In one embodiment, a method comprises: obtaining, by a process, path trace data collected by a plurality of performance monitoring agents across a computer network; obtaining, by the process, one or more catalogs having application-based correlation information for the path trace data; generating, by the process, network mapping directed graphs by correlating the path trace data using the one or more catalogs, the network mapping directed graphs logically comprising nodes categorized at a plurality of levels of aggregation and edges connecting the nodes; associating, by the process, test-based performance data with the edges of the network mapping directed graphs; and providing, by the process, at least one Sankey diagram based on the network mapping directed graphs and test-based performance data associated with their edges for selectable display by a user interface.

A real-time dynamic API traffic shaping and infrastructure resource protection in a multiclient network environment is provided. A traffic rules engine (TRE) applies traffic shaping only to customers that are utilizing “more than their fair share” of the currently available bandwidth without allowing them to negatively impact the user experience of other users. The present invention takes current API traffic into consideration, allowing one or a few high volume users to utilize most of all available bandwidth as long as other users do not need that bandwidth. This includes dynamically measuring and adjusting which users had traffic shaping applied to them based on the overall traffic during any given second. The solution of the present invention avoids any slowdown of customer API requests unless the maximum allowable TPS limit is near to being reached.

A method for delivering quality of service to a moving user equipment in a computer network, the method including: identifying a moving user equipment on the computer network; predicting a path of travel for the user equipment; determining a load of a cell in the path of travel; determining a traffic action response based on the load of the cell; and providing the traffic action. A system for delivering quality of service to a moving user equipment in a computer network, the system including: a location module configured to identify a moving user equipment on the computer network; an analysis module configured to predict a path of travel for the user equipment; a load module configured to determine a load of a cell in the path of travel; and a traffic action module configured to determine a traffic action response based on the load of the cell and provide for the traffic action.

A method includes partitioning a set of configuration management (CM) data for one or more cellular network devices into multiple distinct time intervals, each time interval associated with a distinct set of CM settings at the one or more cellular network devices, the CM data comprising multiple CM parameters. The method also includes determining a regression model based on the set of CM data. The method also includes applying the regression model to compute a distinct set of scores and compare the set of scores to estimate whether a performance of the one or more cellular network devices has changed during a second time interval relative to a first time interval.

A method includes partitioning a set of configuration management (CM) data for one or more cellular network devices into multiple distinct time intervals, each time interval associated with a distinct set of CM settings at the one or more cellular network devices, the CM data comprising multiple CM parameters. The method also includes determining a regression model based on the set of CM data. The method also includes applying the regression model to compute a distinct set of scores and compare the set of scores to estimate whether a performance of the one or more cellular network devices has changed during a second time interval relative to a first time interval.

In one embodiment, a system for managing a virtualization environment includes a set of host machines, each of which includes a hypervisor, virtual machines, and a virtual machine controller, and a data migration system configured to identify one or more existing storage items stored at one or more existing File Server Virtual Machines (FSVMs) of an existing virtualized file server (VFS). For each of the existing storage items, the data migration system is configured to identify a new FSVMs of a new VFS based on the existing FSVM, send a representation of the storage item from the existing FSVM to the new FSVM, such that representations of storage items are sent between different pairs of FSVMs in parallel, and store a new storage item at the new FSVM, such that the new storage item is based on the representation of the existing storage item received by the new FSVM.

There are provided a method of managing radio connectivity of a vehicle and a system thereof. The method comprises: continuously receiving by vehicle's telematic system a predictive model generated by remote system using data continuously collected from a plurality of vehicles, wherein the collected data comprise, for each given vehicle of the plurality of vehicles, data informative of its location, speed and of Radio Access Technology (RAT)-related measurements; and, responsive to a predefined event, applying, by the telematic system, a lastly received predictive model to current values of a predefined set of inputs associated with the vehicle to obtain instructions and respectively provide corrective actions related to radio connectivity of the vehicle. The corrective actions include modifying RRC measurement report(s) so as to force the cellular network to provide one of: intra-RAT handover, inter-RAT handover; excluding available connectivity with undesired RAT or band; and terminating the radio connectivity with further RAT re-selecting.

There are provided a method of managing radio connectivity of a vehicle and a system thereof. The method comprises: continuously receiving by vehicle's telematic system a predictive model generated by remote system using data continuously collected from a plurality of vehicles, wherein the collected data comprise, for each given vehicle of the plurality of vehicles, data informative of its location, speed and of Radio Access Technology (RAT)-related measurements; and, responsive to a predefined event, applying, by the telematic system, a lastly received predictive model to current values of a predefined set of inputs associated with the vehicle to obtain instructions and respectively provide corrective actions related to radio connectivity of the vehicle. The corrective actions include modifying RRC measurement report(s) so as to force the cellular network to provide one of: intra-RAT handover, inter-RAT handover; excluding available connectivity with undesired RAT or band; and terminating the radio connectivity with further RAT re-selecting.

A master node, a local node, a service assurance system, and a respective method performed thereby for predicting one or more metrics associated with a communication network are provided. The method performed by the master node operable in the communication network comprises receiving prediction(s) based on training data from local nodes in the communication network; and determining weight parameter(s) associated with the local nodes based on the current received prediction(s) and past received predictions. The method further comprises adjusting a respective local reporting policy for one or more local nodes based on the determined weight parameter(s).

Systems and methods for providing a guaranteed batch pool are described, including receiving a job request for execution on the pool of resources; determining an amount of time to be utilized for executing the job request based on available resources from the pool of resources and historical resource usage of the pool of resources; determining a resource allocation from the pool of resources, wherein the resource allocation spreads the job request over the amount of time; determining that the job request is capable of being executed for the amount of time; and executing the job request over the amount of time, according to the resource allocation.