Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a local controller associated with a DMC domain, a weighted rank order of causal services associated with a service impairment; obtaining contextual information associated with the weighted rank order of causal services; identifying top causal services based on the weighted rank order of causal services; generating, using a causal symbolic engine, a ranked order of top root causes based on the top causal services and the contextual information; selecting remediation profiles associated with the top root causes; executing remediation profiles in ranked order of the top root causes; making a determination that the remediation profiles were successful; and in response to the determination: logging the service impairment and notifying a user associated with the service impairment.

Abstract: A communications system may include a user equipment (UE) device that communicates with a network. The UE may gather first contextual information about usage of the UE device, may receive second contextual information about usage of the network, and may receive third contextual information about usage of additional UE devices. The UE may run an application that requests wireless data transfer. The UE may train a learning model based on the contextual information. The UE may make a network connectivity decision based on the contextual information, the learning model, and a data sensitivity of the application. Once the connectivity decision is made, signals may be conveyed to implement the decision. Performing the connectivity decision on the UE instead of the network may allow the UE and the network to take full advantage of contextual learning in different use cases to optimize performance of both the UE and the network.

Abstract: Techniques are provided for secure data management in a network computing environment. A security management system receives data from a device which operates in a device network that is managed by the security management system. The security management system performs a data classification process to determine a data sensitivity level of the received data. The security management system determines a type of encryption to apply to the received data based on the determined data sensitivity level. The type of encryption is determined from a plurality of different types of encryption that are supported by a cloud system. The security management system sends the received data to the cloud system to at least one of store the data and perform secured data analytic processing of the data, in a format according to the determined type of encryption.

Abstract: Embodiments of systems and methods for enhanced drift remediation with causal methods and online model modification are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: detect drift in an Artificial Intelligence (AI) or Machine Learning (ML) model configured to make a prediction or a causal reasoning graphical or structural inference based upon input data, identify a root cause of the drift, and tag the input data with an indication of the root cause.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes normalizing, by a local controller associated with an DMC domain, task resource demand dimensions for each task associated with a scheduling job; summing the resource demand dimension for each task to generate resource demand vectors; classifying tasks based on priority; sorting tasks based on associated resource demand vectors; obtaining critical path, earliest start time, and latest start time associated with each task; sorting tasks based on critical path and earliest start time; and generating scheduling assignments based on the priority, capacity of devices in a final candidate list, resource demand vectors, earliest start time, and the critical path.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by an endpoint controller associated with a device, scheduling assignments and a provisioning command package from a local controller; in response to obtaining the scheduling assignments and the provisioning command package: making a determination that the device is available for the scheduling assignments; in response to the determination: modifying configuration of the device based on the scheduling assignments and generating configuration information; executing the provisioning commands included in the provisioning command package to instantiate tasks included in the scheduling assignments; instantiating monitoring services to collect monitoring information associated with the device and the tasks; and sending the monitoring information and configuration information to the local controller.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a global controller, a DMC environment management request from a user; and in response to obtaining the request: making a first determination that the DMC environment management request is a create request, and the create request is associated with provisioning an application in the DMC environment; in response to the first determination: generating scheduling packages associated with target domains of the DMC environment based on the create request; sending the scheduling packages to local controllers associated with the target domains; obtaining application information from the local controllers; and providing the application information to the user.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a local controller associated with a DMC domain, service level objective (SLO) metrics; applying the SLO metrics to a predictive anomaly detection transformer to perform anomaly detection; making a first determination that an anomaly is detected; in response to the first determination: attempting basic remediation to resolve the anomaly; making a second determination that the basic remediation is unsuccessful; in response to the second determination: making a third determination that the anomaly is associated with a silent failure; and in response to the third determination: performing service impairment isolation to obtain a collection of services correlated to the anomaly; and performing root cause analysis to identify causal services.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a global controller, a DMC environment management request from a user, whereas the DMC environment management request is associated with scheduling an application in the DMC environment; and in response to obtaining the request: identifying target domains for tasks associated with the application based on the request; selecting scheduling policies for each target domain; obtaining fingerprints of previously provisioned applications associated with the request; performing case based reasoning using the fingerprints to generate scheduling packages for the target domains; sending the scheduling packages to local controllers associated with the target domains; obtaining application information from the local controllers; and providing the application information to the user.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes decomposing, by a local controller associated with an DMC domain, a service dependency graph associated with a scheduling job; assigning normalized compute units and normalized network units to tasks included in the service dependency graph; generating a Q-table using the service dependency graph and reinforcement Q-learning; calculating a critical path and a max learned path using the Q-table and the service dependency graph; calculating the earliest start time and the latest start time for each task using the service dependency graph and the max learned path to obtain a plurality of earliest start time and latest start time pairs for each task; and generating scheduling assignments using the plurality of earliest start time and latest start time pairs for each task.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes identifying, by a local controller associated with an DMC domain, a domain scheduling event associated with a scheduling job; and in response to identifying the domain scheduling event: identifying a candidate list of devices of the DMC domain to schedule tasks associated with the scheduling job based on a location and service level objectives; refining the candidate list of devices based on device configuration requirements, device management requirements, and security requirements to generate a final candidate list; scheduling tasks to devices using the final candidate list; generating scheduling assignments and provisioning command packages based on the scheduled tasks; providing the scheduling assignments and the provision command packages to the devices; and updating a graph based on the scheduling assignments.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a local controller associated with a DMC domain, a weighted rank order of causal services associated with a service impairment; obtaining contextual information associated with the weighted rank order of causal services; identifying top causal services based on the weighted rank order of causal services; generating, using a causal symbolic engine, a ranked order of top root causes based on the top causal services and the contextual information; selecting remediation profiles associated with the top root causes; executing remediation profiles in ranked order of the top root causes; making a determination that the remediation profiles were successful; and in response to the determination: logging the service impairment and notifying a user associated with the service impairment.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes identifying, by a global controller, a load management prediction event; in response to identifying the load management prediction event: obtaining offline data from local controllers associated with a plurality of DMC domains of the DMC environment; generating load management predictions using the offline data; adjusting local controller scheduling policies based on the load management predictions; adjusting global controller scheduling policies based on the load management predictions; making a first determination that the load management predictions require infrastructure adjustments; and in response to the first determination: initiating infrastructure capacity adjustment based on the load management predictions.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a global controller, an DMC environment management request from a user, and the DMC environment management request is associated with scheduling an application in the DMC environment; and in response to obtaining the request: providing acknowledgment that the request is received to the user; obtaining an order identifier and a manifest from the user; providing a correlation identifier associated with the request to the user; parsing the manifest to extract application deployment information; scheduling the application to target domains of the DMC based on the application deployment information; and providing the user with the correlation identifier, a success indicator, and application information.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by an endpoint controller associated with a device, an initial resource buffer from a local controller; in response to obtaining the initial resource buffer: maintaining the initial resource buffer during task provision for the device; obtaining device metrics based on performance of tasks on the device; making a determination that a resource buffer change event is identified; and in response to the determination: updating the initial resource buffer based on the resource buffer change event.

Abstract: Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a local controller associated with a DMC domain, a set of correlated services associated with an anomaly; obtaining a service dependency graph associated with the set of correlated services; generating a causal variance for each service using the correlated services and the service dependency graph; generating a weighted rank order of causal services based on the causal variance associated with each service, and the weighted rank order of causal services includes a portion of the services associated with an application associated with the anomaly; and performing remediation based on the weighted rank order of the causal services.

Abstract: One example method includes correlating trust scoring with authentication levels. Trust scores are protected in a computing system such that devices can be validated. Authentication levels are based on the verified trust scores.

Abstract: A data communication network includes a network node and a processor. The network node includes an optical link and a reflectometry analyzer to provide a plurality of reflectometry results, each reflectometry result providing a characterization of physical and operational properties of the optical link at the time of the reflectometry result. The processor receives a first set of the reflectometry results, and calculates a federated fingerprint of the physical and operational properties of the optical link based on the plurality of fingerprints in first set of the reflectometry results. The federated fingerprint is a combination of fingerprints from every section of the optical link. The processor receives a second set of the reflectometry results, and compares each of a plurality of fingerprints in the second set of reflectometry results with a respective corresponding fingerprint in the federated fingerprint.

Abstract: Techniques described herein relate to a method for using pipeline data for deploying workflows. The method may include determining that a pipeline testing trigger occurred for a workflow; decomposing a pipeline testing manifest of the workflow; generating a testing execution plan using the decomposed workflow; adding instrumentation to the testing execution plan; determining that the instrumented testing execution plan is valid; deploying computing devices within a CI/CD pipeline ecosystem for performing the instrumented testing execution plan; capturing deployment logs; initiating telemetry capture; executing pipeline testing of the workflow based on the instrumented testing execution plan; generating a pipeline deployment information set based on the pipeline testing; and providing the pipeline deployment information set to an orchestrator of a production device ecosystem.