Abstract: An apparatus comprises a processing device configured to perform natural language processing of a first set of documents to generate a first data structure and a second set of documents to generate a second data structure, the first set of documents characterizing information associated with a user of an organization and the second set of documents characterizing information associated with the organization. The processing device is further configured to process the first data structure and the second data structure utilizing one or more machine learning models to generate a third data structure characterizing resources of an information technology infrastructure to be allocated for use by the user of the organization, and to generate one or more control signals for allocating one or more resources of the information technology infrastructure for use by the user of the organization based at least in part on the third data structure.

Abstract: A method obtains at least one virtual representation representing an infrastructure in a first state and applies a first dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a second state. The method obtains results representing the infrastructure in the second state, wherein at least a portion of the results are indicative of a predicted condition associated with the infrastructure based on usage of the infrastructure. The method applies a second dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a third state and obtains results representing the infrastructure in the third state, wherein at least a portion of the results are indicative of an outcome to a solution applied via the second dataset to the predicted condition associated with the infrastructure.

Abstract: A method identifies training data from at least one of a plurality of data sources, wherein the identified training data is determined to be suitable for a use case for which the model is to be used for representing the infrastructure. The method trains the model based on the identified training data. The method monitors at least one of a performance and an accuracy of the model. The method identifies different training data from at least one of the plurality of data sources, responsive to the monitoring, wherein the identified different training data is determined to be more suitable for the use case for which the model is to be used for representing the infrastructure. The method retrains the model based on the identified different training data.

Abstract: A method obtains at least one virtual representation representing an infrastructure in a first state and applies a first dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a second state. The method obtains results representing the infrastructure in the second state, wherein at least a portion of the results are indicative of a predicted health condition associated with the infrastructure. The method applies a second dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a third state and obtains results representing the infrastructure in the third state, wherein at least a portion of the results are indicative of an outcome to a solution applied via the second dataset to the predicted health condition associated with the infrastructure.

Abstract: A method obtains at least one virtual representation of an infrastructure, wherein the virtual representation represents the infrastructure. The method applies, in a plurality of iterations, a plurality of simulated workloads to the virtual representation to artificially advance the virtual representation to generate a plurality of datasets respectively representing the infrastructure in a plurality of states. The method applies one or more gamification processes, between one or more of the plurality of iterations, to alter the corresponding one or more datasets generated by the virtual representation, wherein the one or more altered datasets are applied to the virtual representation along with one or more of the plurality of simulated workloads to represent the infrastructure in one or more subsequent states of the plurality of states. The method obtains gamification results representing the infrastructure, responsive to applying the one or more gamification processes.

Abstract: A method obtains at least one virtual representation of an infrastructure, wherein the virtual representation represents the infrastructure in a first state. The method applies a dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a second state. The method obtains results representing the infrastructure in the second state, responsive to applying the dataset to the virtual representation, wherein at least a portion of the results are indicative of a predicted power consumption associated with the infrastructure. The method initiates one or more actions with respect to the infrastructure in accordance with the predicted power consumption.

Abstract: Application monitoring techniques in an information processing system environment are disclosed. In one example, at least one processing device is configured to obtain an indication of at least one anomalous behavior associated with execution of an application in an information processing system, wherein the application comprises a plurality of services. The processing device is further configured to analyze, across a plurality of time periods, at least one metric associated with the execution of the application to determine at least one critical path associated with the execution of the application, wherein the critical path comprises at least a portion of the plurality of services. The processing device is then configured to analyze the critical path using a set of variance correlation algorithms and identify a set of one or more services in the critical path that are highest in a ranked order determined by the set of variance correlation algorithms.

Abstract: Application monitoring techniques comprising predictive anomaly detection and fault isolation are disclosed for use in an information processing system environment. For example, an apparatus comprises at least one processing device comprising a processor coupled to a memory. The processing device is configured to obtain application-level data generated for an information processing system in accordance with execution of an application and obtain hardware-level data generated for the information processing system in accordance with the execution of the application. The processing device is further configured to utilize an unsupervised machine learning model to predictively detect anomalous behavior in accordance with the execution of the application in the information processing system, based on at least a portion the application-level data and the hardware-level data. The processing device may also initiate fault isolation in addition to predicting anomalous behavior.

Abstract: Techniques for management of virtual representations (e.g., digital twins) of infrastructure are disclosed. For example, a method comprises obtaining at least one virtual representation of an infrastructure, wherein the virtual representation represents the infrastructure in a first state. The method further comprises applying at least one dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a second state.

Abstract: An apparatus comprises at least one processing device configured to, in a monitoring system in which at any given time at least one monitoring node has a primary monitoring role responsible for monitoring operation of system nodes of a distributed system and other monitoring nodes have a secondary monitoring role responsible for monitoring operation of the at least one monitoring node having the primary monitoring role, identify a first monitoring node having the primary monitoring role at a current time. The processing device is also configured to determine, based at least in part on a time-based ranking of the monitoring nodes, a second monitoring node having the secondary monitoring role at the current time to transition to the primary monitoring role, and to transition the second monitoring node to the primary monitoring role at a subsequent time.

Abstract: An apparatus comprises at least one processing device configured to, in a monitoring system in which at any given time at least one monitoring node has a primary monitoring role responsible for monitoring operation of system nodes of a distributed system and other monitoring nodes have a secondary monitoring role responsible for monitoring operation of the at least one monitoring node having the primary monitoring role, identify a first monitoring node having the primary monitoring role at a current time. The processing device is also configured to determine, based at least in part on a time-based ranking of the monitoring nodes, a second monitoring node having the secondary monitoring role at the current time to transition to the primary monitoring role, and to transition the second monitoring node to the primary monitoring role at a subsequent time.

Abstract: An apparatus comprises at least one processing device having a processor coupled to a memory. The processing device is configured to implement a first ledger node of a first cloud. The first ledger node of the first cloud is configured to communicate over one or more networks with a plurality of additional ledger nodes associated with respective additional clouds. The first ledger node is further configured to obtain a transaction associated with a valuation of a data asset. The first ledger node is further configured to broadcast the valuation transaction to the additional ledger nodes. A cryptographic block characterizing at least the valuation transaction is generated and entered into a blockchain distributed ledger collectively maintained by the first and additional ledger nodes. The first and additional ledger nodes collectively maintain the blockchain distributed ledger on a peer-to-peer basis without utilizing a centralized transaction authority.

Abstract: A method, apparatus and computer program product for handling secure information (e.g., a password, an account number, a personal identification number (PIN), a user identifier, an encryption key, and a path where said secure information is stored) is presented. Secure information is stored in a software container. A plurality of representations of the secure information is provided, each of the plurality of representations for use under different conditions. Dependent on the particular condition, at least one of the plurality of representations of the secure information is provided by the container. The container holds the secure information during all stages of processing in a manner that prevents unauthorized parties from gaining access to the secret in clear form.

Abstract: A system provides of data islands of a federated environment storing scalable data used to efficiently search for data and/or other objects across the federated environment and access details of any objects while minimizing the data replicated across all of the data islands. The scalable data may be replicated across all of the data islands and allow a local search for any object across the federated environment. The scalable data may be a subset of data and/or configured meta data that stores a limited subset of data that enables the local search, at each data island, for objects stored in the federated environment.

Abstract: Example embodiments of the present invention relate to a method, a system, and a computer program product for data analytics. The method includes receiving a plurality of first data streams from respective managed elements in a network and partitioning data of the plurality of first data streams according to an attribute regarding the data into a partitioned second data stream. The partitioned second data stream then may be streamed toward a data analytics platform for consumption by the data analytics platform. In a preferred embodiment, WebSockets are used.

Abstract: A graphics processing unit (GPU) service platform includes a control server, and a cluster of GPU servers each having one or more GPU devices. The control server receives a service request from a client system for GPU processing services, allocates multiple GPU servers within the cluster to handle GPU processing tasks specified by the service request by logically binding the allocated GPU servers, and designating one of the at least two GPU servers as a master server, and send connection information to the client system to enable the client system to connect to the master server. The master GPU server receives a block of GPU program code transmitted from the client system, which is associated with the GPU processing tasks specified by the service request, processes the block of GPU program code using the GPU devices of the logically bound GPU servers, and returns processing results to the client system.

Abstract: A graphics processing unit (GPU) service platform includes a control server, and a cluster of GPU servers each having one or more GPU devices. The control server receives a service request from a client system for GPU processing services, allocates multiple GPU servers within the cluster to handle GPU processing tasks specified by the service request by logically binding the allocated GPU servers, and designating one of the at least two GPU servers as a master server, and send connection information to the client system to enable the client system to connect to the master server. The master GPU server receives a block of GPU program code transmitted from the client system, which is associated with the GPU processing tasks specified by the service request, processes the block of GPU program code using the GPU devices of the logically bound GPU servers, and returns processing results to the client system.

Abstract: A graphics processing unit (GPU) service platform includes a control server, and a cluster of GPU servers each having one or more GPU devices. The control server receives a service request from a client system for GPU processing services, allocates multiple GPU servers nodes within the cluster to handle GPU processing tasks specified by the service request by logically binding the allocated GPU server nodes, and designating one of the at least two GPU servers as a master server, and send connection information to the client system to enable the client system to connect to the master server. The master GPU server node receives a block of GPU program code transmitted from the client system, which is associated with the GPU processing tasks specified by the service request, processes the block of GPU program code using the GPU devices of the logically bound GPU servers, and returns processing results to the client system.

Abstract: A method includes selecting a given hardware configuration for a given application workload based on aligning an application workload specification template with a first hardware configuration template in a repository comprising a plurality of hardware configuration templates, the application workload specification template being generated by parsing and interpreting hardware-agnostic service level objective expressions of an application request.

Abstract: A method comprises building a registry of available discovery adapters, obtaining discovery data from information technology infrastructure, generating a discovery plan comprising a list of discovery adapters by matching one or more information technology resource types identified in the discovery data to information technology resource types associated with respective ones of the available discovery adapters, providing the discovery data to the discovery adapters in the list to execute a discovery run comprising the discovery adapters analyzing the discovery data and storing information on information technology resources in a discovered resources repository, and dynamically adjusting allocation of the information technology resources based on the information stored in the discovered resources repository.