Predictive connectivity service layers

Abstract

A system and method is provided for a predictive connectivity layer. In the disclosed embodiments, resources, such as bandwidth, processing, and memory, at a network node are dynamically allocated based on one or more predicted user behaviors. A predicted user behavior may be determined based on one or more previous actions of a user or a group of users at the network node. For example, if a user accesses the network node to download a particular web site at the same time every morning, the predictive technique may determine that the user will attempt to download the same web site the next morning, and therefore cache a copy of the web site before the user's next attempt to access the network through the network node, Similarly, the network node may predict an amount of bandwidth or other resources to allocate based on previous behavior of one or more users.

1 - 34 . (canceled) 
     
     
         35 . A method for predictive learning in a network node of a network, the method comprising:
 receiving information relating to one or more previous actions of a user when the user is accessing the network through the network node;   predicting a user behavior based on the one or more previous actions of the user when the user is accessing the network through the network node; and   populating user access information in the network node based on the predicted user behavior upon the user's attempt to access the network through the network node.   
     
     
         36 . The method of  claim 35 , further comprising pre-populating the user access information in the network node based on the predicted user behavior before the user's next attempt to access the network through the network node. 
     
     
         37 . The method of  claim 35 , further comprising using a user content caching (UCC) service to cache the predicted user behavior such that the cached predicted user behavior is accessible to the network node of the network. 
     
     
         38 . The method of  claim 37 , wherein the UCC includes a distributed portion configured to execute at the network node and a cloud portion configured to execute on a cloud platform. 
     
     
         39 . The method of  claim 38 , wherein the cloud portion of the UCC includes predictive learning algorithms used to predict the user behavior, and wherein the predictive learning algorithms are configured to communicate with other services in the cloud platform. 
     
     
         40 . The method of  claim 39 , wherein the predictive learning algorithms are further configured to alter network configurations based on the communication with other services in the cloud platform. 
     
     
         41 . The method of  claim 35 , wherein the network node includes an enterprise server configured to provide an enterprise cloud-based service. 
     
     
         42 . The method of  claim 41 , wherein the enterprise cloud-based service comprises a distributed portion configured to execute at the network node and a corresponding cloud portion configured to execute on a cloud platform. 
     
     
         43 . The method of  claim 41 , wherein the enterprise server is configured to communicate with at least one of an access point, a user equipment, or one or more sensors in the network. 
     
     
         44 . The method of  claim 35 , wherein the user access information includes at least one of an updated authentication rule or an updated policy information. 
     
     
         45 . The method of  claim 44 , wherein the updated policy information includes a Quality of Service (QoS) level for one or more users. 
     
     
         46 . The method of  claim 44 , wherein populating user access information comprises populating the updated authentication rule to pre-authenticate the user. 
     
     
         47 . The method of  claim 44 , wherein the updated policy information is associated with one or more users and is indicative of the one or more users' permission to access the network node. 
     
     
         48 . The method of  claim 35 , further comprising altering at least one QoS parameter at one or more network nodes based on the predicted user behavior and including the at least one altered QoS parameter in the user access information. 
     
     
         49 . The method of  claim 44 , further comprising altering at least one of the updated authentication rule or the updated policy information to restrict network access for one or more users at one or more network nodes based on the predicted user behavior. 
     
     
         50 . The method of  claim 35 , wherein one or more network resources are allocated in the network node based on the predicted user behavior. 
     
     
         51 . The method of  claim 35 , further comprising altering at least one of capacity parameter, bandwidth allocation, RF parameter, or mobility parameter at one or more network nodes based on the predicted user behavior. 
     
     
         52 . A network node for predictive learning in a network, the network node comprising:
 a processor;   a memory configured to store computer-readable instructions for execution by the processor, the instructions for performing the steps of:
 receiving information relating to one or more previous actions of a user when the user is accessing the network through the network node; 
 predicting a user behavior based on the one or more previous actions of the user when the user is accessing the network through the network node; and 
 populating user access information in the network node based on the predicted user behavior upon the user's attempt to access the network through the network node. 
   
     
     
         53 . The network node of  claim 52 , wherein the memory further comprises instructions executable by the processor for pre-populating the user access information in the network node based on the predicted user behavior before the user's next attempt to access the network through the network node. 
     
     
         54 . The network node of  claim 53 , wherein the memory further comprises instructions executable by the processor for using a user content caching (UCC) service to cache the predicted user behavior such that the cached predicted user behavior is accessible to the network node of the network. 
     
     
         55 . The network node of  claim 54 , wherein the UCC includes a distributed portion configured to execute at the network node and a cloud portion configured to execute on a cloud platform. 
     
     
         56 . The network node of  claim 55 , wherein the cloud portion of the UCC includes predictive learning algorithms used to predict the user behavior, and wherein the predictive learning algorithms are configured to communicate with other services in the cloud platform. 
     
     
         57 . The network node of  claim 56 , wherein the predictive learning algorithms are further configured to alter network configurations based on the communication with other services in the cloud platform. 
     
     
         58 . The network node of  claim 52 , wherein the network node includes an enterprise server configured to provide an enterprise cloud-based service. 
     
     
         59 . The network node of  claim 58 , wherein the enterprise cloud-based service comprises a distributed portion configured to execute at the network node and a corresponding cloud portion configured to execute on a cloud platform. 
     
     
         60 . The network node of  claim 58 , wherein the enterprise server is configured to communicate with at least one of an access point, a user equipment, or one or more sensors in the network. 
     
     
         61 . The network node of  claim 52 , wherein the user access information includes at least one of an updated authentication rule or an updated policy information. 
     
     
         62 . The network node of  claim 61 , wherein the updated policy information includes a Quality of Service (QoS) level for one or more users. 
     
     
         63 . The network node of  claim 61 , wherein populating user access information comprises populating the updated authentication rule to pre-authenticate the user. 
     
     
         64 . The network node of  claim 61 , wherein the updated policy information is associated with one or more users and is indicative of the one or more users' permission to access the network node. 
     
     
         65 . The network node of  claim 52 , wherein the memory further comprises instructions executable by the processor for altering at least one QoS parameter at one or more network nodes based on the predicted user behavior and including the at least one altered QoS parameter in the user access information. 
     
     
         66 . The network node of  claim 61 , wherein the memory further comprises instructions executable by the processor for altering at least one of the updated authentication rule or the updated policy information to restrict network access for one or more users at one or more network nodes based on the predicted user behavior. 
     
     
         67 . The network node of  claim 52 , wherein one or more network resources are allocated in the network node based on the predicted user behavior. 
     
     
         68 . The network node of  claim 52 , wherein the memory further comprises instructions executable by the processor for altering at least one of capacity parameter, bandwidth allocation, RF parameter, or mobility parameter at one or more network nodes based on the predicted user behavior. 
     
     
         69 . A non-transitory computer-readable medium comprising computer-readable instructions for execution by a processor in a network node that performs a method for predictive learning in a network, the method comprising:
 receiving information relating to one or more previous actions of a user when the user is accessing the network through the network node;   predicting a user behavior based on the one or more previous actions of the user when the user is accessing the network through the network node; and   populating user access information in the network node based on the predicted user behavior upon the user's attempt to access the network through the network node.