A method (1000) for performance modeling of a plurality of microservices (215) includes deploying the plurality of microservices (215) within a network (1260). The plurality of microservices (215) are communicatively coupled to generate at least one service chain (310) for providing at least one service. Based on a resource allocation configuration, an initial set of training data for the plurality of microservices within the network (1260) is determined. At least a portion of data is excluded from the initial set of training data to generate a subset of training data. A Quality of Service (QoS) behaviour model is generated based on the subset of the training data.

A method and an apparatus for controlling a network service of an Internet of things terminal, and a related storage medium are disclosed. In the method, after receiving a uniform resource locator (URL) sent by an Internet of things terminal in an authentication process, a network management device obtains a manufacturer usage descriptions (MUD) file based on the URL, and parses the MUD file to obtain content of at least one field included in the MUD file, where the MUD file includes at least one of: a first-type field or a second-type field, the first-type field is used to describe a security isolation requirement, and the second-type field is used to describe a quality assurance requirement.

An information processing apparatus including: a memory; and a processor coupled to the memory, the processor being configured to: in a network coupling a plurality of storage nodes, at least one proxy, and at least one client; collect information of accesses executed most by the at least one client via the at least one proxy on a path of each access; based on the information of accesses, calculate network distances between the plurality of storage nodes and the at least one proxy; and based on the network distances, determine a leader to be one of the plurality of storage nodes that is close to one of the at least one proxy accessed most frequently.

A device may include a processor configured to register a network function, of a core network associated with a radio access network, in a network function repository for the core network. The processor may be further configured to obtain load information for the network function, wherein the load information indicates a load associated with the network function during a time period; determine that the load associated with the network function has reached a threshold based on the load information; and send an alert to an orchestration system to adjust a capacity for the network function, in response to determining that the load associated with the network function has reached the threshold.

Embodiments are directed to an overlay network for an industrial Internet of Things. The overlay network has multiple main components: (1) a security component, such as a cloaked network, (2) a digital twin component that operates as digital simulations of the physical devices, (3) a communications mesh, and (4) a resource provisioning matrix for adjusting the resources used by the digital twin. The overlay network is a virtual network that is Software Defined—it sits on top of the existing Internet physical hardware of servers, routers, etc. The overlay network is sometimes referred to herein as a Software Defined Secure Content/Context Aware Network (SD-SCAN).

In one embodiment, a service divides one or more time series for a network key performance (KPI) into a plurality of time series chunks. The service clusters the plurality of time series chunks into a plurality of clusters. The service identifies a sketch that represents a particular one of the clusters. The service associates a label with the identified sketch. The service applies the label to a new KPI time series by matching the sketch to the new KPI time series.

Software performance can be automatically managed in a distributed computing environment. In one example, a system that can receive metrics information describing resource usage by a first instance of a service in a distributed computing environment. The system can also determine a quality-of-service (QoS) constraint for the service. The system can then modify a definition file based on the metrics information and the QoS constraint, the definition file being configured for deploying instances of the service in the distributed computing environment. The system can deploy a second instance of the service in the distributed computing environment using the modified definition file. As a result, the second instance can more closely satisfy the QoS constraint than the first instance.

Software performance can be automatically managed in a distributed computing environment. In one example, a system that can receive metrics information describing resource usage by a first instance of a service in a distributed computing environment. The system can also determine a quality-of-service (QoS) constraint for the service. The system can then modify a definition file based on the metrics information and the QoS constraint, the definition file being configured for deploying instances of the service in the distributed computing environment. The system can deploy a second instance of the service in the distributed computing environment using the modified definition file. As a result, the second instance can more closely satisfy the QoS constraint than the first instance.

Techniques described herein relate to a method for diagnosing and remediating service failures. The method includes identifying, by a diagnostic and remediation manager, a diagnostic event associated with a service of services; generating a dependency directed acyclic graph (DAG) associated with the service; generating health vectors associated with each node of the dependency DAG; updating the dependency DAG using the health vectors to generate an unhealthy subgraph; and remediating the service based on the unhealthy subgraph.

The invention relates cloud based IoT network monitoring and validation to enable optimal network selection and connectivity for IoT sensors. The present invention relates to a system to measure the signal quality directly from the network module of IoT sensors. It comprises of an application programming interface (API) 105, a Network detection dongle 103, communication network 110, server 115, network modules of network operators and IoT sensors 120, to be deployed or installed. The invention also relates to a method for determination of signal strength from network module of IoT sensors, wherein the API 105 is configured to run network detection software to determine and validate an optimal location for IoT sensor/device installation or deployment based on the highest signal strength.