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![Page 1: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/1.jpg)
Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational
Constraints
Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University
Dan Pei, Jia Wang, AT&T Labs -Research
![Page 2: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/2.jpg)
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OutlineMotivation
Problem Definition
Monitor Setup•Single-round monitoring
•Multi-round monitoring
Evaluation
Related Works
Conclusion
![Page 3: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/3.jpg)
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Motivation
PE
VPN BackbonePE
CE
CE
VPN1,Site2
VPN2,Site1
VPN1,
Site1
PE
CE
CE
VPN2,Site2
![Page 4: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/4.jpg)
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MotivationVPN performance monitoring• Reliability
• Quality of service (SLA)
Approaches• Passive measurements: SNMP-based Monitoring– Fixed poll rate– Difficult to measure end-to-end path-level features (e.g. delay, bw)
• Active measurements– Operational constraints
• E.g. monitor, link, path constraints
![Page 5: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/5.jpg)
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Problem Definition
PE
VPN BackbonePE
CE
CE
VPN2,Site2
VPN2,Site1
VPN1,
Site1
PE
CE
CE
VPN1,Site2
Goal: continuously monitoring and diagnosing VPN performance under operational constraints
Each monitor can measure <=c paths
Each replier can reply <=r paths
Each link is on <=b measured paths
Traffic isolation between VPNs
Challenges with operational constraints•Optimization problem → constraint satisfactory problem•All paths measured simultaneously?
X
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VScope System Architecture
Two phases:• VScope Setup
• VScope Operation: monitoring + diagnosis
Provides a smooth tradeoff between measurement frequency and monitors deployment/management costs
![Page 7: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/7.jpg)
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Two PhasesMonitor setup phase• From certain monitor candidates, how to select minimal
number of monitors, which in the measurement phase can measure a selected set of paths that covers all links in the network under the given measurement constraints?
• NP-hard even without considering constraints
Monitoring and fault diagnosis phase• When faulty paths are discovered in the path monitoring
phase, how to quickly select some paths under the operational constraints to be further measured so that the faulty link(s) can be accurately identified?
![Page 8: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/8.jpg)
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Two PhasesMonitor setup phase• From certain monitor candidates, how to select minimal
number of monitors, which in the measurement phase can measure a selected set of paths that covers all links in the network under the given measurement constraints?
• NP-hard even without considering constraints
Monitoring and fault diagnosis phase• When faulty paths are discovered in the path monitoring
phase, how to quickly select some paths under the operational constraints to be further measured so that the faulty link(s) can be accurately identified?
![Page 9: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/9.jpg)
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Outline
Motivation
Problem Definition
Monitor Setup•Single-round monitoring
•Multi-round monitoring
Evaluation
Related Works
Conclusion
![Page 10: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/10.jpg)
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Monitoring Strategies
…P1
…P2
…P3
…P4
…P5
…P6
…P1
…P2
…P3
…P4
…P5
…P6
t t
Single-Round Monitoring Multi-Round Monitoring
Round 1 Round 2
Round 1
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Multi-Round MonitoringPros• Relax tight constraints• Reduce number of monitors
Cons• Less monitoring frequency
Monitor Selection Algorithm• Consider R rounds of back-to-back measurements• Step 1: convert multi-round monitor selection
problem to single-round problem and solve the single-round monitor selection problem– Relax monitor & link bw constraints by a factor of R
• Step 2: schedule paths measured in R rounds
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Single-Round Monitor SelectionMonitor Selection Problem• Related to Minimum Set Cover problem
• NP-hard without constraints [Bejerano, Infocom03]
Pure Greedy Algorithm• Simple and locally optimized
Greedy Assisted Integer Linear Programming based algorithm
• Linear programming is good at dealing with constraints
• ILP is NP-hard
• Need to relax ILP to LP
![Page 13: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/13.jpg)
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Pure Greedy Algorithm
Two-level nested Minimum Set Cover Problem and Maximum Coverage Problem
• Iteratively select a candidate router as a new monitor that can measure paths covering maximum number of un-covered links before the selection
• Computing the maximum gain of adding a router as a monitor is a variant of Maximum Coverage problem (also NP-hard)– Iteratively select a path of the router that
• will not violate the link bandwidth constraints and • covers maximum number of un-covered links before the selection
– Until the number of selected paths reaches the monitor’s constraint
![Page 14: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/14.jpg)
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Integer Linear Programming
Minimize number of monitorsA path is monitored iff the source router is selected as monitorMonitor constraintReplier constraint
A link is covered if at least one path containing the link is selectedLink bandwidth constraint
It is NP-hard!
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Relaxation with Random Rounding
Relax the Integer Linear Programming to Linear Programming
• Suppose the solution of linear programming is x*i, y*i
• Rounding rule:
![Page 16: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/16.jpg)
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Greedy Assisted Linear Programming
Use Linear Programming to select a set of monitors and corresponding measurement paths
• Not all links are covered
Use greedy algorithm to cover uncovered links
• Similar to the pure greedy algorithm
![Page 17: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/17.jpg)
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Mulit-Round Path SchedulingNP-hard • Can reduce minimum graph coloring problem to path scheduling
problem
Three algorithms• Random algorithm
– Randomly schedule paths independently– Run random algorithm multiple times to get the best one
• Greedy algorithm– Minimize link utilization in every step
• LP based Randomization Algorithm– ILP + relaxation and random rounding
Optimization metrics• Maximum link violation degree (MLVD)• Average link violation degree (ALVD)
![Page 18: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/18.jpg)
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OutlineMotivation
Problem Definition
Monitor Setup•Single-round monitor selection
•Multi-round monitor selection
Evaluation
Related Works
Conclusion
![Page 19: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/19.jpg)
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EvaluationTopologies• Synthetic topologies generated by BRITE• Real topologies from a tier-1 ISP: one IP backbone topology (IP-
EX), one VPN backbone topology (VB), and two VPN infrastructure topologies (V1-EX, V2-EX)– Scale from 100s nodes to 100,000s nodes– Heterogeneous real link bw (1.54Mbps ~ 10Gbps)
Operational constraints• From ISP management team
– E.g. percent link bw allowed for probing: 1%
Evaluation metrics• Percentage of monitors selected• Maximum (average) link violation degree after scheduling• Running speed
![Page 20: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/20.jpg)
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Experimental setup
Default configuration•Monitor constraint = 12
•Replier constraint = 24
•Probing rate per path = 4 pkt /sec
•Measurement BW consumed per path = 1.6Kbps
•Link constraint = 1% x (link capacity)
![Page 21: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/21.jpg)
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Baseline Monitor Selection Results (VB Topology)
LP+Greedy selects fewer monitors.
Vary Link ConstraintVary Monitor Constraint
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Multi-Round Monitor Selection Results (V1-EX Topology)
More rounds and fewer monitors and diminishing returns.
Vary Link ConstraintVary Monitor Constraint
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Multi-Round Monitor Selection Results (V1-EX Topology)
LP > Random > Greedy.
Link violation degree Percentage of links with violation
Greedy Greedy
Random Random
LP LP
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Related WorkPath Selection• The monitoring problem is not considered or too
simple• Complex path selection goal (basis, SVD, Bayesian
experimental design)
Monitoring Placement• Active Monitoring Systems– Similar problem without operational constraints [Bejerano,
Infocom03]– Robustness consideration
• Passive Monitoring Systems– SNMP Polling– Traffic sampling
![Page 25: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/25.jpg)
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Conclusions
VScope for continuously monitoring & diagnosis• Consider operational constraints
• Design multi-round monitor selection algorithms– Single-round monitor selection– Monitoring path scheduling
• Evaluated with synthetic and real topologies– Our algorithms are efficient in minimizing number of
monitors with low constraint violation
![Page 26: Towards Efficient Large-Scale VPN Monitoring and Diagnosis under Operational Constraints Yao Zhao, Zhaosheng Zhu, Yan Chen, Northwestern University Dan.](https://reader036.fdocuments.in/reader036/viewer/2022070400/56649f115503460f94c23bd0/html5/thumbnails/26.jpg)
Q & A?
Thanks!