Abstract: Methods, apparatus, and processor-readable storage media for request processing techniques for container-based architectures are provided herein. An example method includes obtaining one or more threshold values for each of a plurality of nodes in at least one cluster of a container-based computing environment, wherein the one or more threshold values are configured for one or more corresponding resource types; obtaining resource consumption data for each of the plurality of nodes; determining, based at least in part on the one or more obtained threshold values and the obtained resource consumption data, a set of available nodes from among the plurality of nodes for processing incoming requests to the container-based computing environment; and initiating a routing of the incoming requests to one or more nodes in the set of available nodes.

Abstract: Methods, apparatus, and processor-readable storage media for cache management for web application components are provided herein. An example computer-implemented method includes maintaining information corresponding to a set of resources for a first version of a first component associated with a web application, wherein at least some of the resources in the set of resources are stored in a first portion of a browser cache, of a client device, corresponding to the first component; detecting one or more changes to the set of resources in response to a second version of the first component being deployed; and sending a notification of the changes to a client device, where the client device updates, based at least in part on the one or more changes, the first portion of the browser cache and a second portion of the browser cache corresponding to a second component that is dependent on the first component.

Abstract: Methods, apparatus, and processor-readable storage media for automated processing of dynamic requests are provided herein. An example computer-implemented method includes: obtaining a dynamic request associated with one or more items; determining information for processing the dynamic request, wherein the information comprises at least one action corresponding to the dynamic request and a set of applications impacted by the at least one action; and sending a notification of the at least one action to each respective application in the set of applications, wherein at least one application in the set of applications causes one or more processes to be executed, based at least in part on the notification, to process the dynamic request.

Abstract: Automated project management techniques are disclosed using conversational processing to generate project-based transition states in an information processing system. For example, a method generates a data structure representing a lifecycle of at least one goal of a given project, wherein the data structure comprises one or more transition states associated with the at least one goal, and wherein the transition states are automatically tracked against one or more time instances. The method then facilitates management of the given project based on the generated data structure.

Abstract: In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may: receive multiple information respectively associated with products; store, in a first node of a recursive data structure at a first level of the data structure, pointers to first nodes, at a second level of the data structure, respectively associated with the products; respectively store the multiple information in the first nodes; for each product of the products: respectively store one or more attributes and one or more selections associated with the product in nodes of the data structure at a third level of the data structure; and for each product of the products: for each selection of the one or more selections: respectively store one or more attributes associated with the selection in one or more nodes of the data structure at a fourth level of the data structure.

Abstract: An apparatus comprises a processing device configured to receive, from a given client at a single sign-on manager coupled to a database cluster comprising a plurality of databases, an access request comprising an identifier of a given one of the plurality of databases in the database cluster and single sign-on credentials for the given client to access the database cluster. The processing device is also configured to authenticate, at the single sign-on manager, the single sign-on credentials in the access request and, responsive to authenticating the single sign-on credentials in the access request, to establish a connection between the given client and the given one of the plurality of databases in the database cluster utilizing a session established between the single sign-on manager and the given one of the plurality of databases in the database cluster.

Abstract: Infrastructure resource capacity management techniques in an information processing system are disclosed. For example, a method comprises the following steps. Data associated with at least one resource of one or more computing platforms is obtained. Each of the one or more computing platforms is deployed at one or more locations associated with one or more entities. One or more resource expansion trigger threshold values are computed based on at least a portion of the obtained data for each of the one or more computing platforms. A resource expansion operation is initiated for the one or more computing platforms based on the one or more resource expansion trigger threshold values.

Abstract: Automated item management techniques are disclosed. For example, for a given item type obtainable from one or more sources and storable as inventory at one of a first site or a second site and based on a given demand forecast, a method classifies the item type based on historical data associated with obtaining the item type from the one or more sources. The method computes a risk factor value for the item type based on one or more risk factors. The method then computes, for the given demand forecast and based on the classifying and the risk factor value, a first amount of the item type to store as inventory at the first site and a second amount of the item type to store as inventory at the second site.

Abstract: Techniques are disclosed for managing workloads in data processing systems. For example, a method computes a set of sustainability modes for a computing environment, wherein each sustainability mode comprises respective configuration boundaries defining a different amount of resources that are available for executing one or more workloads in the computing environment.

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: An example methodology implementing the disclosed techniques includes, by a computing device, receiving data about a probable overstock product at a retail partner in a sales region from another computing device, the data including a finished goods assembly (FGA) stock keeping unit (SKU) that identifies the probable overstock product and a corresponding quantity of the probable overstock product. The method also includes, by the computing device, determining a total quantity of the probable overstock product in the sales region, wherein the total quantity includes the quantity of the probable overstock product at the retail partner, and computing a corrected FGA demand of the probable overstock product in the sales region based on the total quantity of the probable overstock product in the sales region. The method further includes, by the computing device, the corrected FGA demand of the probable overstock product in the sales region.

Abstract: Methods, apparatus, and processor-readable storage media for securely executing microservices are described herein. An example computer-implemented method includes: generating a session identifier for a request associated with executing one or more microservices in a microservice architecture; identifying an execution plan for the request based at least in part on a plan identifier specified for a first one of the microservices, wherein the execution plan indicates a specified order for executing the microservices for the request; maintaining a set of chained data structures to track an execution order of the microservices based on the session identifier; detecting, based at least in part on the set of chained data structures, that the execution order of the identified execution plan does not match the specified order for at least a given one of the of the one or more microservices; and in response to the detecting, preventing at least the given microservice from being executed.

Abstract: An apparatus comprises a processing device configured to monitor logs of a database system, to analyze the logs to collect ongoing transaction information for one or more applications utilizing the database system, and to maintain the ongoing transaction information for each of the one or more applications in a corresponding one of a set of one or more queues of a data store external to the database system. The processing device is also configured to detect one or more designated events affecting operation of the database system and, responsive to detecting at least one of the one or more designated events, to process one or more unsaved transactions of the database system utilizing the information stored in the set of one or more queues of the data store external to the database system.

Abstract: Artificial intelligence-based data security management techniques are disclosed. For example, a method comprises the following steps. A dataset is downloaded from a data source. The method detects whether or not the dataset contains at least a subset of data of a given security-based data type, wherein the detection is performed in accordance with an artificial intelligence model. A machine-computed security level classification is generated for the dataset by automatically selecting one of a plurality of security level classifications based on the detection. The machine-computed security level classification is compared with a user-generated security level classification manually selected from a plurality of security level classifications. One or more actions are caused to be taken based on the comparison.

Abstract: Methods, apparatus, and processor-readable storage media for managing transaction consistency in distributed databases are provided herein. An example computer-implemented method includes determining a first one of a plurality of consistency levels to be applied for a transaction in a distributed database comprising a set of database nodes; and in response to determining that the first one of the plurality of consistency levels is to be applied: configuring at least two connections with the distributed database; releasing a first one of the at least two connections in response to detecting that the transaction completed on a first node in the set of database nodes; and in response to one or more of the other database nodes in the set of database nodes being updated to reflect changes on the first database node resulting from the transaction, releasing at least a second one of the at least two connections.

Abstract: Automated item management techniques are disclosed. For example, for an item type obtainable from one or more sources and storable as inventory at one of a first site or a second site and based on a first demand forecast, a method computes a discrepancy value for the item type for each of the first site and the second site based on a second demand forecast, wherein the second demand forecast is computed more recently in time than the first demand forecast. The method generates, based on the discrepancy value computed for each of the first site and the second site, a recommendation operation to mitigate the discrepancy value at one or more of the first site and the second site. The method causes the recommendation operation to be executed in order to mitigate the discrepancy value at one or more of the first site and the second site.

Abstract: An apparatus comprises at least one processing device configured to register two or more source services, wherein registering a given source service comprises executing the given source service to identify inputs and outputs of the given source service, and to select a set of outputs from the registered source services for generating at least a portion of a user interface of an application in an application client, the selected set of outputs comprising at least first and second output from first and second ones of the registered source services. The processing device is also configured to determine an orchestration of the registered source services for obtaining the selected set of outputs, to generate a custom microservice configured to execute the registered source services in accordance with the determined orchestration, and to deploy the custom microservice in an auto-scalable computing framework accessible to the application client of the application.

Abstract: Techniques are disclosed for managing workloads in data processing systems. For example, a method computes a set of sustainability modes for a computing environment, wherein each sustainability mode comprises respective configuration boundaries defining a different amount of resources that are available for executing one or more workloads in the computing environment.

Abstract: Automated item management techniques are disclosed. For example, for a given item type obtainable from one or more sources and storable as inventory at one of a first site or a second site and based on a given demand forecast, a method classifies the item type based on historical data associated with obtaining the item type from the one or more sources. The method computes a risk factor value for the item type based on one or more risk factors. The method then computes, for the given demand forecast and based on the classifying and the risk factor value, a first amount of the item type to store as inventory at the first site and a second amount of the item type to store as inventory at the second site.

Abstract: An example methodology implementing the disclosed techniques includes receiving an online meeting Uniform Resource Locator (URL) for an online meeting that is being scheduled. The method also includes determining whether the online meeting is a secure online meeting. The method also includes, responsive to a determination that the online meeting is a secure online meeting, generating a personalized online meeting URL for each meeting participant that is invited to the secure online meeting, the personalized online meeting URL being based on the online meeting URL. The method further includes encrypting the generated personalized online meeting URLs and sending to each meeting participant that is invited an invitation to the secure online meeting. The invitation sent to each invited meeting participant includes an encrypted personalized online meeting URL that is generated for that invited meeting participant.