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Comparison of caching strategies in modern cellular backhaul networks

Published:25 June 2013Publication History

ABSTRACT

Recent popularity of smartphones drives rapid growth in the demand for cellular network bandwidth. Unfortunately, due to the centralized architecture of cellular networks, increasing the physical backhaul bandwidth is challenging. While content caching in the cellular network could be beneficial, relatively few characteristics of the cellular traffic is known to come up with a highly-effetive caching strategy. In this work, we provide insight into flow and content-level characteristics of modern 3G traffic at a large cellular ISP in South Korea. We first develop a scalable deep flow inspection (DFI) system that can manage hundreds of thousands of concurrent TCP flows on a commodity multicore server. Our DFI system collects various HTTP/TCP-level statistics and produces logs for analyzing the effectiveness of conventional Web caching, prefix-based Web caching, and TCP-level redundancy elimination (RE) without a single packet drop at a 10~Gbps link. Our week-long measurements of over 370 TBs of the 3G traffic reveal that standard Web caching can reduce download bandwidth consumption up to 27.1% while simple TCP-level RE can save the bandwidth consumption up to 42.0% with a cache of 512~GB of RAM. We also find that applying TCP-level RE on the largest 9.4% flows eliminates 68.4% of the total redundancy. Most of the redundancy (52.1%~58.9%) comes from serving the same HTTP objects while the contribution by aliased URLs is up to 38.9%.

References

  1. 3GPP LTE. http://www.3gpp.org/LTE, 2012.Google ScholarGoogle Scholar
  2. ComWorth SwiftWing SIRIUS. http://www.comworth.com.sg/products/swiftwing-sirius-line-rate-capture-storage-system, 2012.Google ScholarGoogle Scholar
  3. Endace DAG. http://www.endace.com/endace-dag-high-speed-packet-capture-cards.html, 2012.Google ScholarGoogle Scholar
  4. Endace Intelligent Network Recoder. http://www.endace.com/endace-high-speed-packet-capture-probes.html, 2012.Google ScholarGoogle Scholar
  5. Libzero for DNA: Zero-copy flexible packet processing on top of DNA. http://www.ntop.org/products/pf_ring/libzero-for-dna/, 2012.Google ScholarGoogle Scholar
  6. Mobile operators risk backhaul gap in LTE networks. http://www.telecomstechnews.com/blog-hub/2013/feb/13/mobile-operators-risk-backhaul-gap-in-lte-networks/, 2012.Google ScholarGoogle Scholar
  7. Napatech NT40/NT20. http://www.napatech.com/products/capture_adapters/1x40g_pcie_nt40e2-1_capture.html, 2012.Google ScholarGoogle Scholar
  8. NPulse HammerHead. http://www.npulsetech.com/Products/HammerHead-Flow-Packet-Capture.aspx, 2012.Google ScholarGoogle Scholar
  9. Perfecting policy control. http://www.ericsson.com/res/docs/whitepapers/WP-e2e-policy-control.pdf, 2012.Google ScholarGoogle Scholar
  10. AfreecaTV, Inc. http://www.afreeca.com/.Google ScholarGoogle Scholar
  11. B. Agarwal, A. Akella, A. Anand, A. Balachandran, P. Chitnis, C. Muthukrishnan, R. Ramjee, and G. Varghese. EndRE: An end-system redundancy elimination service for enterprises. In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. A. Anand, A. Gupta, A. Akella, S. Seshan, and S. Shenker. Packet caches on routers: The implications of universal redundant traffic elimination. In Proceedings of ACM SIGCOMM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Anand, C. Muthukrishnan, A. Akella, and R. Ramjee. Redundancy in network traffic: Findings and implications. In Proceedings of the ACM SIGMETRICS, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. Anand, V. Sekar, and A. Akella. SmartRE: An architecture for coordinated network-wide redundancy elimination. In Proceedings of ACM SIGCOMM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. ARA Networks, Inc. http://www.aranetworks.com/.Google ScholarGoogle Scholar
  16. ARS Technica. The State of 4G: It's All about Congestion, not Speed. http://arstechnica.com/tech-policy/2010/03/faster-mobile-broadband-driven-by-congestion-not-speed/, 2010.Google ScholarGoogle Scholar
  17. A. Badam, K. Park, V. S. Pai, and L. Peterson. HashCache: Cache storage for the next billion. In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. L. Breslau, P. Cue, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web caching and zipf-like distributions: Evidence and implications. In Proceedings of IEEE INFOCOM, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  19. J. Chesterfield, R. Chakravorty, J. Corwcroft, P. Rodriguez, and S. Banerjee. Experiences with multimedia streaming over 2.5G and 3G networks. In Proceedings of the International Conference on Broadband Communications, Networks, and Systems (BROADNETS), 2004.Google ScholarGoogle Scholar
  20. Cisco, Inc. Cisco visual networking index: Global mobile data traffic for 2011-2016. http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-481360_ns827_Networking_Solutions_White_Paper.html, 2012.Google ScholarGoogle Scholar
  21. M. E. Crovella and A. Bestavros. Self-similarity in World Wide Web traffic: evidence and possible causes. IEEE/ACM Transactions on Networking, 5(6):835--846, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. J. Erman, A. Gerber, M. T. Hajiaghayi, D. Pei, S. Sen, and O. Spatscheck. To cache or not to cache: The 3G case. IEEE Internet Computing, 15(2):27--34, Mar. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. J. Erman, A. Gerber, K. Ramakrishnan, S. Sen, and O. Spatscheck. Over the top video: The gorilla in cellular networks. In Proceedings of the ACM SIGCOMM Conference on Internet Measurement Conference (IMC), 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. R. Fielding, J. Gettys, J. Mogul, H. Frystyk, L. Masinter, P. Leach, and T. Berners-Lee. Hypertext Transfer Protocol -- HTTP/1.1. RFC 2616, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. F. Fusco and L. Deri. High speed network traffic analysis with commodity multi-core systems. In Proceedings of the ACM SIGCOMM Conference on Internet Measurement Conference (IMC), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. S. Han, K. Jang, K. Park, and S. B. Moon. PacketShader: a GPU-accelerated software router. In Proceedings of ACM SIGCOMM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. S. Han, S. Marshall, B.-G. Chun, and S. Ratnasamy. MegaPipe: a new programming interface for scalable network I/O. In Proceedings of the USENIX Conference on Operating Systems Design and Implementation (OSDI), 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. S. Ihm and V. Pai. Towards understanding modern web traffic. In Proceedings of the ACM SIGCOMM Conference on Internet Measurement Conference (IMC), 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. M. Jurvansuu, J. Prokkola, M. Hanski, and P. Perala. HSDPA performance in live networks. In Proceedings of IEEE International Conference on Communications (ICC), 2007.Google ScholarGoogle ScholarCross RefCross Ref
  30. H. Krawczyk. LFSR-based hashing and authentication. In Proceedings of the 14th Annual International Cryptology Conference on Advances in Cryptology (CRYPTO), 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. G. Maier, A. Feldmann, V. Paxson, and M. Allman. On dominant characteristics of residential broadband Internet traffic. In Proceedings of the ACM SIGCOMM Conference on Internet measurement Conference (IMC), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. G. Maier, F. Schneider, and A. Feldmann. A first look at mobile hand-held device traffic. In Proceedings of the Passive and Active Measurement (PAM), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. I. Microsoft. MSDN: Introduction to receive-side scaling. http://msdn.microsoft.com/en-us/library/windows/hardware/ff55694228v=vs.8529.aspx, 2012.Google ScholarGoogle Scholar
  34. Oversi, Inc. http://www.oversi.com/.Google ScholarGoogle Scholar
  35. PeerApp, Inc. http://www.peerapp.com/.Google ScholarGoogle Scholar
  36. A. Pesterev, J. Strauss, N. Zeldovich, and R. T. Morris. Improving network connection locality on multicore systems. In Proceedings of the ACM European Conference on Computer Systems (EuroSys), 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. F. Qian, J. Huang, J. Erman, Z. M. Mao, S. Sen, and O. Spatscheck. How to reduce smartphone traffic volume by 30%? In Proceedings of the Passive and Active Measurement (PAM), 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. F. Qian, K. S. Quah, J. Huang, J. Erman, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. Web caching on smartphones: Ideal vs. reality. In Proceedings of the ACM International Conference on Mobile Systems, Applications, and Services (MobiSys), 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. M. O. Rabin. Fingerprinting by random polynomials. Technical Report TR-15-81, Harvard University, 1981.Google ScholarGoogle Scholar
  40. E. Seidel and E. Saad. LTE home node BS and its enhancements in release 9. http://www.nomor.de/uploads/fc/lp/fclpAIhtNJQ9zwyD957atQ/2010-05_LTE_HomeNB_Rel9_Overview.pdf, 2012.Google ScholarGoogle Scholar
  41. R. Sommer, V. Paxson, and N. Weaver. An architecture for exploiting multi-core processors to parallelize network intrusion prevention. Concurrency and Computation: Practice and Experience, 21(10):1255--1279, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. N. T. Spring and D. Wetherall. A protocol-independent technique for eliminating redundant network traffic. In Proceedings of ACM SIGCOMM, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. The Cuttting Edge. U.S. could see $53 billion in 4G network investments in five years. http://www.thecuttingedgenews.com/index.php?article=52617&pageid=28&pagename=Sci-Tech, 2011.Google ScholarGoogle Scholar
  44. G. Vasiliadis, M. Polychronakis, and S. Ioannidis. MIDeA: A multi-parallel intrusion detection architecture. In Proceedings of the ACM Conference on Computer and Communications Security (CCS), 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. I. VerizonWireless. Verizon optimization deployment-terms & conditions. http://support.verizonwireless.com/terms/network_optimization.html, 2012.Google ScholarGoogle Scholar
  46. S. Woo and K. Park. Scalable TCP session monitoring with symmetric receive-side scaling. http://www.ndsl.kaist.ac.kr/~shinae/papers/TR-symRSS.pdf, 2012.Google ScholarGoogle Scholar
  47. E. Zohar, I. Cidon, and O. Mokryn. The power of prediction: Cloud bandwidth and cost reduction. In Proceedings of ACM SIGCOMM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      MobiSys '13: Proceeding of the 11th annual international conference on Mobile systems, applications, and services
      June 2013
      568 pages
      ISBN:9781450316729
      DOI:10.1145/2462456

      Copyright © 2013 ACM

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      Publication History

      • Published: 25 June 2013

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