The presently disclosed subject matter aims to a system and method directed to provide a remote session user experience analysis. The system and method includes Remote Desktop Protocol (RDP) server comprising a processing circuitry configured to: obtain connection information from a Remote Desktop Protocol (RDP) client, remote from the Remote Desktop Protocol (RDP) server, associated with a connection between the Remote Desktop Protocol (RDP) client and a networking device directly communicating with the Remote Desktop Protocol (RDP) client; and, generate, based on the connection information, a user experience score indicative of the quality of the connection during the remote session.

The present technology can provide a simple to use interface for receiving a selected machine learning task and one or more file pointers indicating a network location where data to be input in the machine learning task is stored. The present technology can also provide a connector that can ingest the input data from the network location; and automatically label the input data to be suitable for the selected machine learning task. The connector can further generate a machine learning compute request comprising a control information specifying one or more parameters for the selected machine learning task and a machine learning dataset generated from the labeled sequences of input data.

A method for configuring a service node, a service node pool registrar, and a system are provided. The method includes receiving a service node query request sent by a management configuration device. The service node query request includes a service requirement. The service requirement is from a user or caused by a network change. The method further includes searching a service node database, to obtain service node information that matches the service node query request. The method further includes sending the matching service node information to the management configuration device, causing the management configuration device to perform network and service configuration on the matching service node according to network topology information that has been obtained and the matching service node information.

A method implemented to provide a virtual network to tenants requiring bandwidth in a cloud computing environment is disclosed. The method starts with receiving a request for a task at a network device, the request including a first parameter indicating VMs required, a second parameter indicating bandwidths the required VMs need, a third parameter indicating a duration of the task, and a fourth parameter indicating a deadline of the task. The network device then selects a starting time and a bandwidth allocation of the task, where the bandwidth allocation is shrank to be smaller than the second parameter indicating, and where the selection aims at minimizing a measurement of cloud resource utilization considering consumptions of both VMs and bandwidth. Then the network device allocates VMs for the request at the starting time with the bandwidth allocated at a particular location in the cloud computing environment.

The present disclosure facilitates improving the operation of a cloud service by updating its configuration information and its resource requirements. The resource utilization of the cloud service can be monitored, and a decision logic module can determine whether action is required. When action is required, an update can be prepared and applied, and notifications can be generated about the condition and its resolution. Resolutions can require correlation of multiple cloud services to provide real-time access to information that is not otherwise available to a single entity. Resolutions can be learned and predicted in a number of ways using a predictive engine.

Cloud infrastructure planning systems and methods can utilize artificial intelligence/machine learning agents for developing a plan of demand, plan of record, plan of execution, and plan of availability for developing cloud infrastructure plans that are more precise and accurate, and that learn from previous planning and deployments. Some agents include one or more of supervised, unsupervised, and reinforcement machine learning to develop accurate predictions and perform self-tuning alone or in conjunction with other agents.

Techniques are disclosed for generating a virtual representation (e.g., one or more digital twin models) of a multi-access edge computing system environment, and managing the multi-access edge computing system environment via the virtual representation. By way of example only, such techniques enable understanding, prediction and/or optimization of performance of applications and/or systems operating in the multi-access edge computing environment.

A method for configuring a service node, a service node pool registrar, and a system are provided. In certain embodiments, the method includes receiving a service node query request sent by a management configuration device. The service node query request includes a service requirement. The service requirement is from a user or caused by a network change. The method further includes searching a service node database, to obtain service node information that matches the service node query request. The method further includes sending the matching service node information to the management configuration device, causing the management configuration device to perform network and service configuration on the matching service node according to network topology information that has been obtained and the matching service node information.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: identifying a plurality of asset resources; selecting a workload for allocation of asset resources; determining which asset resources of the plurality of asset resources may be needed for allocation, determination of which asset resources of the plurality of asset resources may be needed for allocation taking into account on-premises asset resources and cloud-based asset resources the inventory of the available asset resources; and, performing a data center hybrid cloud asset allocation operation, the data center asset allocation operation allocating resources the workload based upon the determining.

Systems and methods of cloud deployment optimization are disclosed. In some example embodiments, a method comprises running original instances of an application concurrently on original servers to implement an online service, receiving, by the original instances of the application original requests for one or more functions of the online service, receiving a command to deploy a number of additional instances of the application, transmitting synthetic requests for the function(s) of the online service to one of the original servers according to a predetermined optimization criteria, deploying the number of additional instances of the application on additional servers using a copy of the original instance of the application, and running the deployed additional instances of the application on their corresponding additional servers concurrently with the original instances of the application being run on their corresponding original servers.