LOAD DISTRIBUTION IN DATA NETWORKS

• US 20140089500A1
• Filed: 09/17/2013
• Published: 03/27/2014
• Est. Priority Date: 09/25/2012
• Status: Active Grant

• Current Assignee: A10 Networks Incorporated
• Original Assignee: A10 Networks Incorporated
• Inventors: Sankar, Swaminathan, Sampat, Rishi, Jalan, Rajkumar, Karampurwala, Hasnain, Gupta, Rahul, Kamat, Gurudeep
• Granted Patent: US 9,705,800 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: H04L 47/125 by balancing the load, e.g. traffic engineering
• CPC Class Codes: H04L 67/101 based on network conditions
• CPC Class Codes: H04L 67/1012 based on compliance of requirements or conditions with available server resources
• CPC Class Codes: H04L 67/1025 Dynamic adaptation of the criteria on which the server selection is based
• CPC Class Codes: H04L 67/1031 Controlling of the operation of servers by a load balancer, e.g. adding or removing servers that serve requests

Abstract

Provided are methods and systems for load distribution in a data network. A method for load distribution in the data network may comprise retrieving network data associated with the data network and service node data associated with one or more service nodes. The method may further comprise analyzing the retrieved network data and service node data. Based on the analysis, a service policy may be generated. Upon receiving one or more service requests, the one or more service requests may be distributed among the service nodes according to the service policy.

Claims

1. A method for service load distribution in a data network, the method comprising:

generating a service policy;

providing the service policy to devices associated with the data network; and

distributing one or more service requests according to the service policy.

2. The method of claim 1, further comprising:

retrieving network data associated with the data network;

retrieving service node data associated with one or more service nodes;

analyzing the network data and the service node data;

generating a service policy based on the analysis of the network data and the service node data;

receiving one or more service requests; and

distributing the one or more service requests among the one or more service nodes according to the service policy;

3. The method of claim 2, further comprising pushing the service policy to devices associated with the data network, wherein the device associated with the data network include traffic classification engines and the service nodes.

4. The method of claim 2, wherein the network data includes at least one of health of a service node, a number of total connections, processing unit utilization, a memory status, backend server capacity, and network connectivity.

5. The method of claim 2, wherein the service node data includes at least one of a dynamic state, node processing unit utilization, a node memory status, and responsiveness of the one or more service nodes.

6. The method of claim 2, further comprising developing a further service policy based on the analysis, wherein the further service policy is associated with scaling up, scaling down, remedying, and removing one or more service nodes, traffic classification engines and backend servers, and introducing one or more new service nodes, traffic classification engines and backend servers.

7. The method of claim 1, wherein the devices associated with the data network include the service nodes and traffic classification engines.

8. The method of claim 1, wherein the data network includes a software driven network (SDN), the SDN comprising at least one of traffic classification engines, service nodes, and application delivery controllers.

9. The method of claim 1, wherein the service policy includes at least one of a traffic distribution policy and a service node load policy.

10. The method of claim 2, wherein the one or more service nodes include a virtual machine and a physical device.

11. The method of claim 1, further comprising:

facilitating an application programmable interface to a network administrator; and

developing a further service policy based on the analysis, the further service policy being for the network administrator via the application programmable interface.

12. The method of claim 1, further comprising performing a health check of a backend server by the devices associated with the data network.

13. The method of claim 1, further comprising scaling up service nodes, backend servers, traffic classification engines, cluster masters, and other devices in the SDN network while minimizing disruption to traffic flow.

14. The method of claim 1, further comprising scaling down service nodes, backend servers, traffic classification engines, cluster masters, and other devices in the SDN network while minimizing disruption to traffic flow.

15. The method of claim 1, further comprising scaling up and scaling down services while minimizing disruption to traffic flow.

16. The method of claim 6, wherein when a service node is scaled up or scaled down, the one or more service requests are redirected to the one or more service nodes to continue processing data associated with the service request.

17. The method of claim 2, further comprising optimizing reverse traffic from backend servers to the one or more service nodes.

18. The system of claim 1, further comprising an orchestrator that:

detects the one or more service nodes; and

transmits data associated with the one or more service nodes to the cluster master.

19. The system of claim 1, further comprising a network database that:

stores data associated with at least one of the cluster master, the traffic classification engine, the one or more service nodes, backend servers, and service policies; and

allows the data to be shared among the cluster master, the traffic classification engine, and the one or more service nodes.

20. The system of claim 2, further comprising an orchestrator that:

detects the one or more service nodes; and

transmits data associated with the one or more service nodes to the cluster master.

21. The system of claim 2, further comprising a network database that:

stores data associated with at least one of the cluster master, the traffic classification engine, the one or more service nodes, backend servers, and service policies; and

allows the data to be shared among the cluster master, the traffic classification engine, and the one or more service nodes.

22. A system for service load distribution in a data network, the system comprising:

a cluster master that;

retrieves network data associated with the data network;

retrieves service node data associated with one or more service nodes;

analyzes the network data and the service node data;

generates a service policy based on the analysis; and

provides the service policy to devices associated with the data network;

one or more service nodes that receive the service policy; and

a traffic classification engine that;

receives the service policy;

receives one or more service requests; and

distributes the one or more service requests among the one or more service nodes according to the service policy, the service nodes processing the one or more service requests according to the service policy.

23. The system of claim 22, wherein the devices associated with the data network include the service nodes and traffic classification engines.

24. The system of claim 23, wherein the service nodes and the traffic classification engines act as a cluster master, and wherein the cluster master and the service nodes act as a traffic classification engine.

25. The system of claim 22, further comprising an orchestrator that:

detects the one or more service nodes; and

transmits data associated with the one or more service nodes to the cluster master.

26. The system of claim 22, further comprising a network database that:

stores data associated with at least one of the cluster master, the traffic classification engine, the one or more service nodes, backend servers, and service policies; and

allows the data to be shared among the cluster master, the traffic classification engine, and the one or more service nodes.

27. The system of claim 26, wherein the stored data includes the network data and the service node data.

28. The system of claim 22, wherein the processing of the one or more service requests includes forwarding the one or more service requests to one or more backend servers.

29. The system of claim 22, wherein the one or more service nodes are further configured to send the service node data to the cluster master.

30. The system of claim 22, wherein the network data includes at least one of a number of total connections, processing unit utilization, a memory status, and network connectivity.

31. The system of claim 22, wherein the service node data includes at least one of a health of the service node, a dynamic state, and responsiveness of the one or more service nodes.

32. The system of claim 22, wherein the service policy includes at least one of service address, service node address, a traffic distribution policy, and a service node load policy.

33. The system of claim 22, wherein the cluster master includes a backup unit configured to replace the cluster master in case of a failure of the cluster master.

34. The system of claim 22, wherein the cluster master performs at least one of:

developing a further service policy based on the analysis, wherein the further service policy is associated with scaling down, scaling up, remedying, and removing services associated with the one or more service nodes, and introducing a new service associated with the one or more service nodes; and

facilitating provision of an application programmable interface to a network administrator to enable the network administrator to develop, based on the analysis, a further service policy.

35. The system of claim 22, further comprising performing a health check of a backend server by the devices associated with the data network.

36. The system of claim 22, further comprising scaling up service nodes, backend servers, traffic classification engines, cluster masters, and other devices in the SDN network while minimizing disruption to the traffic flow.

37. The system of claim 22, further comprising scaling down the service nodes, backend servers, traffic classification engines, cluster masters, and other devices in the SDN network while minimizing disruption to traffic flow.

38. The system of claim 22, further comprising scaling up and scaling down services while minimizing disruption to traffic flow.

39. The method of claim 34, wherein when services associated with a service node are scaled up or scaled down, the one or more service requests are redirected to the one or more service nodes to continue processing data associated with the service request.

40. The method of claim 22, further comprising optimizing reverse traffic from backend servers to the one or more service nodes.

41. A non-transitory processor-readable medium having instructions stored thereon, which when executed by one or more processors, cause the one or more processors to perform the following operations:

retrieving network data associated with a data network;

retrieving service node data associated with one or more service nodes;

analyzing the network data and the service node data;

generating a service policy;

receiving one or more service requests;

providing the service policy to devices associated with the data network;

distributing the one or more service requests among the one or more service nodes according to the service policy;

developing a further service policy based on the analysis, wherein the further service policy is associated with scaling up, scaling down, remedying, and removing services associated with the one or more service nodes, and introducing a new service associated with the one or more service nodes;

facilitating providing an application programmable interface to a network administrator;

developing a further service policy based on the analysis by the network administrator via the application programmable interface;

performing a health check of a backend server by the devices associated with the data network;

scaling up and scaling down at least one of service nodes, backend servers, traffic classification engines, and cluster masters while minimizing disruption to traffic flow;

scaling up and scaling down services while minimizing disruption to the traffic flow;

optimizing reverse traffic from backend servers to the service node; and

redirecting the one or more service requests to the one or more service nodes to continue processing data associated with the service request when at least one service node has been scaled up or down.