Abstract: A computer-implemented method, according to one approach, includes: receiving observability data produced by a first portion of a distributed application. The observability data is evaluated and in response to determining that the observability data indicates the first portion of the distributed application is degraded, the amount of the observability data produced by the first portion of the distributed application is increased. Additionally, the amount of observability data produced by other portions of the distributed application that are correlated with the first portion of the distributed application is also increased.

Abstract: Systems and methods include receipt of search terms, determination of an embedding for each of the search terms, generation of a composite embedding based on the determined embeddings, determination of similarities between the composite embedding and second composite embeddings associated with each of a plurality of hierarchical group codes, determination of a hierarchical group code of the plurality of hierarchical group codes based on the determined similarities, and generation of search results based on the search terms and the hierarchical group code.

Abstract: Techniques are provided for dynamic alert suppression policy management. In one embodiment, the techniques involve receiving an event stream, wherein the event stream includes metric values comprising at least one of: log anomaly data and metric anomaly data, determining an anomalous event based on the event stream, determining a persistent region of the anomalous event, determining a quantum representation of the persistent region, determining X-Y values of the persistent region based on the quantum representation, and generating a policy based on a set of the X-Y values.

Abstract: A computer-implemented method, according to one approach, includes: receiving observability data produced by a first portion of a distributed application. The observability data is evaluated and in response to determining that the observability data indicates the first portion of the distributed application is degraded, the amount of the observability data produced by the first portion of the distributed application is increased. Additionally, the amount of observability data produced by other portions of the distributed application that are correlated with the first portion of the distributed application is also increased.

Abstract: Various systems and methods are presented herein regarding automatically identifying one or more entities to for assignment to resolving an issue and/or removing an entity from a group of entities assigned to resolve the issue. The issue can relate to an information technology/computer-based environment. The entities can be subject matter experts, having expertise identified to be related to the issue. A summary of the issue and activity relating to resolving the issue can be automatically generated and supplied to an entity who is being onboarded after the project is underway to resolve the issue. By implementing automated assignment/removal of entities, efficiency of the resolution is improved.

Abstract: Techniques are disclosed to establish trust in a cluster of edge devices. An edge device cloud service can associate a first cloud-computing edge device with a fleet of cloud-computing edge devices and provision the first cloud-computing edge device with a master encryption key. The edge device cloud service can associate a second cloud-computing edge device with the fleet and provision the second cloud-computing edge device with the master encryption key and the first public encryption key. The first cloud-computing edge device can receive from the second cloud-computing edge device encrypted message data comprising the second public encryption key. The first cloud-computing edge device can decrypt the encrypted message data using the master encryption key stored in the first key store and update the first key store with the second public encryption key.

Abstract: The present disclosure relates to a framework that provides execution of serverless functions in a cloud environment based on occurrence of events/notifications from services in an entirely different cloud environment. A target agent obtains a notification from a source agent, where the target agent is deployed in a target cloud environment and the source agent is deployed in a source cloud environment that is different than the target cloud environment. The target agent determines a function that is to be invoked based on the notification. Upon successfully verifying whether the target agent is permitted to invoke the function that is deployed in a target customer tenancy of the target cloud environment, the target agent invokes the function in the target customer tenancy of the target cloud environment.

Abstract: Techniques are provided for dynamic alert suppression policy management. In one embodiment, the techniques involve receiving an event stream, wherein the event stream includes metric values comprising at least one of: log anomaly data and metric anomaly data, determining an anomalous event based on the event stream, determining a persistent region of the anomalous event, determining a quantum representation of the persistent region, determining X-Y values of the persistent region based on the quantum representation, and generating a policy based on a set of the X-Y values.

Abstract: A method, system, and non-transitory processor-readable medium for optimizing lifecycle rule computer processing including: selecting a set of lifecycle rules to apply to interactions, finding a set of interactions satisfying criteria associated with the lifecycle rule; calculating an associated rule occupancy score based on the number of executed interactions associated with the rule, and executing the rule based on the rule occupancy score associated with the rule.

Abstract: Embodiments receive a plurality of faults for at least one microservice in a cloud native based application within a fault set selection server; inject the faults into at least one microservice in the cloud native based application within the fault set selection server; obtain a system state representation for each of the injected faults in the at least one microservice in the cloud native based application using an unsupervised clustering algorithm; derive a fault subset based on the system state representation for each the faults in the at least one microservice in the cloud native based application; and inject the derived fault subset into the at least one microservice in the cloud native based application and logging behavior of the at least one microservice in the cloud native based application with the injected derived fault subset.

Abstract: Systems and methods are provided for creating and running an instance of a dynamic access control system (DACS). Trust providers may be defined in a trust broker of the DACS such that trust information associated with the trust providers can be used to create a custom data structure. Resources and resource groups may be defined in the DACS. Policies may be configured or coded in the DACS to map the custom data structure to recourses or resources groups. Additionally, policies may be configured or coded in the DACS to route the data structure and request to network segments or shared with other parties.

Abstract: A method, system, and non-transitory processor-readable medium for optimizing lifecycle rule computer processing including: selecting a set of lifecycle rules to apply to interactions, finding a set of interactions satisfying criteria associated with the lifecycle rule; calculating an associated rule occupancy score based on the number of executed interactions associated with the rule, and executing the rule based on the rule occupancy score associated with the rule.

Abstract: Techniques discussed herein relate to implementing a distributed computing cluster (the “cluster”) including a plurality of edge devices (e.g., devices individually configured to selectively execute within an isolated computing environment). Each of the edge devices of the cluster may be configured with a respective control plane computing component. A subset of the edge devices may be selected to operate in a distributed control plane of the computing cluster. Any suitable combination of the subset selected to operate in the distributed control plane can instruct remaining edge devices of the distributed computing cluster to disable at least a portion of their respective control planes. Using these techniques, the cluster's distributed control plane can be configured and modified to scale as the cluster grows. The edge devices of the control plane may selectively connect to a centralized cloud, alleviating the remaining edge devices from needless and costly connections.

Abstract: A new and innovative high-pressure priming valve is provided for use in high-pressure fluid systems that require a high level of fluid purity. The priming valve includes at least three ports, some of which are angled. The priming valve also includes a needle that variably blocks and unblocks a pathway to one of the ports between normal operation and a priming operation, respectively. The priming valve includes a sealing insert positioned below a stack of washers that maintain the needle's alignment in response to high fluid pressures exerted on the needle. The sealing insert helps prevent fluid from contacting the stack of washers, which helps prevent biological growth within the valve. The angled ports help facilitate priming valve drainage to further help prevent biological growth. By helping prevent biological growth, the sealing insert helps prevent fluid contamination and enables the priming valve to be utilized for high-purity fluid applications.

Abstract: A method, system, and computer program product that is configured to: train at least one model based on a corpus of historical data comprising annotated historical tickets; extract a textual sequence of a historical ticket based on the at least one trained model; determine a sentiment of the textual sequence of the historical ticket; and generate mitigation guidance to mitigate an issue in a current ticket based on the textual sequence of the historical ticket and the determined sentiment of the textual sequence of the historical ticket.

Abstract: Methods, systems, and computer program products for mining multi-party collaboration platforms to create triaging trees and playbooks are provided herein. A computer-implemented method includes obtaining, from a multi-user platform, conversations related to at least one technical issue; generating a plurality of triaging trees by analyzing the conversations, wherein each of the triaging trees stores information corresponding to temporal sequences of steps related to diagnosing and resolving said at least one technical issue; and deriving a playbook for resolving said at least one technical issue at least in part by combining two or more of the plurality of triaging trees.

Abstract: Systems and methods are provided for creating and running an instance of a dynamic access control system (DACS). Trust providers may be defined in a trust broker of the DACS such that trust information associated with the trust providers can be used to create a custom data structure. Resources and resource groups may be defined in the DACS. Policies may be configured or coded in the DACS to map the custom data structure to recourses or resources groups. Additionally, policies may be configured or coded in the DACS to route the data structure and request to network segments or shared with other parties.

Abstract: A computerized-method for gauging agent's self-assessment effectiveness, is provided herein. The computerized-method includes for each interaction (i) operating a Self-assessment Consolidation module to calculate a confidence-interval for each data-point of one or more preconfigured data-points, and (ii) operating a Self-assessment Divergence Determinant (SDD) module. The operating of the SDD includes: retrieving one or more data-points of the interaction; for each data-point retrieving the confidence interval; setting a divergence-indicator as zero, when the data point is within the confidence-interval; setting the divergence-indicator as a subtraction of the data point from the calculated lower-bound, when the data-point is lower than the lower-bound of the confidence-interval; and setting the divergence-indicator as a subtraction of the calculated upper-bound from the data-point, when the data-point is greater than the upper-bound of the confidence-interval.

Abstract: Deriving augmented knowledge defining a knowledge base by extracting entities from a plurality of heterogeneous data sources; and augmenting the extracted entities; and utilizing an augmented entity to enhance a user activity.

Abstract: Identifying an log anomaly resolution by generating a knowledge base linking each of a plurality of incidents with historical anomalous log lines, calculating a resolution specificity score for each knowledge base record, identifying a run-time anomalous log line using the knowledge base, predicting a category for the run-time anomalous log line, identifying resolutions according to the category, ranking the resolutions according to the resolution specificity scores, and recommending a resolution according to the ranking.