Calculus for trust in edge computing and named function networks

    公开(公告)号:US11888858B2

    公开(公告)日:2024-01-30

    申请号:US17064218

    申请日:2020-10-06

    CPC classification number: H04L63/123 G06F8/60 H04L45/72 H04L63/08 H04L67/568

    Abstract: Various aspects of methods, systems, and use cases for verification and attestation of operations in an edge computing environment are described, based on use of a trust calculus and established definitions of trustworthiness properties. In an example, an edge computing verification node is configured to: obtain a trust representation, corresponding to an edge computing feature, that is defined with a trust calculus and provided in a data definition language; receive, from an edge computing node, compute results and attestation evidence from the edge computing feature; attempt validation of the attestation evidence based on attestation properties defined by the trust representation; and communicate an indication of trustworthiness for the compute results, based on the validation of the attestation evidence. In further examples, the trust representation and validation is used in a named function network (NFN), for dynamic composition and execution of a function.

    SYSTEMS, METHODS, ARTICLES OF MANUFACTURE, AND APPARATUS FOR END-TO-END HARDWARE TRACING IN AN EDGE NETWORK

    公开(公告)号:US20220121556A1

    公开(公告)日:2022-04-21

    申请号:US17561516

    申请日:2021-12-23

    Abstract: Systems, methods, articles of manufacture, and apparatus for end-to-end hardware tracing in an Edge network are disclosed. An example compute device includes at least one memory, instructions in the compute device, and processing circuitry to execute the instructions to, in response to receiving detecting an object having a global group identifier, generate monitoring data corresponding to a respective process executing on the compute device, the monitoring data including a process identifier, index the monitoring data having the process identifier to the corresponding global group identifier, synchronize a time stamp of the monitoring data to a network time protocol corresponding to the global group identifier, and transmit the indexed and synchronized monitoring data as tracing data in to the a tracing datastore.

    METHODS AND APPARATUS TO OFFLOAD EXECUTION OF A PORTION OF A MACHINE LEARNING MODEL

    公开(公告)号:US20210397999A1

    公开(公告)日:2021-12-23

    申请号:US17359395

    申请日:2021-06-25

    Abstract: Methods, apparatus, systems and articles of manufacture to offload execution of a portion of a machine learning model are disclosed. An example apparatus includes processor circuitry to instantiate offload controller circuitry to select a first portion of layers of the machine learning model for execution at a first node and a second portion of the layers for remote execution for execution at a second node, model executor circuitry to execute the first portion of the layers, serialization circuitry to serialize the output of the execution of the first portion of the layers, and a network interface to transmit a request for execution of the machine learning model to the second node, the request including the serialized output of the execution of the first portion of the layers of the machine learning model and a layer identifier identifying the second portion of the layers of the machine learning model.

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