International Business Machines Corporation

Mohamed Mohamed

Samir Tata

Aly Megahed

Nagapramod S Mandagere

Heiko Hary Ludwig

Yang Song

Dean Hildebrand

Robert Engel

Bryan S Langston

Amit Suresh Warke

Ramani Ranjan Routray

Mu Qiao

Gabriel Alatorre

Ali Anwar

Ahmed El Harouni

Alaa Elwany

Obinna B Anya

Nejib Ben Hadj-Alouane

Hana Teyeb

Aikaterini Stamou

Ali Balma

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

Embodiments relate to a computer system, computer program product, and method to support context aware container volume management. Two or more containers are operatively coupled to a shared operating system instance. Workflow and system topologies directed at the containers are modeled. The workflow models transition states of containers. The container topology model identifies relationships between two more containers. The storage topology model identifies a relationship and dependency of storage volumes. The models are leveraged to assess and autonomously determine functional container storage requirements. Assignment(s) of an application container to a volume is optimized and selectively attached responsive to the autonomous determination.

Context aware container management

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 method includes generating, by a processor, an index data structure including allocation of nodes that each represent a computing element of multiple computing elements. In response to determining a change in status of any of the nodes, the method propagates changes in status of any computing element between associated node levels of the index data structure using an application programming interface (API). An updated status of one or more of the nodes is provided based on the change in status.

Discovering and publishing device changes in a cloud environment

An embodiment of the invention may include a method, computer program product and system for data storage management. The embodiment may include receiving a request from a second container runtime for access to a first container runtime volume by the second container runtime. The embodiment may include determining whether the first container runtime volume exists within a shared unified volume listing. Based on determining that the first container runtime volume exists within the shared unified volume listing, the embodiment may include transforming volume information of the first container runtime volume from a unified volume listing schema to a second container runtime volume listing schema. The embodiment may include returning the volume information of the first container runtime volume to the second container runtime.

Scale-out container volume service for multiple frameworks

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.

Optimal dynamic placement of virtual machines in geographically distributed cloud data centers

The embodiments described herein relate to dynamically managing metric data of a network environment with respect to a data storage system. A data retention policy is analyzed, which includes extracting one or more metric definitions from the retention policy. A relevance of a set of metric data is identified based on the analysis. The set of metric data includes an aggregation of one or more metric observations. A storage location in a data storage system for the set of metric values is selected based on the identified relevance. The data storage system includes a cache storage location and a persistent storage location. The set of metric data is retained in the selected storage location. As the retention policy is modified, select data may be re-classified and moved within the storage system based on the re-classification.

Cost effective service level agreement data management

The embodiments described herein relate to dynamically optimizing a mapping from applications to shared service providers via one or more monitoring modules. A software layer positioned between a set of requestor applications and a set of shared service providers is configured. The layer is populated with a collection of one or more monitoring modules, with each monitoring module configured to monitor application resource usage. In response to receiving a monitoring request from the set of requestor applications, a mapping of the received request to the collection of xlets is dynamically optimized.

Growing community of inventors

San Jose, CA, United States of America

Mohamed Mohamed