agreement (SLA) classes. Each respective one of the plurality of SLA classes is characterized by a given response time for the allocation of the respective members of the pools of hardware resources corresponding to a requested workload by the tenant.

Abstract: Various embodiments for optimizing memory bandwidth in a disaggregated computing system, by a processor device, are provided. Respective memory devices are assigned to respective processor devices in the disaggregated computing system, the disaggregated computing system having at least a pool of the memory devices and a pool of the processor devices. An analytic function is performed on data resident in the pool of the memory devices using memory bandwidth not currently committed to a primary compute task.

Abstract: Various embodiments for elastic resource provisioning in a disaggregated cloud computing environment, by a processor device, are provided. Respective members of pools of hardware resources within the disaggregated cloud computing environment are provisioned to a tenant according to an application-level service level agreement (SLA). Upon detecting a potential violation of the application-level SLA, additional respective members of the pools of hardware resources are provisioned on a component level to the tenant to avoid a violation of the SLA by one of a scale-up process and a scale-out process.

Abstract: Various embodiments for optimizing memory bandwidth in a disaggregated computing system, by a processor device, are provided. Respective memory devices are assigned to respective processor devices in the disaggregated computing system, the disaggregated computing system having at least a pool of the memory devices and a pool of the processor devices. An iterative learning algorithm is used to define data boundaries of a dataset for performing an analytic function on the dataset using memory bandwidth not currently committed to a primary compute task.

Abstract: Various embodiments for allocating resources in a disaggregated cloud computing environment, by a processor device, are provided. Respective members of a pool of hardware resources are assigned to each one of a plurality of tenants based upon a classification of the respective members of the pool of hardware resources. The respective members of the pool of hardware resources are assigned to each one of the plurality of tenants independently of a hardware enclosure in which the respective members of the pool of hardware resources are physically located.

Abstract: An unmanned aerial vehicle for interacting with a pet. The unmanned aerial vehicle includes a processor-based monitoring device to provide a behavioral assessment of the pet, an activity recommender to select an activity program dependent on the behavioral assessment, a motor mounted on the unmanned aerial vehicle to provide aerial movement based on the activity program, and an activity coordinator to perform a function based on the activity program. The function includes activating feedback outputs upon completion of the activity program.

Abstract: An unmanned aerial vehicle for interacting with a pet. The unmanned aerial vehicle includes a processor-based monitoring device to provide a behavioral assessment of the pet, an activity recommender to select an activity program dependent on the behavioral assessment, a motor mounted on the unmanned aerial vehicle to provide aerial movement based on the activity program, and an activity coordinator to perform a function based on the activity program. The function includes activating feedback outputs upon completion of the activity program.

Abstract: A system and method dynamically provisions resources in a virtual environment. A current resource requirement is determined based on a current workload demand using one or more computer systems providing resources and access to the resources. The method and system includes comparing the current resource requirement with a current resource allocation using an engine communicating with resources. The engine is configured to allocate the resources, and the engine determines the resource requirement responsive to communications with a plurality of library instances. The library exposes a single-node interface for use by a user-application. The current resource allocation is modified based on the comparison of the current resource requirement with the current resource allocation, and in response to the current resource requirement, using the engine.

Abstract: An unmanned aerial vehicle for interacting with a pet. The unmanned aerial vehicle includes a processor-based monitoring device to provide a behavioral assessment of the pet, an activity recommender to select an activity program dependent on the behavioral assessment, a motor mounted on the unmanned aerial vehicle to provide aerial movement based on the activity program, and an activity coordinator to perform a function based on the activity program. The function includes activating feedback outputs upon completion of the activity program.

Abstract: A method for providing a cognitive alert control framework for a mobile device, wherein the mobile device includes an alert mechanism is provided. The method may include detecting, by a processor, a call being initiated by a caller using a first mobile device to a recipient using a second mobile device. The method may also include determining a cognitive state of the caller. The method may further include determining a cognitive state of the recipient. The method may include analyzing a relationship between the caller and the recipient based on the determined cognitive state of the caller and the determined cognitive state of the recipient. The method may also include determining a level of alert associated with second mobile device based on the analyzed relationship. The method may include invoking a ringtone characteristic on the second mobile device based on the determined level of alert.

Abstract: Meta data specifying modification constraints for one or more of the attributes of an (operational) policy is generated/provided. Thereafter, the meta data is associated with the policy so that the constraints specified by the meta data can govern the modification of the policy. Under the present invention there are at least two ways of associating the meta data with a policy. In one embodiment, the meta data is embodied as a meta policy that can be associated with one or more (operational) policies. In another embodiment, the meta data is inserted into individual policies as additional attributes.

Abstract: A method for providing a cognitive alert control framework for a mobile device, wherein the mobile device includes an alert mechanism is provided. The method may include detecting, by a processor, a call being initiated by a caller using a first mobile device to a recipient using a second mobile device. The method may also include determining a cognitive state of the caller. The method may further include determining a cognitive state of the recipient. The method may include analyzing a relationship between the caller and the recipient based on the determined cognitive state of the caller and the determined cognitive state of the recipient. The method may also include determining a level of alert associated with second mobile device based on the analyzed relationship. The method may include invoking a ringtone characteristic on the second mobile device based on the determined level of alert.

Abstract: In one embodiment, a computer-implemented method includes receiving a query. A target confidence level is established for the query, the target confidence level representing a requested level of accuracy for a result of the query. At least one individual confidence level is assigned to each of a plurality of analytics engines. One or more analytics engines are queried based on the query. A group of the analytics engines are selected, by a computer processor, where the analytics engines in the selected group have query responses to the query that are deemed to be similar to one another, and where the selection of the selected group is at least partially based on the target confidence level. The query responses from the selected group of analytics engines are summarized into a final result, where the final result is an answer to the query.

Abstract: A system and method are provided. The method includes receiving, by a server, characteristics of a work product produced by using at least one application in a device. The method further includes computing, by the server, measures of at least one of a complexity, a quality, and an expertise level for the work product responsive to the characteristics. The method also includes determining, by the server, whether to cause at least one hardware device to selectively perform an action relating to using the application, responsive to the measures.