Dynamic configuration of network features

• US 10,581,680 B2
• Filed: 11/25/2015
• Issued: 03/03/2020
• 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.
• Primary Examiner(s): Nguyen, Angela
• Application Number:
  US14/952,437
• Publication Number:
  US 20170149608A1
• Time in Patent Office: 1,560 Days
• Field of Search: None
• 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 in communication with an elastic cloud computing network across a network connection, real-time traffic analysis on network traffic flowing though the network connection between the elastic cloud computing network and the edge appliance as t appliance as the edge appliance and elastic cloud computing network communicate with each other through the network connection, the real-time traffic analysis comprising analysis of application data transported as part of the network traffic, wherein a network service provider of the elastic cloud computing network provides an initial allocated amount of elastic network bandwidth for the network connection between the edge appliance and the elastic cloud computing network;

evaluating an effect of modifying elastic network bandwidth allocation from the network service provider of the elastic cloud computing network to change the amount of elastic network bandwidth being provided by the network service provider for the network connection from the initial amount being provided to a different amount, and an effect of applying network traffic optimizations in routing traffic flowing between the elastic cloud computing network and the edge appliance; and

based on the real-time traffic analysis, the evaluating, and on comparing the effect of modifying elastic network network bandwidth allocation from the network service provider with the effect of applying network traffic optimizations, dynamically configuring at least one selected from the group consisting of;

(i) elastic network bandwidth allocation from the network service provider, and (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 1, wherein the dynamically configuring comprises dynamically configuring the at least one network traffic optimization, the at least one network traffic optimization comprising at least one selected from the group consisting of:

(i) TCP optimization, including local acknowledgements of traffic receipt, and (ii) data compression.

4. 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.

5. 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.

6. The method of claim 1, wherein the application data is transported as part of application data flow traffic through the edge appliance to another device, the edge appliance sitting between the another device and the elastic cloud computing network in a communication path between the elastic cloud computing network and the another device.

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

8. The method of claim 3, 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.

9. The method of claim 8, wherein the evaluating the effect of applying the data compression comprises a consideration of at least one selected from the group consisting of:

(i) data rate, and (ii) amount of time to compress data.

10. The method of claim 3, 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 selected at least one of TCP optimization and the data compression to the one or more of the several distinct application data flows.

11. The method of claim 5, wherein the edge appliance comprises additional virtualized processes for firewall functionality and routing functionality.

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 through a network connection between an elastic cloud computing network and an edge appliance, in communication with the elastic cloud computing network and configured to access the elastic cloud computing network, as the edge appliance and elastic cloud computing network communicate with each other through the network connection, the real-time traffic analysis comprising analysis of application data transported as part of the network traffic, wherein a network service provider of the elastic cloud computing network provides an initial allocated amount of elastic network bandwidth for the network connection between the edge appliance and the elastic cloud computing network;

evaluating an effect of modifying elastic network bandwidth allocation from the network service provider of the elastic cloud computing network to change the amount of elastic network bandwidth being provided by the network service provider for the network connection from the initial amount being provided to a different amount, and an effect of applying network traffic optimizations in routing traffic flowing between the elastic cloud computing network and the edge appliance; and

based on the real-time traffic analysis, the evaluating, and on comparing the effect of modifying elastic network bandwidth allocation from the network service provider with the effect of applying network traffic optimizations, dynamically configuring at least one selected from the group consisting of;

(i) elastic network bandwidth allocation from the network service provider, and (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 at least one selected from the group consisting of:

(i) TCP optimization, including local acknowledgements of traffic receipt, and (ii) data compression.

15. 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.

16. 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 selected at least one of the TCP optimization and the data compression to the one or more of the several distinct application data flows.

17. A computer system comprising:

a memory; and

a processor in communication 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 through a network connection between an elastic cloud computing network and an edge appliance, in communication with the elastic cloud computing network and configured to access the elastic cloud computing network, as the edge appliance and elastic cloud computing network communicate with each other through the network connection, the real-time traffic analysis comprising analysis of application data transported as part of the network traffic, wherein a network service provider of the elastic cloud computing network provides an initial allocated amount of elastic network bandwidth for the network connection between the edge appliance and the elastic cloud computing network;

evaluating an effect of modifying elastic network bandwidth allocation from the network service provider of the elastic cloud computing network to change the amount of elastic network bandwidth being provided by the network service provider for the network connection from the initial amount being provided to a different amount, and an effect of applying network traffic optimizations in routing traffic flowing between the elastic cloud computing network and the edge appliance; and

based on the real-time traffic analysis, the evaluating, and on comparing the effect of modifying elastic network bandwidth allocation from the network service provider with the effect of applying network traffic optimizations, dynamically configuring at least one selected from the group consisting of;

(i) elastic network bandwidth allocation from the network service provider, and (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 at least one selected from the group consisting of:

(i) TCP optimization, including local acknowledgements of traffic receipt, and (ii) 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 selected at least one of the TCP optimization and the data compression to the one or more of the several distinct application data flows.