DYNAMIC CONFIGURATION OF NETWORK FEATURES

• US 20170149608A1
• Filed: 11/25/2015
• Published: 05/25/2017
• Est. Priority Date: 11/25/2015
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: FREIMUTH, Douglas M., HOLLINGSWORTH, John F., MANDALIA, Baiju D.
• Granted Patent: US 10,581,680 B2
• Time in Patent Office: Days
• CPC Class Codes: H04L 41/0816 the condition being an adaptation, e.g. in response to network events
• CPC Class Codes: H04L 41/0823 characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability for optimising operational conditions of wireless networks H04W24/02
• CPC Class Codes: H04L 41/0826 for reduction of network costs H04L41/0833 takes precedence
• CPC Class Codes: H04L 41/083 for increasing network speed
• CPC Class Codes: H04L 41/0895 Configuration of virtualised networks or elements, e.g. virtualised network function or OpenFlow elements
• CPC Class Codes: H04L 41/0896 Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76
• CPC Class Codes: H04L 43/08 Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 43/0829 Packet loss
• CPC Class Codes: H04L 43/0852 Delays
• CPC Class Codes: H04L 43/087 Jitter
• CPC Class Codes: H04L 47/11 Identifying congestion

Abstract

Dynamic configuration of network features is provided by performing real-time traffic analysis on network traffic flowing between an elastic cloud computing network and an edge appliance, evaluating effects of modifying elastic network bandwidth allocation and applying network traffic optimizations in routing traffic flowing between the elastic cloud computing network and the edge appliance, and dynamically configuring, based on the real-time traffic analysis and on the evaluating, one or more of (i) elastic network bandwidth allocation from the network service provider or (ii) at least one of the network traffic optimizations for performance by the edge appliance in routing traffic flowing between the elastic cloud computing network and the edge appliance.

Claims

1. A computer-implemented method comprising:

performing, by an edge appliance configured to access an elastic cloud computing network, real-time traffic analysis on network traffic flowing between the elastic cloud computing network and the edge appliance, the real-time traffic analysis comprising analysis of application data transported as part of the network traffic;

evaluating an effect of modifying elastic network bandwidth allocation from a network service provider of the elastic cloud computing network, and an effect of applying network traffic optimizations in routing traffic flowing between the elastic cloud computing network and the edge appliance; and

dynamically configuring, based on the real-time traffic analysis and on the evaluating, one or more of (i) elastic network bandwidth allocation from the network service provider or (ii) at least one network traffic optimization, of the network traffic optimizations, for performance by the edge appliance in routing traffic flowing between the elastic cloud computing network and the edge appliance.

2. The method of claim 1, wherein the evaluating the effect of modifying the elastic network bandwidth allocation comprises a consideration of bandwidth costs for different available bandwidth levels at different times of day.

3. The method of claim 2, wherein the dynamically configuring comprises dynamically configuring the elastic network bandwidth allocation to increase bandwidth of the elastic network.

4. The method of claim 1, wherein the dynamically configuring comprises dynamically configuring the at least one network traffic optimization, the at least one network traffic optimization comprising one or more of TCP optimization, including local acknowledgements of traffic receipt, or data compression.

5. The method of claim 4, wherein the evaluating the effect of applying network traffic optimizations comprises evaluating an effect of applying the TCP optimization and an effect of applying the data compression.

6. The method of claim 5, wherein the evaluating the effect of applying the data compression comprises a consideration of one or more of data rate or amount of time to compress data.

7. The method of claim 4, wherein the application data is exchanged as part of several distinct application data flows for several applications, wherein the real-time traffic analysis comprises applying analytics against the several distinct application data flows in the network traffic flowing between the elastic cloud computing network and the edge appliance, and wherein the dynamically configuring comprises determining one or more of the several distinct application data flows that would benefit from optimization and applying the one or more of TCP optimization or data compression to the one or more of the several distinct application data flows.

8. The method of claim 1, wherein the dynamically configuring is performed according to edge appliance policies configured by a user, the edge appliance policies setting parameters on determinations to configure the elastic network bandwidth allocation and the network traffic optimizations.

9. The method of claim 1, wherein the performing real-time traffic analysis, the evaluating, and the dynamically configuring are delivered as one or more virtualized processes executing on the edge appliance.

10. The method of claim 9, wherein the edge appliance comprises additional virtualized processes for firewall functionality and routing functionality.

11. The method of claim 1, further comprising periodically or aperiodically repeating the performing real-time traffic analysis and the evaluating to dynamically determine and apply reconfigurations of one or more of the elastic network bandwidth allocation or network traffic optimization.

12. A computer program product comprising:

a computer readable storage medium readable by a processor and storing instructions for execution by the processor for performing a method comprising;

performing real-time traffic analysis on network traffic flowing between an elastic cloud computing network and an edge appliance configured to access the elastic cloud computing network, the real-time traffic analysis comprising analysis of application data transported as part of the network traffic;

evaluating an effect of modifying elastic network bandwidth allocation from a network service provider of the elastic cloud computing network, and an effect of applying network traffic optimizations in routing traffic flowing between the elastic cloud computing network and the edge appliance; and

dynamically configuring, based on the real-time traffic analysis and on the evaluating, one or more of (i) elastic network bandwidth allocation from the network service provider or (ii) at least one network traffic optimization, of the network traffic optimizations, for performance by the edge appliance in routing traffic flowing between the elastic cloud computing network and the edge appliance.

13. The computer program product of claim 12, wherein the evaluating the effect of modifying the elastic network bandwidth allocation comprises a consideration of bandwidth costs for different available bandwidth levels at different times of day, and wherein the dynamically configuring comprises dynamically configuring the elastic network bandwidth allocation to increase bandwidth of the elastic network.

14. The computer program product of claim 12, wherein the dynamically configuring comprises dynamically configuring the at least one network traffic optimization, the at least one network traffic optimization comprising one or more of TCP optimization, including local acknowledgements of traffic receipt, or data compression.

15. The computer program product of claim 14, wherein the application data is exchanged as part of several distinct application data flows for several applications, wherein the real-time traffic analysis comprises applying analytics against the several distinct application data flows in the network traffic flowing between the elastic cloud computing network and the edge appliance, and wherein the dynamically configuring comprises determining one or more of the several distinct application data flows that would benefit from optimization and applying the one or more of TCP optimization or data compression to the one or more of the several distinct application data flows.

16. The computer program product of claim 12, wherein the performing real-time traffic analysis, the evaluating, and the dynamically configuring are delivered as one or more virtualized processes executing on the edge appliance.

17. A computer system comprising:

a memory; and

a processor in communications with the memory, wherein the computer system is configured to perform a method, the method comprising;

performing real-time traffic analysis on network traffic flowing between an elastic cloud computing network and an edge appliance configured to access the elastic cloud computing network, the real-time traffic analysis comprising analysis of application data transported as part of the network traffic;

evaluating an effect of modifying elastic network bandwidth allocation from a network service provider of the elastic cloud computing network, and an effect of applying network traffic optimizations in routing traffic flowing between the elastic cloud computing network and the edge appliance; and

dynamically configuring, based on the real-time traffic analysis and on the evaluating, one or more of (i) elastic network bandwidth allocation from the network service provider or (ii) at least one network traffic optimization, of the network traffic optimizations, for performance by the edge appliance in routing traffic flowing between the elastic cloud computing network and the edge appliance.

18. The computer system of claim 17, wherein the evaluating the effect of modifying the elastic network bandwidth allocation comprises a consideration of bandwidth costs for different available bandwidth levels at different times of day, and wherein the dynamically configuring comprises dynamically configuring the elastic network bandwidth allocation to increase bandwidth of the elastic network.

19. The computer system of claim 17, wherein the dynamically configuring comprises dynamically configuring the at least one network traffic optimization, the at least one network traffic optimization comprising one or more of TCP optimization, including local acknowledgements of traffic receipt, or data compression.

20. The computer system of claim 19, wherein the application data is exchanged as part of several distinct application data flows for several applications, wherein the real-time traffic analysis comprises applying analytics against the several distinct application data flows in the network traffic flowing between the elastic cloud computing network and the edge appliance, and wherein the dynamically configuring comprises determining one or more of the several distinct application data flows that would benefit from optimization and applying the one or more of TCP optimization or data compression to the one or more of the several distinct application data flows.