International Business Machines Corporation

Samir Tata

Aly Megahed

Mohamed Mohamed

Ramani Ranjan Routray

Heiko Hary Ludwig

Nagapramod S Mandagere

Mu Qiao

Hovey R Strong, Jr

Ali Anwar

Shubhi Asthana

Ahmed El Harouni

Alaa Elwany

Obinna B Anya

Ali Balma

Nejib Ben Hadj-Alouane

Hana Teyeb

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.

Personalized device recommendations for proactive health monitoring and management

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.

Cognitive classification of workload behaviors in multi-tenant cloud computing environments

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.

Systemic adaptive data management in an internet of things environment

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.

Dynamic parallelism adjustment

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).

Method for making cognitive bidding decision

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.

Cognitive quality of service monitoring

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.

Adaptive multi-tenant monitoring in resource constrained environments

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.

Optimizing adaptive monitoring in resource constrained environments

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.

Monitoring system for metric data

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.

Cognitive elasticity of cloud applications

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.

Growing community of inventors

Cupertino, CA, United States of America

Samir Tata