1. A method comprising: collecting a virtual network function key performance index data from a corresponding containerized virtual network function; maintaining state information of the corresponding containerized virtual network function; running a machine learning algorithm that, once trained, learns and predicts whether the corresponding containerized virtual network function requires one of a scaling, a healing or a context switching to sister virtual network function to yield a determination; executing the predicted scaling, the predicted healing, or the predicted context switching of the virtual network function in response to the predictions; wherein the machine learning algorithm comprises: 
 T ( s )=(Σ( M ( v )+ R ( a )))% T ( m ) 
 R ( a )= R vnf /R total <=global median resource usage 
 T ( m )= M ( v ) max +R ( a ) max , where T(s) is a threshold for the scaling, the healing or the context switching to the sister virtual network function for the corresponding containerized virtual network function; M(v) is a metric variable; R(a) comprises an absolute individual resource usage for the corresponding containerized virtual network function out of multiple containerized virtual network functions; R vnf comprises a resource usage for a given virtual network function; R total comprises a total resource usage for a network service comprising a group of virtual network functions; T(m) is a threshold maximum; and Σ comprises a summation from i=1 to N, wherein N is a number of times the threshold T(s) for the scaling, the healing or the context switching has succeeded.