System and method of using a machine learning algorithm to meet SLA requirements
A method includes collecting, at a monitoring and recovery node, a virtual network function key performance index data through multiple channels from a corresponding containerized virtual network function. The method includes maintaining, at the monitoring and recovery node, state information of the corresponding containerized virtual network function and running, at the monitoring and recovery node, 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 and meet the service level agreement of a network service.
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.
2. The method of claim 1 , wherein the collecting and running steps occur at a monitoring and recovery node.
3. The method of claim 1 , further comprising, when the T(s) threshold is met N times, and the determination indicates an action should be taken, providing an instruction to a provisioning node to perform one of the scaling, the healing and the context switching for the corresponding containerized virtual network function.
4. The method of claim 1 , wherein the step of collecting the virtual network function key performance index data occurs through multiple channels.
5. The method of claim 4 , wherein the multiple channels comprise at least two of IPSLA, NETCONF, and SNMP.
6. The method of claim 3 , wherein when the machine learning algorithm predicts that the corresponding containerized virtual network function requires scaling, providing an instruction to the provisioning node to add a new virtual network function instance.
7. The method of claim 6 , wherein the provisioning node adds the new virtual network function instance using a new docker container or a virtual machine.
8. A system comprising:
a processor; and
a computer-readable medium, storing instructions which, when executed by the processor, cause the processor to perform operations 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.
9. The system of claim 8 , wherein the collecting and running steps occur at a monitoring and recovery node.
10. The system of claim 8 , wherein the computer-readable medium stores instructions which, when executed by the processor, cause the processor to perform further operations comprising, when the T(s) threshold for scaling is met N times, and the determination indicates an action should be taken, providing an instruction to a provisioning node to perform one of the scaling, the healing and the context switching for the corresponding containerized virtual network function.
11. The system of claim 8 , wherein the step of collecting the virtual network function key performance index data occurs through multiple channels.
12. The system of claim 11 , wherein the multiple channels comprise at least two of IPSLA, NETCONF, and SNMP.
13. The system of claim 10 , wherein when the machine learning algorithm predicts that the corresponding containerized virtual network function requires scaling, providing an instruction to the provisioning node to add a new virtual network function instance.
14. The system of claim 13 , wherein the provisioning node adds the new virtual network function instance using a new docker container or a virtual machine.
15. A computer-readable storage device storing instructions which, when executed by a processor, cause the processor to perform operations 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.
16. The computer-readable storage device of claim 15 , wherein the collecting and running steps occur at a monitoring and recovery node.
17. The computer-readable storage device of claim 15 , wherein the computer-readable device stores additional instructions which, when executed by the processor, cause the processor to perform further operations comprising, when the T(s) threshold for scaling is met N times, and the determination indicates an action should be taken, providing an instruction to a provisioning node to perform one of the scaling, the healing and the context switching for the corresponding containerized virtual network function.
18. The computer-readable storage device of claim 15 , wherein the step of collecting the virtual network function key performance index data occurs through multiple channels.
19. The computer-readable storage device of claim 18 , wherein the multiple channels comprise at least two of IPSLA, NETCONF, and SNMP.
20. The computer-readable storage device of claim 15 , wherein when the machine learning algorithm predicts that the corresponding containerized virtual network function requires scaling, providing an instruction to the provisioning node to add a new virtual network function instance.