Some embodiments provide a managed network for implementing a logical network for a tenant. The managed network includes a first set of host machines and a second set of host machines. The first set of host machines is for hosting virtual machines (VMs) for the logical network. Each of the first set of host machines operates a managed forwarding element that implements a first logical router for the tenant logical network and a second logical router to which the first logical router connects. The implementation of the second logical router is for processing packets entering and exiting the tenant logical network. The second set of host machines is for hosting L3 gateways for the second logical router. The L3 gateways connect the tenant logical network to at least one external network.

The technologies described herein are generally directed to utilizing templates with associated naming policies to deploy network equipment in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include, based on a similarity criterion, evaluating a request to deploy network equipment at a deployment location, resulting in a deployment template. The method can further include, based on the deployment template, identifying a naming policy of the group of naming policies to apply to deployment of the network equipment, resulting in an identified naming policy. Further, the method can include, based on the identified naming policy and the deployment template, facilitating deploying the network equipment.

A device control method includes a first electronic device that configures a network for a home device using a first home application logged in using a first account, sends, in response to a first user operation, an authorization request to a device server, and receives a first authorization code; the home device receives the first authorization code; the first electronic device outputs the first authorization code; the device server stores second personal information corresponding to the second account; a second electronic device sends account information of the second account and a second authorization code to the home device, wherein a second home application is installed on the second electronic device and is logged in using a second account; the home device performs verification on the second authorization code based on the first authorization code, and when the verification succeeds, stores the account information of the second account.

Methods and systems for automated shell discovery and access. The method includes gathering, by a control server, fingerprint data for a target device for which root shell access is required, generating, by a decision tree controller, a decision tree from the gathered fingerprint data, traversing, by the decision tree controller, the decision tree to determine a path to the root shell, reporting, by the decision tree controller to the control server, a found access path, connecting, by the control server to the root shell on the target device, using one or more commands associated with the found access path, and executing, by the control server at an accessed root shell of the target device, privileged commands associated with a task.

Disclosed are various examples of an action framework for configuring a gateway to perform actions on the gateway itself or in conjunction with connected IoT devices. In some embodiments, a gateway client is permitted on a gateway device using a gateway configuration received from a management service. A command callback function is registered in association the gateway client. An action message is received. The action message specifies an action corresponding to the command callback function. The gateway client using the action message. A command object for the gateway client is generated based on the action message. The command callback function is invoked, and the gateway client processes the command object to perform the action.

Remotely access computing resources had been a major challenge to many information technology (IT) vendors. For example, the traditional remote file system access products such as network file system (NFS) and message block (MSB) all cannot be used for web based accessing across global due to both of them demanding huge computing powers and network bandwidth. For other example, traditional web based remote files and folders accessing products such as Yahoo Briefcase and VMWare ESX Server 2.0 all cannot support operations such as move or copy a file from one folder into another due to both of them only can display two levels for a file system, thus only can operate files and folders within one flat level. This invention has overcome the problems had occurred in the traditional accessing of remote computing resources by introducing a method and apparatus for creating multi-layered item list to mirror computing resources. The use of the multi-layered item list has effectively reduced the requirement of computing power and network bandwidth and also provides user to operate computing resources at two or more levels by controlling displaying two more levels of the computing resources.

The present technology provides a system and method for automating on-boarding and management of IoT devices on data network. The disclosed technology further provides an interactive representation of various performance attribute with automatically generated actionable alert based on operator defined rules and performance-specific threshold values. Furthermore, disclosed technology provides for single-click activation of suggested actions at scale directed at once to all device units within one or more device groups reported in critical state. In this way the proposed technology enables rapid restoration of a network state. Offending device(s) may then be easily identified, from device units within the device category isolated in a resolution space, and managed according to one or more device-specific actionable alerts automatically generated on the offending device.

A system for facilitating emulation in a distributed environment is provided. During operation, the system can obtain configuration of a physical edge rack, which can include one or more physical computing devices. The system can then present an emulated edge rack in a user interface. The emulated edge rack can be a digital twin of the physical edge rack. Based on a user input received via the user interface, the system can emulate a power-on event on the emulated edge rack. The emulated power-on event can emulate a power-on event for the physical edge rack. The system can then emulate the deployment of a component in the powered-on emulated edge rack based on configuration information received via the user interface. The deployed component can be configured based on the configuration information.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying data indicating respective hardware profiles of the server computing systems; analyzing the hardware profiles, including: detecting, for each of the server computing systems, a model type of the server computing system, and detecting, for each model type of the server computing systems, a module configuration of the server computing system; grouping, for each model type, each server computing system that is associated with a same module configuration into a set of server computing systems; receiving user input indicating selection of at least one set of server computing systems; determining that, for at least one set of server computing systems, that a respective module configuration is to be applied globally based on the user input; and applying the respective module configuration to the at least one set of server computing systems.

The present application relates to developing and deploying machine learning analysis systems using a low-code or no-code approach. A cloud service is configured to receive a first data stream from a sensor device and train a machine-learning model to recognize selected elements of the first data stream that are selected from a package of template models via a graphical user interface. The cloud service deploys the machine-learning model to an edge datacenter configured to receive a second data stream via a network connection. The edge datacenter locally interrogates the second data stream based on the machine-learning model to generate an element set including the selected elements. A logic service may receive a selection of one or more properties of the element set and one or more logical operators via a graphical user interface to generate user-configured logical rules. The logic service may the user-configured logical rules to the element set.