Intel Corporation

Dario Sabella

Miltiadis Filippou

Ned M Smith

Francesc Guim Bernat

Kshitij Arun Doshi

Markus Dominik Mueck

Kilian Roth

Jing Zhu

Ingolf Karls

Leonardo Gomes Baltar

Yang Yang

Suraj Prabhakaran

Neal Conrad Oliver

Honglei Miao

Michael Faerber

Rajesh Poornachandran

Kapil Sood

John Joseph Browne

Ana Lucia Pinheiro

Mats Gustav Agerstam

Alexander Bachmutsky

Zongrui Ding

Eve M Schooler

Tarun Viswanathan

Sunil K Cheruvu

Kilian Peter Anton Roth

Bernhard Raaf

Nageen Himayat

Qian Li

Hosein Nikopour

Yang-Seok Choi

Zhibin Yu

Jerome Parron

Bertram Gunzelmann

Biljana Badic

Sabine Roessel

Sudeep Manithara Vamanan

Ching-Yu Liao

Domagoj Siprak

Rajesh Gadiyar

Leifeng Ruan

Shashanka Totadamane Ramappa

Xiaopeng Tong

Mustafa Emara

Christopher Stobart

Soo Jin Tan

Rajkumar Krishnaperumal

Nirlesh Kumar Koshta

Steven A Bowers

Pradeep C Pangi

Bernd Schaller

Disclosed embodiments are related to techniques for implementing Multiple Access Management Services (MAMS), Multi-access Edge Computing (MEC), and 3GPP Fifth Generation (5G) technologies. Embodiments include enhanced application programming interfaces (APIs) that allow applications to influence multi-access (MA) traffic steering decisions, and also get informed of various MA traffic steering modes. Disclosed embodiments also include techniques to enable coexistence between MEC-based MAMS systems and 5G MA traffic steering mechanisms. Other embodiments may be described and/or claimed.

Technologies for control and management of multiple traffic steering services

Disclosed embodiments are related to techniques for implementing Vehicle-to-Everything (V2X) communications in Multi-access Edge Computing (MEC) systems and networks. V2X system scenarios characterized by high mobility and dynamic topologies, where the accuracy and timeliness of radio network information, location information may be hampered by environmental conditions and deployment density of network infrastructure. The disclosed embodiments provide a V2X Information Service (VIS) framework for cooperative acquisition, partitioning, and distribution of information for efficient, journey-specific quality-of-service (QoS) predictions. The VIS framework identifies space/time correlations between radio condition/quality data collected in V2X system(s) and a vehicle's planned journey for better prediction of the radio conditions/quality of the communication network along the designated route. As a consequence, the VIS may expose journey-specific information about the QoS prediction to authorized devices. Other embodiments may be described and/or claimed.

V2X services for providing journey-specific QoS predictions

Methods, systems, and computer programs are presented for sharing information among multiple Mobility as a Service (MaaS) systems for presenting service information to users. One system includes means for receiving, by a multi-access edge computing (MEC) application, parameters of a global quality of service (QoS) prediction model, and means for sending, to a local prediction function (PF), an update of a local QoS prediction model. Further, the system includes means for detecting a journey of a vehicle requested by a user, and means for sending to the local PF, a request for a value of the QoS along the journey. The value of the QoS is a measure of quality of transportation services delivered during the journey. The system further includes means for receiving the predicted value of the QoS along the journey, and means for providing, by the MEC application, the predicted value of the QoS to the vehicle.

Transportation operator collaboration system

Embodiments herein may include systems, apparatuses, methods, and computer-readable media, for a multi-access edge computing (MEC) system. An apparatus for MEC may include a communication interface, a local cost measurements module, and a service allocation module. The communication interface may receive, from a UE, a request for a service to be provided to the UE. The local cost measurements module may collect a set of local cost measurements for the service. The service allocation module may determine to allocate the service to a MEC host based on an allocation policy related to a cost for the MEC host to provide the service or a cost for a service provider to provide the service in view of the one or more local cost measurements. Other embodiments may be described and/or claimed.

Edge computing service provision based on local cost measurements

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.

Calculus for trust in edge computing and named function networks

A named function network (NFN) system includes a routing node, a function generation node, and a server node. The routing node receives requests for new functions, the requests including data values for generating the new functions. The function generation node receives the data values from the routing node and generates a new function for the NFN using the data values. The server node receives a request from the routing node to execute the new function, executes the new function, and transmits results of the execution to the routing node.

Function generation in named function networks

An architecture to allow Multi-Access Edge Computing (MEC) billing and charge tracking, is disclosed. In an example, a tracking process, such as is performed by an edge computing apparatus, includes: receiving a computational processing request for a service operated with computing resources of the edge computing apparatus from a connected edge device within the first access network, wherein the computational processing request includes an identification of the connected edge device; identifying a processing device, within the first access network, for performing the computational processing request; and storing the identification of the connected edge device, a processing device identification, and data describing the computational processes completed by the processing device in association with the computational processing request.

Multi-access edge computing (MEC) billing and charging tracking enhancements

Various techniques for collective perception messaging are disclosed herein. In an example, a machine receives, from a source device, a signal value for provision to a sink device, the signal value corresponding to a measurement of an environmental value. The machine accesses, from a storage device, an error term for the signal value. The machine accesses, from the storage device, a source reliability term for the source device. The machine accesses, from the storage device, a source-sink relation term based on the source device and the sink device. The machine determines a distribution for the environmental value based on the error term, the source reliability term, and the source-sink relation term. The machine determines, based on the distribution for the environmental value, whether the signal value is reliable.

Collective perception messaging for source-sink communication

A service coordinating entity device includes communications circuitry to communicate with a first access network, processing circuitry, and a memory device. The processing circuitry is to perform operations to, in response to a request for establishing a connection with a user equipment (UE) in a second access network, retrieve a first Trusted Level Agreement (TLA) including trust attributes associated with the first access network. One or more exchanges of the trust attributes of the first TLA and trust attributes of a second TLA associated with the second access network are performed using a computing service executing on the service coordinating entity. A common TLA with trust attributes associated with communications between the first and second access networks is generated based on the exchanges. Data traffic is routed from the first access network to the UE in the second access network based on the trust attributes of the common TLA.

Multi-domain trust establishment in edge cloud architectures

A computing device includes communications circuitry to communicate with a first access network and processing circuitry. The processing circuitry is to perform operations to transmit an authorization request for a vehicle-to-everything (V2X) communication to a V2X application function within a service coordinating entity, the request transmitted from the device via the first access network V2X configuration parameters are received from the service coordinating entity, via the first access network. The V2X configuration parameters are received in response to the authorization request and based on V2X subscription information received by the V2X application function via a V2X application programming interface (API) within the service coordinating entity. A V2X communication link for the V2X communication is established with a second device based on the V2X configuration parameters, the second device associated with a second access network.

Multi-access edge computing (MEC) based multi-operator support for C-V2X systems

A system configured to track network slicing operations within a 5G communication network includes processing circuitry configured to determine a network slice instance (NSI) associated with a QoS flow of a UE. The NSI communicates data for a network function virtualization (NFV) instance of a Multi-Access Edge Computing (MEC) system within the 5G communication network. Latency information for a plurality of communication links used by the NSI is retrieved. The plurality of communication links includes a first set of non-MEC communication links associated with a radio access network (RAN) of the 5G communication network and a second set of MEC communication links associated with the MEC system. A slice configuration policy is generated based on the retrieved latency information and slice-specific attributes of the NSI. Network resources of the 5G communication network used by the NSI are reconfigured based on the generated slice configuration policy.

Multi-slice support for MEC-enabled 5G deployments

Systems, apparatuses, methods, and computer-readable media, are provided for offloading computationally intensive tasks from one computer device to another computer device taking into account, inter alia, energy consumption and latency budgets for both computation and communication. Embodiments may also exploit multiple radio access technologies (RATs) in order to find opportunities to offload computational tasks by taking into account, for example, network/RAT functionalities, processing, offloading coding/encoding mechanisms, and/or differentiating traffic between different RATs. Other embodiments may be described and/or claimed.

Application computation offloading for mobile edge computing

Systems and methods for establishing, configuring, and operating multi-access edge computing (MEC) services and service consumption through zoning hosts in multi-vendor or multi-system environments. An apparatus operating as a MEC orchestrator to manage services consumption using zones is configurable to perform operations to: receive, from an application executing at a host, a request for a list of services and corresponding proximity zones; in response to receiving the request for the list of services, query a plurality of hosts for performance metrics of respective services offered from the plurality of hosts, the respective services to be used by the application executing at the host; construct a zone map, the zone map maintaining a mapping between the application and the plurality of hosts based on the performance metrics; and manage migration of the application or a service of the respective services, based on the zone map, to ensure QoS of the application.

Flexible multi-access edge computing (MEC) services consumption through hosts zoning

A central trajectory controller including a cell interface configured to establish signaling connections with one or more backhaul moving cells and to establish signaling connections with one or more outer moving cells, an input data repository configured to obtain input data related to a radio environment of the one or more outer moving cells and the one or more backhaul moving cells, and a trajectory processor configured to determine, based on the input data, first coarse trajectories for the one or more backhaul moving cells and second coarse trajectories for the one or more outer moving cells, the cell interface further configured to send the first coarse trajectories to the one or more backhaul moving cells and to send the second coarse trajectories to the one or more outer moving cells.

Methods and devices for wireless communications

Systems, apparatuses, methods, and computer-readable media, are provided for managing background data transfer sessions based on network events monitored by one or more network elements. Embodiments may be relevant to multi-access edge computing (MEC) and Automotive Edge Computing Consortium (AECC) technologies. Other embodiments may be described and/or claimed.

Vehicle-to-everything session and service continuity in automotive edge computing systems

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Munich, Germany

Dario Sabella