Abstract: A method for executing a service sequence, comprising: selecting, by a service orchestrator, a service that is part of the service sequence; detecting, by the service orchestrator, whether a load metric exceeds a threshold; when the load metric does not exceed the threshold, executing the service to obtain a real response, and continuing execution of the service sequence based on the real response; when the load metric exceeds the threshold, identifying, by the service orchestrator, a deviation behavior identifier that is associated with the service, obtaining an estimated response for the service based on the deviation behavior identifier, and continuing execution of the service sequence based on the estimated response.

Abstract: Techniques for automated supply planning management are disclosed. For example, a method obtains a first data set representing historical data associated with a non-customizable system, a second data set representing historical data associated with a customizable base system, and a third data set representing historical data associated with components used to customize the customizable base system. The method pre-processes at least portions of the first data set, the second data set and the third data set, and then performs forecasting processes respectively on the pre-processed portions of the first data set, the second data set and the third data set. Results of the forecasting processes are correlated and the forecasting results associated with the third data set are modified based on variations in one or more of the forecasting results associated with the first data and the forecasting results associated with the second data.

Abstract: Techniques are disclosed for data management techniques using artificial intelligence-based cache distribution within a distributed information processing system. For example, a cohesive and distributed machine learning approach between the same or similar customer data centers and products predict optimal data needed at each customer data center, and intelligently synchronize or federate the data between customer data centers and a core data center using a combination of customized caching and push techniques according to one or more customer behavior patterns.

Abstract: Intelligent hardware delivery for on-premises hardware as a service is described herein. A method as described herein can include classifying data storage systems into a system group based on a common characteristic of the respective systems; generating predicted storage capacity data representative of respective predicted available storage capacities of the respective systems over a first time period based on storage usage data obtained from the respective systems over a second, earlier time period; receiving, from a manufacturing system, manufacturing schedule data representative of a manufacturing schedule for new storage devices; receiving, from the respective systems, maintenance schedule data representative of respective defined maintenance windows for the respective systems; and generating storage expansion schedule data representative of a schedule for addition of the new storage devices to the respective systems, e.g.

Abstract: In a software product installed in a given computing environment, wherein the software product when installed is configured to operate in accordance with a licensing model having a first licensing functionality, a method receives an instruction to modify the licensing model to enable the software product to operate in accordance with a second licensing functionality. The method then automatically modifies the licensing model to enable the software product to operate in accordance with the second licensing functionality.

Abstract: Techniques for managing activation of functionalities in an information processing system are provided. For example, a method generates, at a first node in a distributed ledger network, at least one data object comprising data associated with a system and one or more unactivated functionalities of the system, and the data object further comprises one or more parameter fields configured for one or more other nodes in the distributed ledger network to subsequently insert data therein. The method obtains the at least one data object after data is inserted in the one or more parameter fields by the one or more other nodes. The method sends the at least one object to an additional node in the distributed ledger network for use in activating the one or more unactivated functionalities of the system.

Abstract: Data protection techniques for data structures in an information processing system are provided. For example, a method comprises the following steps. A request is received to create a data structure with a given data structure name and one or more given parameter names. A pair of data structures is generated in response to the request. Each of the pair of data structures is assigned a different randomly-generated data structure name derived from the given data structure name in the request, and the one or more given parameter names are assigned different one or more randomly-generated parameter names in each of the pair of data structures.

Abstract: A method for executing a service sequence, comprising: selecting, by a service orchestrator, a service that is part of the service sequence; detecting, by the service orchestrator, whether a load metric exceeds a threshold; when the load metric does not exceed the threshold, executing the service to obtain a real response, and continuing execution of the service sequence based on the real response; when the load metric exceeds the threshold, identifying, by the service orchestrator, a deviation behavior identifier that is associated with the service, obtaining an estimated response for the service based on the deviation behavior identifier, and continuing execution of the service sequence based on the estimated response.

Abstract: As an example, a server may receive and/or retrieve news items and process the news items using natural language processing to identify news related to entities (e.g., people, locations, and organizations) extracted from an enterprise resource planning system. A term frequency-inverse document frequency algorithm may be used to identify critical news items that may impact one or more supply chains associated with at least one product that is to be manufactured. A long short-term memory artificial recurrent neural network may be used to determine a confidence score for each critical news item. The confidence scores of the critical news items may be used to adjust replenishment planning and a master build plan that includes a plan to build the at least one product. In this way, news items may be used to automatically (e.g., without human interaction) adjust the master build plan.

Abstract: A method comprising: receiving, by a first email client, an input specifying one or more permissions that are associated with an email message, any of the permissions being associated with a corresponding action, any of the permissions indicating whether a recipient of the email message is permitted to perform the permission's corresponding action on the email message; and transmitting, by the first email client, the email message and the one or more permissions to an email server.

Abstract: In product testing, a script prioritization tool (102) is used to intelligently prioritize the execution sequence of test scripts. This tool creates a repository of test outputs from the executions of test scripts and analyzes the outputs to train and deploy a machine learning, ML, model that defines the priority of the scripts that may need to be executed and the scripts whose execution may be skipped without affecting the quality of testing. Scripts that are more likely to fail and/or are time consuming to execute are prioritized, while other scripts may be skipped. The ML model ranks the scripts based on the average execution time of the script, a count of the execution failures of the script, a count of the number of execution retries for the script, and the most recent failure time of the script. The scripts can be executed based on their rankings for efficiency and time-saving.

Abstract: A machine learning (ML) module that intelligently stores data in an online backup system based on the criticality of a business process. For a critical business process, all pre- and post-operation data are stored in the backup. For a non-critical business process, the ML module does not collect all data, but collects that data which deviates from a pre-defined target data pattern. Such deviating data is stored for a given time period and then discarded if no recovery request is received for it within the time period. Subsequent receipt of similar type of data may trigger the ML module to not store it. Thus, online backup storage is maintained at a manageable size without any loss of data for a critical business process. During data recovery, data is retrieved from the online backup with referential integrity to restore the original database without downtime to give a truly online recovery experience.

Abstract: A system, method, and computer-readable medium are disclosed for performing a digital fulfillment product onboarding operation. The digital fulfillment product onboarding operation includes: receiving an order for a digital asset; deriving digital fulfillment attributes associated with the digital asset; identifying a digital asset fulfillment process flow from a plurality of digital asset fulfillment process flows, the identifying being based upon the order for the digital asset and the attributes associated with the digital asset; plotting the digital asset fulfillment process flow; and, executing the digital asset fulfillment process flow.

Abstract: Methods, information handling systems and computer readable media are disclosed for formulating a proposed action involving a current repair process for a failed product in a manufacturing process. According to one embodiment, a method includes receiving identification of a current repair process and associating a first set of parameter values with the current repair process. The method further includes determining a likelihood of shipment delay resulting from the current repair process, where the determining includes applying a first machine learning model to the first set of parameter values. Based on the likelihood of shipment delay, the method further includes formulating a proposed action, including at least one of waiting for completion of the current repair process, replacing the failed product with an alternative product undergoing the manufacturing process, or initiating production of a new product to replace the failed product.

Abstract: Described embodiments provide real time load balancing and calibration processes. An aspect includes receiving a request to execute a service, assessing capacity of a target server to execute the service, and setting a threshold for a number of services to be executed on the target server. A further aspect includes assigning a unique identifier of the service to the target server, and based on the threshold and an assessment of a capability of other servers, assigning the service to the server.

Abstract: An apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to receive, at a first microservice, context information for a second microservice, the context information characterizing a current state of execution of one or more tasks by the second microservice. The at least one processing device is also configured to detect, by the first microservice, failure of the second microservice. The at least one processing device is further configured to re-initiate, by the first microservice, the second microservice utilizing the received context information for the second microservice. Re-initiating the second microservice utilizing the received context information for the second microservice enables the re-initiated second microservice to continue execution of the one or more tasks from the current state.

Abstract: Techniques for safe guarding of functionalities of a computer program in an information processing system are disclosed. For example, a method comprises the following steps. A plurality of criticality levels is obtained for a plurality of functionalities of a computer program configured to execute on a computer system, wherein the computer system comprises one or more resources usable to execute the computer program. Data is collected for the plurality of functionalities of the computer program. Usage of the one or more resources for executing the plurality of functionalities of the computer program for a given set of data is predicted. Execution of a portion of the plurality of functionalities of the computer program is ensured based on the predicted usage of the one or more resources and the criticality level for each of the plurality of functionalities.

Abstract: A method comprises dividing a plurality of operations of a microservice between a plurality of databases, and synchronizing data corresponding to the plurality of operations between the plurality of databases. The microservice is a create, read, update, delete (CRUD) microservice, and the plurality of operations comprise creating, reading, updating and deleting the data.

Abstract: A system for testing electrical components comprising a supplier test system operating on a first processor and configured to generate test data for a component and to store the component test data on a block chain. A board level test system operating on a second processor and configured to generate test data for a board and to store the board test data on the block chain. A test tracking system configured to request a first key to access to the component test data and a second key to access the board test data and to store the first key and the second key.

Abstract: Techniques for managing activation of software, e.g., an operating system, in an information processing system are provided. For example, a method comprises the following steps. A data object is obtained for a system having an un-activated computer program stored thereon. The data object comprises information about the system encrypted by a private value. The method then causes the data object to be securely stored on a distributed ledger for subsequent access during a process to activate the computer program.