Abstract: In an example embodiment, the concept of effective application availability is introduced. In a data center, there may be a minimum amount of a particular resource that is needed to continue proper functioning of an application. This may be termed a safe application availability limit. The safe application availability limit may be obtained or determined using a safe application availability limit rule. An effective application availability may be calculated by taking a current application availability and subtracting it by an availability loss from a requested operation. The effective application availability may then be compared to the safe application availability limit in order to determine whether to permit the operation to be performed at the requested time, or whether to request that the operation be attempted again at some later time.

Abstract: In an example embodiment, the concept of effective application availability is introduced. In a data center, there may be a minimum amount of a particular resource that is needed to continue proper functioning of an application. This may be termed a safe application availability limit. The safe application availability limit may be obtained or determined using a safe application availability limit rule. An effective application availability may be calculated by taking a current application availability and subtracting it by an availability loss from a requested operation. The effective application availability may then be compared to the safe application availability limit in order to determine whether to permit the operation to be performed at the requested time, or whether to request that the operation be attempted again at some later time.

Abstract: A computer implemented method for selecting service providers includes receiving a set of client requirements and analyzing available service providers based on the received set of client requirements. The method additionally includes scoring the available service providers based on the analysis. The method further includes identifying one or more unstructured external data sources corresponding to the available service providers and analyzing the reliability of the one or more unstructured external data sources with respect to the available service providers. The method further includes adjusting the scoring of the service providers based, at least in part, on the data source reliability, and subsequently providing an optimal selection of service providers based on the adjusted scoring. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: Systems, methods, and computer program products for providing personalized recommendations of devices for monitoring and/or managing a health condition are disclosed, and generally include receiving first structured information regarding a patient and a first set of one or more patient populations; receiving unstructured information regarding at least the patient and a second set of one or more patient populations; analyzing the unstructured information to derive second structured information; determining one or more health metrics to be monitored for the patient based on analyzing each of the first structured information and the second structured information, using a classification model; and determining an optimum set of devices to be used for monitoring the one or more health metrics. In some embodiments, metrics may be continuously monitored to detect a change exceeding an event trigger threshold, and a new set of recommended devices may be generated.

Abstract: One embodiment provides a method comprising receiving data relating to a tenant utilizing a cloud computing environment, and determining one or more classifications for a variation in current workload resource consumption of the tenant based on the data. The current workload resource consumption is indicative of current usage of one or more computing resources of the cloud computing environment.

Abstract: A computer-implemented method according to one embodiment includes identifying a plurality of policies to be implemented within a system, aggregating the plurality of policies to create an aggregated policy, disseminating the aggregated policy within the system, receiving data collected according to the aggregated policy, and disaggregating the data.

Abstract: A computer-implemented method includes receiving characteristics of available resources usable for downloading layers of a container image and fetching a manifest of the container image from a container registry. The method includes determining layers of the container image to be downloaded based on the manifest and, based on the characteristics of the available resources and sizes of the layers to be downloaded, adjusting an optimal parallelism to download the layers. The method includes downloading the layers.

Abstract: A method for a provider to make a cognitive bidding decision may include using a processor and associated memory to a) obtain information, including unstructured data, relating to deals with procuring organizations, and b) determine a respective predicted business growth of each procuring organization based upon the information. The method may also include using the processor and the associated memory to c) determine a respective predicted growth of provider services for each procuring organization based upon the information, and d) determine a respective probability of winning each bid for each deal from each procuring organization. The method may further include using the processor and the associated memory to e) determine a respective predicted revenue based upon winning each bid, and determine whether to submit each bid based upon b), c), d), and e).

Abstract: One embodiment provides a quality of service (QoS) monitoring framework for dynamically binding one or more customer applications to one or more microservices in a dynamic service environment, collecting compliance data and contextual data from the dynamic service environment and one or more hosting environments, and modifying a monitoring infrastructure for the one or more customer applications based on the compliance data and the contextual data.

Abstract: An example method for multi-tenant adaptive monitoring comprises detecting occurrence of a trigger event and modifying a selection of metrics included in a plurality of monitored metrics that are monitored using available resources of a plurality of tenants. The method further comprises assigning a respective monitoring frequency for each metric; computing respective weights for each metric in the modified selection of metrics; performing a feasibility check to find a solution to a mathematical model for monitoring the modified selection of metrics at the respective assigned monitoring frequency for each metric; and, in response to determining that a solution to the mathematical model cannot be found, adjusting the respective monitoring frequency for one or more metrics. The method further comprises, in response to finding a first solution to the mathematical model, allocating processing associated with monitoring each metric among the available resources of the plurality of tenants.

Abstract: Adaptive monitoring dynamically optimizes the monitoring frequency of metrics with respect to system constraints. One or more metrics are monitored. The monitoring includes receiving a value for the metric and evaluating the received metric value. If the evaluation is determined to affect one or monitoring parameters, or if an environment-based event occurs the metrics are adapted. Adapting metrics includes removing or adding a metric based on each metric's correlation to the affected monitoring parameter or environment based trigger. The frequencies of the metrics are optimized based on the available resources.

Abstract: A computer-implemented method includes receiving characteristics of available resources usable for downloading layers of a container image and fetching a manifest of the container image from a container registry. The method includes determining layers of the container image to be downloaded based on the manifest and, based on the characteristics of the available resources and sizes of the layers to be downloaded, adjusting an optimal parallelism to download the layers. The method includes downloading the layers.

Abstract: A monitoring system for metric data may include devices, each device capable of generating respective metric data. The monitoring system may also include a processor and a memory cooperating therewith. The processor may be configured to monitor the devices via a network to obtain the respective metric data, generate predicted trigger events based on monitoring the devices, and generate a respective adapted monitoring for the devices based upon each predicted trigger event. The processor may also be configured to, upon occurrence of one of the predicted trigger events, implement the respective adapted monitoring to obtain new respective metric data.

Abstract: A computing device retrieves historical data regarding one or more historical time periods during which cloud application instances were provisioned. A probability distribution of a number of queries received by the historical cloud application instances during each of the one or more historical time periods is determined by the computing device. A probability distribution of a number of received queries completed by each of the one or more historical cloud application instances during each of the one or more historical time periods is determined by the computing device. A new provisioning plan for further time periods is generated via application of a stochastic optimization model based upon the probability distribution of the number of received queries during each of the one or more historical time periods and the probability distribution of the number of received queries completed by each of the one or more cloud application instances.

Abstract: A computer-implemented method according to one embodiment includes identifying a plurality of policies to be implemented within a system, aggregating the plurality of policies to create an aggregated policy, disseminating the aggregated policy within the system, receiving data collected according to the aggregated policy, and disaggregating the data.

Abstract: One embodiment provides a method comprising receiving a first set of data relating to a plurality of applications to be monitored, receiving a second set of data relating to one or more available resources, and determining one or more recommended allocations of the one or more available resources for monitoring the plurality of applications based on each set of data received. The first set of data includes unstructured data.

Abstract: A method for a provider to make a cognitive bidding decision may include using a processor and associated memory to a) obtain information, including unstructured data, relating to deals with procuring organizations, and b) determine a respective predicted business growth of each procuring organization based upon the information. The method may also include using the processor and the associated memory to c) determine a respective predicted growth of provider services for each procuring organization based upon the information, and d) determine a respective probability of winning each bid for each deal from each procuring organization. The method may further include using the processor and the associated memory to e) determine a respective predicted revenue based upon winning each bid, and determine whether to submit each bid based upon b), c), d), and e).

Abstract: A method for cognitive handling of workload requests in a Cloud environment including data centers (DCs) may include operating a processor and associated memory to obtain historical resource consumption data of historical workloads of the DCs. The method may also include operating the processor to generate a trained prediction model based upon the historical resource consumption data, obtain current resource consumption data of current workloads of the DCs, and operate the trained prediction model based upon the current resource consumption data to generate predicted future resource consumption data for future workloads of the DCs. The method may also include operating the processor to receive a workload request, and generate a recommended handling of the workload request based upon the predicted future resource consumption data.

Abstract: A computer-implemented method according to one embodiment includes identifying a set of virtual machines to be placed within a system; receiving characteristics associated with the set of virtual machines; determining characteristics associated with a current state of the system; determining a placement of the set of virtual machines within the system, based on the characteristics associated with the set of virtual machines and the characteristics associated with a current state of the system; determining an updated placement of all virtual machines currently placed within the system, based on the characteristics associated with the set of virtual machines and the characteristics associated with a current state of the system; determining a migration sequence within the system in order to implement the updated placement of all virtual machines currently placed within the system; and implementing the updated placement, utilizing the migration sequence.

Abstract: A computer-implemented method according to one embodiment includes identifying a set of virtual machines to be placed within a system, receiving characteristics associated with the set of virtual machines, determining characteristics associated with a current state of the system, determining a placement of the set of virtual machines within the system, based on the characteristics associated with the set of virtual machines and the characteristics associated with a current state of the system, determining an updated placement of all virtual machines currently placed within the system, based on the characteristics associated with the set of virtual machines and the characteristics associated with a current state of the system, and determining a migration sequence within the system in order to implement the updated placement of all virtual machines currently placed within the system.