Reducing floating dags and stabilizing topology in llns using learning machines
US-2015188801-A1 · Jul 2, 2015 · US
US9667559B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9667559-B2 |
| Application number | US-201414171206-A |
| Country | US |
| Kind code | B2 |
| Filing date | Feb 3, 2014 |
| Priority date | Feb 3, 2014 |
| Publication date | May 30, 2017 |
| Grant date | May 30, 2017 |
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Embodiments of the disclosure are directed to tunnel flap damping in a traffic engineered network. One exemplary method for tracking a re-routing history of Multi-Protocol Label Switching (“MPLS”)/Generalized MPLS (“GMPLS”) tunnels over intervals includes measuring a number of policy-based re-routes during at least one measurement interval; determining a tunnel re-route frequency based on the measurement; comparing the tunnel re-route frequency to a frequency threshold; and determining if the tunnel re-route frequency exceeds the frequency threshold and, if the tunnel re-route frequency exceeds the frequency threshold, delaying at least one tunnel re-route.
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What is claimed is: 1. A method for tracking a re-routing history of Multi-Protocol Label Switching (“MPLS”)/Generalized MPLS (“GMPLS”) tunnels over intervals, the method comprising: measuring a number of policy-based re-routes of the MPLS/GMPLS tunnels during at least one measurement interval, wherein the policy-based re-routes comprise re-routing of the MPLS/GMPLS tunnels based on policy to optimize associated traffic engineering resources; determining a tunnel re-route frequency based on the measurement; comparing the tunnel re-route frequency to a frequency threshold; determining if the tunnel re-route frequency exceeds the frequency threshold; and delaying at least one tunnel re-route using an exponential back-off time with a duration derived as a function of a re-route dampening factor if the tunnel re-route frequency exceeds the frequency threshold avoiding tunnel flapping of the MPLS/GMPLS tunnels. 2. The method of claim 1 , wherein determining the tunnel re-route frequency comprises calculating an average number of re-routes over the at least one measurement interval. 3. The method of claim 2 , wherein the calculating the tunnel re-route frequency comprises using an Auto-Regressive Moving Average (“ARMA”) model. 4. The method of claim 2 , wherein the calculating the tunnel re-route frequency comprises using a Periodic Auto-Regressive Moving Average (“PARMA”) model. 5. The method of claim 4 , wherein using the PARMA model comprising estimating the average number of re-routes using a finite approximation based on a predetermined number of observations. 6. The method of claim 1 , further comprising tuning the value of the re-route dampening factor. 7. A system for tracking a re-routing history of Multi-Protocol Label Switching (“MPLS”)/Generalized MPLS (“GMPLS”) tunnels over intervals, the system comprising: a measuring module configured to measure a number of policy-based re-routes of the MPLS/GMPLS tunnels during at least one measurement interval, wherein the policy-based re-routes comprise re-routing of the MPLS/GMPLS tunnels based on policy to optimize associated traffic engineering resources; a tunnel re-route frequency module configured to determine a tunnel re-route frequency based on the measurement; a comparison module configured to compare the tunnel re-route frequency to a frequency threshold; and a delaying module configured to delay at least one tunnel re-route using an exponential back-off time with a duration derived as a function of a re-route dampening factor if the tunnel re-route frequency exceeds the frequency threshold avoiding tunnel flapping of the MPLS/GMPLS tunnels. 8. The system of claim 7 , wherein the tunnel re-route frequency module is configured to calculate an average number of re-routes over the at least one measurement interval. 9. The system of claim 8 , wherein the tunnel re-route frequency module is configured to use an Auto-Regressive Moving Average (“ARMA”) model. 10. The system of claim 8 , wherein the tunnel re-route frequency module is configured to use a Periodic Auto-Regressive Moving Average (“PARMA”) model. 11. The method of claim 10 , wherein the tunnel re-route frequency module is configured to estimate the average number of re-routes using a finite approximation based on a predetermined number of observations. 12. The method of claim 7 , wherein the exponential back-off timer is configured to tune the value of the re-route dampening factor. 13. A non-transitory computer program product residing on a processor-executable computer storage medium, the computer program product comprising processor-executable instructions configured to cause a processor to: measure a number of policy-based re-routes of Multi-Protocol Label Switching (“MPLS”)/Generalized MPLS (“GMPLS”) tunnels during at least one measurement interval; determine a tunnel re-route frequency based on the measurement, wherein the policy-based re-routes comprise re-routing of the MPLS/GMPLS tunnels based on policy to optimize associated traffic engineering resources; compare the tunnel re-route frequency to a frequency threshold; determine if the tunnel re-route frequency exceeds the frequency threshold; and delay at least one tunnel re-route using an exponential back-off time with a duration derived as a function of a re-route dampening factor if the tunnel re-route frequency exceeds the frequency threshold avoiding tunnel flapping of the MPLS/GMPLS tunnels. 14. The non-transitory computer program product of claim 13 , wherein determine the tunnel re-route frequency comprises calculating an average number of re-routes over the at least one measurement interval. 15. The non-transitory computer program product of claim 14 , wherein the calculate the tunnel re-route frequency comprises using an Auto-Regressive Moving Average (“ARMA”) model. 16. The non-transitory computer program product of claim 14 , wherein the calculate the tunnel re-route frequency comprises using a Periodic Auto-Regressive Moving Average (“PARMA”) model.
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