Abstract: An overload protection mechanism for network nodes in an edge node cluster uses two tier reinforcement learning models; one at the edge node level and one at the cluster level. The node level reinforcement learning model optimizes a routing policy for a service/application to determine whether an edge node receiving a client request shall handle the traffic for the service/application. The cluster level reinforcement learning model optimizes a policy for an application that determines which neighboring edge node shall be considered to handle a client request for the given application in case of redirection or proxy by the edge node receiving the client request.

Abstract: A method implements a network slicing controller to manage network slicing instances in an edge cloud platform. The method includes receiving at least one policy change from an artificial intelligence powered smart traffic controller (APSTC) or an artificial intelligence powered edge traffic controller (APETC), determining whether the at least one policy change is valid based on local monitoring information, and sending the at least one policy change to a common control network function in a 5G mobile network.

Abstract: An overload protection mechanism for network nodes in an edge node cluster uses two tier reinforcement learning models; one at the edge node level and one at the cluster level. The node level reinforcement learning model optimizes a routing policy for a service/application to determine whether an edge node receiving a client request shall handle the traffic for the service/application. The cluster level reinforcement learning model optimizes a policy for an application that determines which neighboring edge node shall be considered to handle a client request for the given application in case of redirection or proxy by the edge node receiving the client request.

Abstract: The present disclosure provides a system for coordinating the distribution of resource instances (e.g. Kubernetes nodes) that belong to different infrastructure providers providing resource instances at different locations. Each infrastructure provider provides one or more resource instances. The resource instances provide by an infrastructure can be spread over multiple locations. Several Kubernetes master nodes are deployed to manage the RIs spread among multiple infrastructure providers and multiple locations.

Abstract: An open edge cloud platform (OECP) enables tenants to access resources of a pool of network operator to support tenant applications. The tenant can specify a virtual location when requesting creation of a virtual network for a tenant application. The OECP creates a virtual network for the tenant application from available resources of a pool of network operators at the virtual location specified by the tenant in the request. The flexibility of selecting resources from a pool of network operators enables the tenant to access resources closer to the end user devices that will use the tenant application and thus reduce latency for applications and increase data throughput.

Abstract: The disclosure relates to an Edge Cloud Platform (ECP) and a method executed in the ECP, for providing a low-latency, distributed, multi-user application. The method comprises determining a first location of a first group of users requesting access to the multi-user application and deploying the multi-user application in a first Point of Presence (PoP) in a first Service Provider (SP) domain operative to serve the first group of users. The method comprises determining a second location of a second group of users requesting access to the multi-user application and deploying a proxy of the multi-user application in a second PoP in a second SP domain operative to serve the second group of users. The method comprises, upon determining that a Software License Agreement (SLA) exists between the first and second SPs, establishing a tunnel for linking the multi-user application and the proxy of the multi-user application, thereby providing the low-latency.

Abstract: The disclosure relates to a method and system, for managing components of a fifth generation (5G) network slice. The method comprises retrieving current locations of a plurality of user equipments (UEs) connected to radio base stations (RBSs) in communication with the 5G network slice; predicting future traffic at the RBSs based on past and current locations of the plurality of UEs; and managing the components of the 5G network slice based on the predicted future traffic patterns.

Abstract: A method, Delivery Node, DN, and Content Delivery Network, CDN, are optimized to deliver content of different categories. The DN is operative to receive a request for a content, obtain a determination of whether or not a category of content is associated with the requested content and responsive to the determination that no category of content is associated with the requested content, forward the request for the content towards an origin server and upon receiving a response from the origin server serve the content. The CDN comprises a plurality of DNs, a data processing service operative to obtain, from the DNs, and assemble, data sets into training and validation data formatted to be used for training and validating a Neural Network, NN. The CDN comprises a NN training service, operative to train and validate the NN and a configuration service, operative to configure the plurality of DNs with the trained and validated NN.

Abstract: A request router is disclosed for a multi-tier Content Delivery Network (CDN), each tier corresponding to a hierarchical layer of the CDN. The request router includes processing circuitry configured to: receive, from a first delivery node of a hierarchical layer of the CDN, a routing request to bypass the first delivery node for content delivery to a user equipment; and as a result of receiving the routing request to bypass the first delivery node for the content delivery to the user equipment, select at least a second delivery node for the content delivery to the user equipment.

Abstract: The disclosure relates to a system, method, and network node for generating at least one classification based on multiple data sources. The system comprises at least one layer comprising one or more supervised neural networks (SNN); at least one layer comprising one or more unsupervised neural networks (USNN); and at least one normalization layer. Each of the layers has inputs and outputs, the inputs of a first layer being operative to receive data from the data sources, the inputs of a layer other than the first layer being communicatively connected to the outputs of a previous layer, the outputs of a layer other than a last layer being communicatively connected to inputs of a following layer, the last layer having at least one output, and the at least one normalization layer being operative to normalize the outputs from the previous layer into normalized inputs for the following layer.

Abstract: A method and request router (RR) are provided for dynamically pooling resources in a Content Delivery Network (CDN), for efficient delivery of live and on-demand content. The method comprises receiving, at the RR, a request for a content from a client, determining a content type associated with the request for the content, the content type being one of: live content and on-demand content. The method also comprises, based on the determined content type, dynamically electing, at the RR, delivery nodes at edge, region or core for content delivery and grouping the dynamically elected nodes into a resource pool, selecting a delivery node within the resource pool for delivering the content and sending a response to the client including an address of the delivery node selected within the resource pool to be used to get the requested content.

Abstract: A network node (20) operative to adjust chunk size is provided. The network node (20) includes processing circuitry (34) having a processor (36) and memory (38). The memory (38) includes executable instructions which, when executed by the processor (36), configure the processor (36) to: determine at least one measured 5 performance characteristic associated with the network, dynamically adjust a chunk size for caching each of a plurality of segments of at least a portion of content, cause transmission of a content request, according to the dynamic adjustment of the chunk size, for at least one segment of the plurality of segments of at least the portion of the content. The dynamic adjustment of the chunk size is based on the at least one 10 measured performance characteristic associated with the network.

Abstract: A method and request router (RR) are provided for dynamically pooling resources in a Content Delivery Network (CDN), for efficient delivery of live and on-demand content. The method comprises receiving, at the RR, a request for a content from a client, determining a content type associated with the request for the content, the content type being one of: live content and on-demand content. The method also comprises, based on the determined content type, dynamically electing, at the RR, delivery nodes at edge, region or core for content delivery and grouping the dynamically elected nodes into a resource pool, selecting a delivery node within the resource pool for delivering the content and sending a response to the client including an address of the delivery node selected within the resource pool to be used to get the requested content.

Abstract: A method, Delivery Node, DN, and Content Delivery Network, CDN, are optimized to deliver content of different categories. The DN is operative to receive a request for a content, obtain a determination of whether or not a category of content is associated with the requested content and responsive to the determination that no category of content is associated with the requested content, forward the request for the content towards an origin server and upon receiving a response from the origin server serve the content. The CDN comprises a plurality of DNs, a data processing service operative to obtain, from the DNs, and assemble, data sets into training and validation data formatted to be used for training and validating a Neural Network, NN. The CDN comprises a NN training service, operative to train and validate the NN and a configuration service, operative to configure the plurality of DNs with the trained and validated NN.

Abstract: The disclosure relates to an Edge Cloud Platform (ECP) and a method executed in the ECP, for providing a low-latency, distributed, multi-user application. The method comprises determining a first location of a first group of users requesting access to the multi-user application and deploying the multi-user application in a first Point of Presence (PoP) in a first Service Provider (SP) domain operative to serve the first group of users. The method comprises determining a second location of a second group of users requesting access to the multi-user application and deploying a proxy of the multi-user application in a second PoP in a second SP domain operative to serve the second group of users. The method comprises, upon determining that a Software License Agreement (SLA) exists between the first and second SPs, establishing a tunnel for linking the multi-user application and the proxy of the multi-user application, thereby providing the low-latency.

Abstract: A request router is disclosed for a multi-tier Content Delivery Network (CDN), each tier corresponding to a hierarchical layer of the CDN. The request router includes processing circuitry configured to: receive, from a first delivery node of a hierarchical layer of the CDN, a routing request to bypass the first delivery node for content delivery to a user equipment; and as a result of receiving the routing request to bypass the first delivery node for the content delivery to the user equipment, select at least a second delivery node for the content delivery to the user equipment.

Abstract: The disclosure relates to a method, that can be executed by an edge delivery node, a set-top-box or a user device, for providing to a user a higher resolution media segment by enhancing a low-resolution media segment. The method comprises obtaining the low-resolution media segment; using a neural network operative to enhance media segments to enhance the low-resolution media segment into the higher resolution media segment; and providing the higher resolution media segment to the user.

Abstract: The disclosure relates to a system, methods, devices and nodes for managing shared user content. The system comprises a wireless device which is operative to upload the content to be shared to a Content Delivery Network (CDN) Delivery and Caching Node (DCN), which in turn is operative to cache and deliver the shared user content to other wireless devices and network nodes. The system further comprises a CDN management node, operative to manage the storage of the shared user content in the CDN DCN and a network node operative to instruct the wireless device to store the shared user content to the CDN DCN and to retrieve the shared user content from the CDN DCN.

Abstract: According to some embodiments, a virtual machine manager is operable to manage a virtual machine (VM) that spans a plurality of hardware appliances. The virtual machine manager receives a request to provide a service to a device, selects an application instance to provide the service to the device, and forwards the request for the service to the selected application instance. The selected application instance is running in an instance of the virtual machine associated with a first hardware appliance. The virtual machine manager determines session data associated with the service and provides the session data associated with the service to a second hardware appliance of the VM in order to enable the VM to provide high availability of the service to the device.

Abstract: The invention is directed to secure content delivery. A device (10) sends (4) a content request to a CP (14) identifying targeted content and a public key (K PUB) of the device. A response (8) identifies a Content Delivery Network (18-1, 18-2), CDN, and includes an authorization token (TOKEN A) and a content key (K-NEW) derived from the public key of the communication device and a private key of the CP. The authorization token is sent (10) to the CDN and a delivery token (TOKEN B) and delivery node address are received (15). The delivery token and content key are sent (18) to the delivery node and encrypted content is received (30), the encrypted content comprises content as initially encrypted by the CP and as further encrypted by the CDN using the content key. The encrypted content is decrypted (31) using a private key of the device (10).