Abstract:
A method and a first agent (200) controlling computing resources in a first edge cloud (200A), for supporting a machine learning operation. When detecting (2:3) that additional computing resources outside the first edge cloud are needed for the machine learning operation, the first agent obtains (2:4 – 2:6) said additional computing resources from a second edge cloud (202A). The machine learning operation is then performed (2:8) by using computing resources in the first edge cloud (200A) and the additional computing resources obtained from the second edge cloud (202A).
Abstract:
The present disclosure relates to a method for anchoring an edge cloud to a central cloud, the method being performed in a cloud environment comprising a central cloud and an edge cloud, the method comprising obtaining (S238, S310), by a connectivity controller of an edge cloud, an address of an anchoring registry of a central cloud; sending (S240, S312), by the connectivity controller, to the anchoring registry, information about networking configuration of the edge cloud; setting up (S246, S314), by an orchestrator of the central cloud, a virtual private network, VPN, service in the central cloud; requesting (S248, S316), by the orchestrator of the central cloud, edge VPN configuration information from the central VPN service, based on the information about networking configuration of the edge cloud; sending (S252, S318), by the anchoring registry, the edge VPN configuration information, to an orchestrator of the edge cloud; creating (S258, S320), by an orchestrator of the edge cloud, an edge VPN service, based on the edge VPN configuration information; and establishing (S260, S322) a VPN connection between the edge VPN service and the central VPN service, whereby services from either one of the edge cloud or the central cloud are exposed in the edge cloud and the central cloud.
Abstract:
A method in a user equipment for attaching the user equipment to a mobile communications network comprises receiving a list of network slice identities, wherein a network slice identity identifies a portion of the mobile communications network that serves as a logical network to a set of user equipment (step 201). A network slice is selected based on one or more criteria (step 203). A network slice attachment request is sent to a network node (step 205), for requesting attachment of the user equipment to the selected network slice of the mobile communications network.
Abstract:
Example embodiments presented herein are directed towards a physical node, and corresponding methods therein, for providing authentication of a wireless device within a visiting wireless network while the wireless device is in a roaming state. The wireless device is registered to a home wireless network. The physical node further comprises a virtual representation of a functionality of at least one core network node controlled by the home wireless network. Thus, such authentication may be provided and control according to home network based procedures.
Abstract:
A computing node is disclosed. The computing node comprises processing circuitry configured to cause the computing node to receive a message (102) comprising configuration information for a resource of a data object that is hosted at the computing node and is associated with a computational operation, which computational operation is executable by the computing node. The processing circuitry is further configured to cause the computing node to configure (104) the resource of the data object on the computing node in accordance with the received configuration information, and to execute (106) the computational operation in accordance with the configured resource. Also disclosed are a corresponding server node and methods of operating a computing node and a server node. The computing node may comprise a Lightweight Machine to Machine (LwM2M) client and the server node may comprise an LwM2M server.
Abstract:
A method (100) for orchestrating execution of a complex computational operation by at least one computing node is disclosed, wherein the complex computational operation can be decomposed into a plurality of component computational operations. The method, performed by an orchestration node, comprises discovering at least one computing node that has exposed, as a resource, a capability of the computing node to execute at least one component computational operation of the plurality of component operations (110). The method further comprises, for each component computational operation of the complex computational operation, selecting a discovered computing node for execution of the component computational operation (120), and sending a request message to each selected computing node requesting the selected computing node execute the component computational operation for which it has been selected (130). The method further comprises checking for a response to each sent request message (140).
Abstract:
In response to a transition (189) from a previous operational state (181 -184) to a current operational state (181 -184) of a given network partition (171, 172) of a plurality of network partitions (171, 172) of a core (1 15) of a cellular network (100), a respective entry (191, 192) of a registry (190) of the plurality of network partitions (171, 172) is updated. Network partition selection (501 ) for a terminal (130-1, 130-2, 130) is effected by participating in a communication of at least one selection control message (412, 424, 425) corresponding to at least one entry (191, 192) of the registry (190).
Abstract:
A VLC signal representing an alignment identifier is detected by cameras (4) of multiple user devices (1, 2, 3) filming a scene. Encoded video frames (91, 92, 93) from the user devices (1, 2, 3) are decoded and light patterns representing the captured VLC signal are identified in at least some of the video frames following decoding. The light patterns are decoded into alignment identifiers that are used in order to time align video frames (91, 92, 93) from different user devices (1, 2, 3) to thereby achieve video frame synchronization of video data from multiple user devices (1, 2, 3) recording a scene. The embodiments thereby enable video frame synchronization without the need for accurate clock synchronization between the user devices (1, 2, 3) and a video synchronization system (10).
Abstract:
Method and machine learning agent (200) for executing machine learning on an industrial process (202) by using computing resources in an edge cloud (204). A state (202A) of the industrial process is identified (2:1) and a learning model (206) comprising a training algorithm for the machine learning is selected (2:2) based on the identified state. The training algorithm in the selected model is then adapted (2:4) so that the amount of available computing resources in the edge cloud is sufficient for computations in the training algorithm. The adapted training algorithm is finally applied (2:5) on data generated in the industrial process using computing resources in the edge cloud. Thereby, computing resources in the edge cloud can be used and no additional resources are needed, thus reducing latency and bandwidth consumption.
Abstract:
Embodiments described herein provide a method, in a serverless life-cycle management, LCM, dispatcher, and an associated serverless LCM dispatcher for implementing a workload in a virtualization network. The method comprises receiving a workload trigger comprising an indication of a first workload, obtaining a description of the first workload from a workload description database based on the indication of the first workload; categorising, based on the description and the workload trigger, the first workload as a non LCM workload capable of being implemented with no LCM routines, or an LCM workload capable of being implemented using LCM routines; and responsive to categorising the first workload as an LCM workload, determining, a LCM capability level for implementing the first workload, identifying an LCM component capable of providing the LCM capability level; and transmitting an implementation request to the LCM component to implement the first workload.