A Protocol-Independent Technique for Eliminating Redundant Network Traffic

1. A Protocol-Independent Technique for Eliminating Redundant Network Traffic Neil T.Spring and David Wetherall Computer Science and Engineering, University of Washington Presented by Kalyan Boggavarapu, Lehigh University Kalyan Boggavarapu Lehigh University

2. Introduction Kalyan Boggavarapu Lehigh University

3. Aim • To eliminate redundant transfers • Currently highest cacheabitly obtained is 45% by squid. ( to improve that) • Origins of uncached content • Dynamically generated or personalized • Mirrored on a different server • Named by a different URL • Delivered using a new or unsupported protocol • Updated static content • Access counted for advertising revenue Kalyan Boggavarapu Lehigh University

4. Algorithm Kalyan Boggavarapu Lehigh University

5. Rabin Fingerprint • Rabin fingerprint for a sequence of bytes t1,t2,t3,…tß • ß = Length of the string • M = 260 • P = 1048583 • Make a table of ti x ß values for fast access (256 entries) • RF/FP are calculated for consecutive bytes of length of ß • Eg: • 1,2,3,4,5,6 bytes,where ß=3 • 1,2,3 • 2,3,4 • 3,4,5 Kalyan Boggavarapu Lehigh University

6. Algorithm • Hold recent packets in the cache • Maintain a FP index of the first bytes of the packets • Check for each incoming packets • Follow LRU for cache replacement Kalyan Boggavarapu Lehigh University

7. Architecture Kalyan Boggavarapu Lehigh University

8. Shared Cache Architecture • Not implemented • Inconsistency is detected by New FPs • Protocol independent • Web Caching + Delta transmission Bandwidth constrained link Packets = tokens + encoded Kalyan Boggavarapu Lehigh University

9. Implementation Kalyan Boggavarapu Lehigh University

10. Parameters Selection Selected values are β=64 bytes γ = 5; Throughput is inversly proportional to β, γ; Cache Allocation = 40% for Fp Index, 60% cache Kalyan Boggavarapu Lehigh University

11. Amount of Redundancy Incoming is redundancy can be captured by increasing the cache size Outgoing data redundancy has limit Kalyan Boggavarapu Lehigh University

12. Locality of Redundancy Same server, lot of redundancy, therefore we can apply end-end solutions Name based proxy caching would fail to capture this amount of redundancy Kalyan Boggavarapu Lehigh University

13. Traffic Analysis Kalyan Boggavarapu Lehigh University

14. Link Utilization Less utilization => more Byte savings (cache is divided among less number of users) More utilization => less Byte savings ( can be improved by increasing the cache size) Kalyan Boggavarapu Lehigh University

15. Redundant incoming traffic High Kalyan Boggavarapu Lehigh University

16. Summary Best Kalyan Boggavarapu Lehigh University To eliminate requests Long Term repitition

17. Related Work Kalyan Boggavarapu Lehigh University

18. Duplicate Suppression • Similar to this method • β is larger or equal to the packet size • Calculate similarity between larger regions • Achieved suppression is less • Delta Encoding • Send the differences in the page Kalyan Boggavarapu Lehigh University

19. Conclusion • Redundancy suppression > Duplicate Suppression • Redundancy suppression > Delta encoding • Protocol independent • Useful to suppress 60% of the incoming traffic and 30% of the outgoing traffic Kalyan Boggavarapu Lehigh University

20. Questions ! Kalyan Boggavarapu Lehigh University