Abstract: There is provided mechanisms for generating a security configuration profile for a network entity. A method is performed by a security configuration entity. The method comprises generating the security configuration profile for the network entity based on network entity information, deployment information, and feedback information for a previously generated security configuration profile. The method comprises determining, based on calculating a risk score for the generated security configuration profile, whether the security configuration profile is to be provided towards the network entity or not. The method comprises generating feedback information for the security configuration profile based on the risk score, the network entity information, and the deployment information.

Abstract: Method and machine learning agent for executing machine learning on an industrial process by using computing resources in an edge cloud. A state of the industrial process is identified (2:1) and a learning model 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 herein relate to e.g. a method performed by an entity in a communication network for providing a service using distributed resources in the communication network. The entity obtains a detected state of computational performance of the service, and determines, based on the obtained detected state, a policy for handling scaling of resources in the communication network. The entity further initiates a scaling of the resources based on the determined policy.

Abstract: Method and machine learning agent for executing machine learning on an industrial process by using computing resources in an edge cloud. A state of the industrial process is identified (2:1) and a learning model 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: A method and a first agent controlling computing resources in a first edge cloud, for supporting a machine learning operation. When detecting that additional computing resources outside the first edge cloud are needed for the machine learning operation, the first agent obtains said additional computing resources from a second edge cloud. The machine learning operation is then performed by using computing resources in the first edge cloud and the additional computing resources obtained from the second edge cloud.