Learning information associated with shaping resources and virtual machines of a cloud computing environment
US-2015263944-A1 · Sep 17, 2015 · US
US9553813B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9553813-B2 |
| Application number | US-201414338823-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jul 23, 2014 |
| Priority date | Jul 23, 2014 |
| Publication date | Jan 24, 2017 |
| Grant date | Jan 24, 2017 |
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In one embodiment, a device in a network identifies a set of one or more destination addresses for which traffic shaping is to be performed by controlling the data rate at which traffic is sent to the one or more destination addresses. The device sends the traffic to one of the destination addresses along a communication path in the network and at a particular data rate. The device identifies a change in a performance characteristic for the communication path. The device adjusts the data rate at which the traffic is sent along the communication path, in response to identifying the change in the performance characteristic for the communication path.
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What is claimed is: 1. A method comprising: maintaining, by a device in a network, a list of a set of one or more destination addresses which require traffic shaping to be performed by controlling the data rate at which traffic is sent to the one or more destination addresses; sending, by the device, the traffic to one of the destination addresses along a communication path in the network and at a particular data rate; identifying, by the device, a change in a performance characteristic for the communication path, wherein the communication path is a particular communication path; and in response to identifying the change in the performance characteristic for the communication path, performing, by the device, the traffic shaping on a per-destination basis by adjusting the data rate at which the traffic is sent along the communication path. 2. The method as in claim 1 , wherein the change in the performance characteristic for the communication path is predicted by a machine learning process. 3. The method as in claim 1 , wherein the change in the performance characteristic for the communication path is an observed change in the communication path. 4. The method as in claim 1 , wherein the destination address is associated with a rule that controls when the data rate is to be adjusted, wherein the rule specifies at least one of: a time period in which the data rate is to be adjusted, a traffic class that corresponds to the sent traffic, or a traffic coloring that corresponds to the sent traffic. 5. The method as in claim 1 , wherein the one or more destination addresses are identified by the device as top communication destinations used by the device. 6. The method as in claim 1 , wherein the performance characteristic corresponds to one or more of: a delay along the path, an amount of jitter along the path, or an amount of dropped packets along the path. 7. The method as in claim 1 , wherein identifying the change in the performance characteristic of the communication path comprises: determining a degree of variability between the performance characteristic and a history of observed performance characteristics of the communication path. 8. The method as in claim 7 , wherein the degree of variability is determined using a clustering model. 9. The method as in claim 1 , wherein the data rate is adjusted as part of a closed-loop control mechanism that monitors the performance characteristic as feedback for the adjustment. 10. The method as in claim 9 , further comprising: providing data regarding the data rate adjustment to a network controller or to a network management system. 11. An apparatus, comprising: one or more network interfaces to communicate with a network; a processor coupled to the network interfaces and configured to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed operable to: maintain a list of a set of one or more destination addresses which require traffic shaping to be performed by controlling the data rate at which traffic is sent to the one or more destination addresses; send the traffic to one of the destination addresses along a communication path in the network and at a particular data rate; identify a change in a performance characteristic for the communication path, wherein the communication path is a particular communication path; and, in response to identifying the change in the performance characteristic for the communication path, perform the traffic shaping on a per-destination basis perform the traffic shaping on a per-destination basis by adjusting the data rate at which the traffic is sent along the communication path. 12. The apparatus as in claim 11 , wherein the change in the performance characteristic for the communication path is predicted by a machine learning process. 13. The apparatus as in claim 11 , wherein the change in the performance characteristic for the communication path is an observed change in the communication path. 14. The apparatus as in claim 11 , wherein the destination address is associated with a rule that controls when the data rate is to be adjusted, wherein the rule specifies at least one of: a time period in which the data rate is to be adjusted, a traffic class that corresponds to the sent traffic, or a traffic coloring that corresponds to the sent traffic. 15. The apparatus as in claim 11 , wherein the one or more destination addresses are identified by the device as top communication destinations used by the device. 16. The apparatus as in claim 11 , wherein the performance characteristic corresponds to one or more of: a delay along the path, an amount of jitter along the path, or an amount of dropped packets along the path. 17. The apparatus as in claim 11 , wherein the change in the performance characteristic of the communication path is identified by: determining a degree of variability between the performance characteristic and a history of observed performance characteristics of the communication path. 18. The apparatus as in claim 17 , wherein the degree of variability is determined using a clustering model. 19. The apparatus as in claim 11 , wherein the data rate is adjusted as part of a closed-loop control mechanism that monitors the performance characteristic as feedback for the adjustment. 20. A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor operable to: maintain a list of a set of one or more destination addresses for which traffic shaping is to be performed by controlling the data rate at which traffic is sent to the one or more destination addresses; send the traffic to one of the destination addresses along a communication path in the network and at a particular data rate, wherein the communication path is a particular communication path; identify a change in a performance characteristic for the communication path; and in response to identifying the change in the performance characteristic for the communication path, perform the traffic shaping on a per-destination basis perform the traffic shaping on a per-destination basis by adjusting the data rate at which the traffic is sent along the communication path.
with rate being modified by the source upon detecting a change of network conditions · CPC title
by using congestion prediction · CPC title
in response to processing delays, e.g. caused by jitter or round trip time [RTT] · CPC title
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