discovering in-network caching policies in ndn networks...
TRANSCRIPT
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Discovering in-network Caching Policies in NDN Networksfrom a Measurement Perspective
Chengyu Fan (Colostate), Susmit Shannigrahi (Tennessee Tech), Christos Papadopoulos (Colostate), Craig Partridge (Colostate)
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NDN requires measuring in-network states• Network measurement tools cover various aspects in IP networks
– Network performance, states (routing, configurations, and topology, etc.), and traffic
• NDN measurements must capture in-network states– Caching policies, forwarding strategies, etc.
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Goals and Assumptions• Our goal: first work to detect caching decisions from a measurement perspective
– Caching is a central feature of NDN
– Caching policy = caching decision + cache replacement
– Multiple caching decisions may exist in NDN networks, and they may interact poorly
• Assumptions– The best-route forwarding strategy and uniform caching decision policy are used
– Priority-FIFO cache replacement policy is used (by default)
– Only one producer exists
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List of caching decisions developed for NDN• Caching Everything Everywhere (CEE)
– Cache every Data chunk locally
• Leave Copy Down (LCD) – Move down the cached copy one hop down
• Label-caching– Pre-decide assign labels to routers, caching chunks whose ID%N match the label value
• Static probabilistic caching (Prob-20, Prob-50, Prob-80)– Pre-define the probability value, and compare it with the generated random number for each chunk
• Dynamic probabilistic caching (ProbCache, ProbCache-inv)– Dynamically calculate a cache weight based on the ratio of hop count of Data and Interest
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Measurement procedure
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NDN
Client Server
1. Send out a train (50) of Interests with the given name prefix• Each contains a unique name: /<name-prefix>/<chunk-id>
3. Save the hop count for each chunk
4. Repeat step 1 ~ 3 for ten times (cached copy can satisfy duplicate request), and plot the hop count distribution in the figure
Pre-defined the target name prefix, Data payload size, and other Data packet parameters
2. Answer each Interest
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Example: LCD caching decision
• Leave Copy Down (LCD) caching decision mechanism– The requested chunks is cached only at the cache that below the location of the hit on the path
• Takeaways– All chunks are cached at specific hops in each round, and the hop count across rounds differs
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R1 R2 R3Client Serverrequest/data/chunk/<1, 2, or 3>
respond/data/chunk/<1, 2, or 3>
/data/chunk/1
/data/chunk/2
/data/chunk/3
/data/chunk/1
/data/chunk/2
/data/chunk/3
/data/chunk/1
/data/chunk/2
/data/chunk/3
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1 2 3 4 5 6 7 8 9 10#round to send requests
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Fingerprint of LCD mechanism• Simulations with ndnSIM
– A linear topology with 10 routers
• Two metrics uniquely identify a caching decision– Hop count distribution in one round
– The distribution change cross multiple rounds
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Fingerprints for other mechanisms• Caching Everything Everywhere (CEE) - cache every Data chunk locally
• Label-caching - cache chunks whose ID%N match the pre-assigned label value
• Static probabilistic caching (Prob-X) - compare the random number with pre-defined probability value
8Label-caching Prob-50CEE
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1 2 3 4 5 6 7 8 9 10#round to send requests
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Fingerprints for probabilistic caching mechanisms
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Static Dynamic• Dynamic probabilistic caching:
Ø Calculate a weight based on the ratio of the hop count of Data and Interest
• ProbCache-inv converges faster than prob-20
• ProbCache converges slower than prob-80
• CacheWeight = #hop(Data) / #hop(Interest)• Rand() < CacheWeight
• Rand() < (1 - CacheWeight)
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Cross traffic may hurt measurements
• Cross traffic may exist in networks– Occupy cache slots
– Evict cached probe packets
– Impact detecting caching policies
• We check the effects by introducing cross traffic at two ends of the linear topology
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NDN router
Content Store MeasurementServer
MeasurementClient
Cross traffic
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Robustness to cross traffic• We can identify the caching mechanisms, as most plot shapes are almost unchanged
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Server-side cross trafficClient-side cross traffic
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Robustness to cross traffic (cont.)• Shapes for LCD are changed, but it has the unique feature
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Server-side cross trafficClient-side cross traffic
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1 2 3 4 5 6 7 8 9 10#round to send requests
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0.2 0.5 0.8Static Probability
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25126321
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Estimate static probabilistic value
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Ideal shapes for 1st round
Shapes based on simulation results
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Detecting on real topology• The NDN stack does not expose the hop count information to applications
– Can we use delays to infer the correct hops?
• Use topology Rocketfuel 7018 with randomly chosen client and server
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LCD Prob-20Delays may produce “correct” hop counts
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Delays do not always infer the correct hops• In some cases, link delays may not be identical with hops
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Estimating hop counts• Using clustering algorithms (e.g. K-means) to group samples with similar delays
– The figures approximately show the correct shapes
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Conclusion• Proposed a novel method to extract fingerprints for caching decision mechanisms
• The method can detect caching decisions mechanisms from end hosts– Not sensitive to cross traffic
– Can estimate probability value
• Evaluated the method on a real topology– Applications use delays to estimate hop counts
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Future work• Evaluate the method with more caching mechanisms on a real testbed (i.e. NDN
testbed)
• Study the robustness of our method with other cache replacement policies
• Integrate the measurement tool with the NDN measurement framework designed by NIST [1] [2]
• Study the scenarios where multiple producers exist and other forwarding strategies are used
18[1] Pesavento, Davide, et al. "A network measurement framework for named data networks." Proceedings of the 4th ACM Conference on Information-Centric Networking. 2017.[2] Nichols, Kathleen. "Lessons Learned Building a Secure Network Measurement Framework using Basic NDN." Proceedings of the 6th ACM Conference on Information-Centric Networking. 2019.
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Thanks!19