A cloud-based platform may receive an indication of one or more event associated with a service provider and modify a configuration associated with providing a schedule of respective service appointments offered by the service provider based on the indication of the one or more events. The cloud-based platform may then receive a request for a new service appointment after modifying the configuration and in response to receiving the request for the new service appointment, determine a number of scheduled appointments associated with a first time window, determine whether the number of scheduled appointments is less than a maximum number of appointments associated with the first time window, and automatically schedule the new service appointment during the first time window in response to determining that the number of scheduled appointments is less than the maximum number of appointments associated with the first window.

A cloud-based platform may receive an indication of one or more event associated with a service provider and modify a configuration associated with providing a schedule of respective service appointments offered by the service provider based on the indication of the one or more events. The cloud-based platform may then receive a request for a new service appointment after modifying the configuration and in response to receiving the request for the new service appointment, determine a number of scheduled appointments associated with a first time window, determine whether the number of scheduled appointments is less than a maximum number of appointments associated with the first time window, and automatically schedule the new service appointment during the first time window in response to determining that the number of scheduled appointments is less than the maximum number of appointments associated with the first window.

An organization has or uses an SLA system that selects a service-level agreement (SLA) that should apply to a given user request. The SLA system then can monitor the state of the ticket created based on the user request and provide messages or other feedback to the agent to aid the agent in meeting any performance goals associated with the SLA. The SLA system can determine the SLA to associate with a given user request by inferring a type of the request and selecting the SLA based on the type, or by using a model to directly associate an SLA to a given user request, without the need to infer an intermediate type for the user request, which eliminates the need for administrators to create and maintain metadata to guide the association between types and SLAs.

An organization has or uses an SLA system that selects a service-level agreement (SLA) that should apply to a given user request. The SLA system then can monitor the state of the ticket created based on the user request and provide messages or other feedback to the agent to aid the agent in meeting any performance goals associated with the SLA. The SLA system can determine the SLA to associate with a given user request by inferring a type of the request and selecting the SLA based on the type, or by using a model to directly associate an SLA to a given user request, without the need to infer an intermediate type for the user request, which eliminates the need for administrators to create and maintain metadata to guide the association between types and SLAs.

A marketplace system (MPS) controls display of an input screen on which a network service is selectable. The MPS receives from a purchaser terminal to select one or a plurality of network services on the input screen. A network operating system (NOS) generates network configuration data indicating a configuration of the selected network service. The NOS constructs the selected network service based on the network configuration data.

A marketplace system (MPS) controls display of an input screen on which a network service is selectable. The MPS receives from a purchaser terminal to select one or a plurality of network services on the input screen. A network operating system (NOS) generates network configuration data indicating a configuration of the selected network service. The NOS constructs the selected network service based on the network configuration data.

A network operating system (NOS) receives a bundle file transmitted from a vendor terminal of a vendor providing a network service, the bundle file including first data defining a functional unit group that achieves the network service and second data defining a monitoring policy for the network service. The NOS constructs the functional unit group based on the first data of the bundle file when the network service is purchased by a purchaser. The NOS executes a monitoring process on the functional unit group based on information on the functional unit group to be constructed and the second data of the bundle file.

A latency processing system detects traffic at a cloud service end point and analyzes packets in the detected traffic to identify a network configuration of a client that is accessing the cloud service. Latency components corresponding to different parts of the network configuration are identified and quantified. A recommendation engine is controlled to generate and surface an output indicative of recommendations for reducing network latency.

A method for controlling data transmission within a telecommunications network involves providing interconnections to both a core network and to at least one user device via a base station. A configurable network is defined interconnecting the at least one core network and the base station. A first network slice is selected responsive to use of the configurable network by a first application. A second network slice is selected responsive to use of the configurable network by a second application. Data transmission are provided between the core network and the base station over the configurable network based on the selected first or second network slice.

Described herein are systems and methods for work from home solutions according to various embodiments of the invention. These solutions accurately diagnose connectivity issue locations and severity from any, some, or all points in a network framework. Embodiments of these solutions may also use artificial intelligence and machine learning to process customer quality-of-experience (QoE) feedback and other relevant indicators. The solutions may improve network connectivity based on these measurements and processes.