Abstract: One or more processors are configured to execute instructions that cause a UE to perform operations. The operations include offloading a task to a first network node. The operations include determining a computation requirement forecast, wherein the computation requirement forecast indicates a computation requirement of the task at an upcoming time. The operations include transmitting the computation requirement forecast to the first network node. The operations include performing a handover procedure from the first network node to a second network node.

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: Data characterization techniques in an information processing system environment are disclosed. In one example, at least one processing device is configured to obtain data associated with execution of at least one of a plurality of applications in an information processing system. The processing device extracts features from the obtained data, and then selects a reduced subset of features from the extracted features for use in a machine learning classification process configured to determine an intent of the obtained 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: 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. The method further comprises obtaining results representing the infrastructure in the second state, responsive to applying the at least one dataset to the virtual representation, wherein at least a portion of the results are indicative of an issue with the infrastructure, and then initiating one or more debugging actions with respect to the infrastructure to address the issue.

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, and applying at least one dataset to the virtual representation to artificially advance the virtual representation to represent the infrastructure in a second state. The method further comprises obtaining results representing the infrastructure in the second state, responsive to applying the at least one dataset to the virtual representation, wherein at least a portion of the results are indicative of a cybersecurity issue on the infrastructure, and then initiating one or more of a remedial action and a preventative action with respect to the infrastructure to address the cybersecurity issue.

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 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: 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 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: Presented herein are embodiments to implement a temporal queueing system with class-based fair queuing and dynamic resource allocation based on a novel look-ahead capability to manage various models and workloads for utilization/efficiency improvements. Embodiments may be implemented to allocate accelerator resources based on platform-defined timeslots, and therefore significantly increase the ability of workloads to access hardware accelerator resources. Training and inference may be supported with flexible preemption and the ability to support run-to-completion for training tasks while still supporting non-run-to-completion for inference tasks. Embodiments may be implemented by an edge software operation platform through virtual accelerators to allow emulation of different types of hardware accelerators and to map to the hardware accelerators with hardware-specific procedures managed by an edge orchestrator and an edge endpoint.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Unlike cloud systems and other frameworks that are able to use closed-form solutions based on Poisson processes or other tractable Gaussian-based probability distributions, edge systems present complex waveforms, pareto/alpha-stable distributions, and long-range dependence. Based on elaborately designed embodiments that recognize the complexities of edge data, one can estimate scaling and multi-fractal dimensionality to determine predictive models.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. A time period in which an application or task is operating based on initial demand resource load values that are provided by a customer may be inaccurate, which may expose sub-standard execution. Embodiments herein seek to significantly mitigate the potential of sub-standard execution. Embodiments collect a repository of resource demand load usage data over a time period that can be used to accurately determine the statistical moments of uncertain resource demand load. In one or more embodiments, a repository of hypervector and/or hyperspace representations may be generated and used to help with resource demand load estimation.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Edge resource demand load scheduling systems and methods are disclosed that can ensure that edge systems operate smoothly and efficiently while balancing multiple scheduling objectives. Scheduling techniques disclosed herein may utilize heuristic rules for candidate edge system selection (e.g., utilizing ARMA/ARIMA averages and/or service level objectives) and modified best fit decreasing (mBFD) assignment/allocation techniques.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Embodiments herein provide edge resource demand load estimation systems and methods that inform scheduling and associated edge orchestration to ensure that edge system resource capacity is appropriately utilized. Efficient utilization allows an increased number of applications to be deployed at a reduced level of reserved resources. Also presented are embodiments of assurance mechanisms for monitoring edge resource demand load characterizations. In one or more embodiments, when an estimate or estimates are deemed to not be valid (e.g., having experienced stationary drift), updated estimates may be obtained.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Unlike cloud systems and other frameworks that are able to use closed-form solutions based on Poisson processes or other tractable Gaussian-based probability distributions, edge systems present complex waveforms, pareto/alpha-stable distributions, and long-range dependence. Based on elaborately designed embodiments that recognize the complexities of edge data, one can estimate scaling and multi-fractal dimensionality to determine predictive models.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. A time period in which an application or task is operating based on initial demand resource load values that are provided by a customer may be inaccurate, which may expose sub-standard execution. Embodiments herein seek to significantly mitigate the potential of sub-standard execution. Embodiments collect a repository of resource demand load usage data over a time period that can be used to accurately determine the statistical moments of uncertain resource demand load. In one or more embodiments, a repository of hypervector and/or hyperspace representations may be generated and used to help with resource demand load estimation.

Abstract: Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. Embodiments herein provide edge resource demand load estimation systems and methods that inform scheduling and associated edge orchestration to ensure that edge system resource capacity is appropriately utilized. Efficient utilization allows an increased number of applications to be deployed at a reduced level of reserved resources. Also presented are embodiments of assurance mechanisms for monitoring edge resource demand load characterizations. In one or more embodiments, when an estimate or estimates are deemed to not be valid (e.g., having experienced stationary drift), updated estimates may be obtained.

Abstract: Data characterization techniques in an information processing system environment are disclosed. In one example, at least one processing device is configured to obtain data associated with execution of at least one of a plurality of applications in an information processing system. The processing device extracts features from the obtained data, and then selects a reduced subset of features from the extracted features for use in a machine learning classification process configured to determine an intent of the obtained data.