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.

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.

A method of abstracting network resources in a mobile communications network includes: determining a service coverage area for a class of service, the class of service defined by service parameters; determining a set of tracking areas that fall at least partly within the service coverage area; selecting available network resources for tracking areas of the set of tracking areas, for providing the class of service in the tracking areas; defining an abstraction view of the selected network resources for the class of service in the service coverage area, the abstraction view having deliverable values of the service parameters within the set of tracking areas; and outputting a communication signal having an indication of the abstraction view.

A service level adjustment method includes obtaining by a first network device at least one piece of queue status information at a target service level of the first network device; when any one of the at least one piece of queue status information exceeds a first threshold upper limit corresponding to the any queue status information, adjusting by the first network device a parameter of the target service level based on a maximum delay associated with the target service level; when the queue status information at the target service level exceeds the corresponding threshold upper limit, adjusting by the first network device the parameter of the target service level based on the associated maximum delay.

In a 5G network, a profiler component of a network slice controller is arranged to dynamically observe behaviors of pre-defined types of network slices when handling current traffic. The profiler employs the observed behaviors to generate profiles of the pre-defined slice types in terms of throughput, reliability, or other suitable metrics. In response to a request from an application for admission to the 5G network for which an ID of an appropriate pre-defined network slice type is unknown, the application request and traffic is handled on a slice which is temporarily utilized while the profiler dynamically observes application behaviors to generate an application profile. The profiler identifies a pre-defined slice type having a profile that is the closest match to the generated application profile. The application may then be moved from the temporary slice to a slice of the identified pre-defined type so that optimal slice characteristics are provided for the application's traffic.

In a 5G network, a slice controller is arranged to dynamically configure a radio access network (RAN) by allocating physical radio resources into RAN slices by making predictions of channel state information (CSI) for user equipment (UE) executing applications that make connectivity requests for admission to particular identified slices. The slice controller grants or denies admission requests based on the predicted CSI to ensure that applicable service level agreement (SLA) guarantees are satisfied for traffic across all the RAN slices. Each time new admission requests are received from applications, the slice controller determines whether a suitable RAN configuration exists that will enable SLA guarantees for the slices to continue to be satisfied for the current traffic while also meeting the SLA guarantees applicable to the new admission request.

In one embodiment, a supervisory service for a network obtains quality of experience metrics for application sessions of an online application. The supervisory service maps the application sessions to paths that traverse a plurality of autonomous systems. The supervisory service identifies, based in part on the quality of experience metrics, a particular autonomous system from the plurality of autonomous systems associated with a decreased quality of experience for the online application. The supervisory service causes application traffic for the online application to avoid the particular autonomous system.

A method of identifying a network condition between a pair of network devices, wherein one of the devices comprises a jitter buffer for storing packets received via a network, the method comprising: monitoring a measure of delay in receiving media packets over the network; monitoring a size of the jitter buffer; and identifying a network condition in dependence on a change in the measure of delay and a variation in the size of the jitter buffer.

A method for a multi-cluster warehouse includes allocating processing units as part of a data warehouse. The processing units access data within one or more databases in cloud storage resources. The method also includes providing one or more queries to each processing unit within the data warehouse. In response to the queries, each processing unit performs database operations on a particular portion of a database table. The method also includes monitoring a workload of the processing units to determine that a processing capacity of the processing units has reached a threshold processing capacity. The method also includes dynamically adding additional processing units to and removing processing units from the data warehouse as needed based on a workload of the processing units.

A method for a multi-cluster warehouse includes allocating processing units as part of a data warehouse. The processing units access data within one or more databases in cloud storage resources. The method also includes providing one or more queries to each processing unit within the data warehouse. In response to the queries, each processing unit performs database operations on a particular portion of a database table. The method also includes monitoring a workload of the processing units to determine that a processing capacity of the processing units has reached a threshold processing capacity. The method also includes dynamically adding additional processing units to and removing processing units from the data warehouse as needed based on a workload of the processing units.