Performance and Precision of Web Caching Simulations Including a Random Generator for Zipf Request Pattern
The steadily growing Internet traffic volume for video, IP-TV and other content needs support by caching systems and architectures which are provided in global content delivery networks as well as in local networks, on home gateways or user terminals. The efficiency of caching is important in order to save transport capacity and to improve throughput and delays.
However, since analytic solutions for the hit rate as the main caching performance measure are not available even under the baseline scenario of an independent request model (IRM) with usual Zipf request pattern and caching strategies, simulation methods are used to evaluate caching efficiency. Based on promising experience with simulation approaches of caching methods in previous work, we study and verify two main prerequisites: First, a fast random Zipf rank generator is derived, which allows to extend simulations to billions of requests. Moreover, the accuracy of alternatives of the hit rate evaluation is compared based on the 2nd order statistics. The results indicate that the sum of request probabilities of objects in the cache provides a more precise estimator of the hit rate as a simple hit count.
KeywordsSimulation of caching strategies Least Recently Used (LRU) Score gated LRU Least Frequently Used (LFU) Zipf request pattern Random zipf rank generator 2nd order statistics Hit rate estimators
This work has received funding from the European Union’s Horizon 2020 research and innovation programme 2014-2018 under grant agreement No. 644866. This work reflects only the authors’ views and the European Commission is not responsible for any use that may be made of the information it contains.
- 2.Breslau, L., et al.: Web caching and Zipf-like distributions: Evidence and implications. In: Proceedings of the IEEE INFOCOM, New York, USA (1999)Google Scholar
- 4.Che, H., Tung, Y., Wang, Z.: Hierarchic web caching systems: modeling, design and experimental results. IEEE JSAC 20(7), 1305–1314 (2002)Google Scholar
- 6.Figueiredo, F., et al.: TrendLearner: Early prediction of popularity trends of user generated content (2014). http://arxiv.org/abs/1402.2351
- 7.Fricker, C., Robert, P., Roberts, J., Sbihi, N.: Impact of traffic mix on caching performance in a content-centric network. In: IEEE INFOCOM Workshops, pp. 310–315 (2012). http://arxiv.org/abs/1202.0108
- 8.Hasslinger, G.: Efficiency of caching and content delivery in broadband access networks. In: Mukkadim, P. et al. (ed.) Chapter 4 in Advanced Content Delivery, Streaming & Cloud Services, pp. 71–90. Wiley (2014)Google Scholar
- 9.Hasslinger, G., Hartleb, F.: Content delivery and caching from a network provider’s perspective. Spec. Issue Int. Content Delivery, Comput. Netw. 55, 3991–4006 (2011)Google Scholar
- 10.Hasslinger, G., Ntougias, K., Hasslinger, F.: A new class of web caching strategies for content delivery. In: Proceedings of the Networks Symposium, Funchal, Madeira, Portugal, pp. 1–7 (2014)Google Scholar
- 12.Hasslinger, G., Schwahn, A., Hartleb, F.: 2-state (semi-)Markov processes beyond Gilbert-Elliot: Traffic models based on 2nd order statistics. In: Proceedings of the IEEE INFOCOM, Turin, Italy, pp. 1438–1446 (2013)Google Scholar
- 15.Megiddo, N., Modha, S.: Outperforming LRU with an adaptive replacement cache algorithm. IEEE Comput. 5, 4–11 (2004)Google Scholar
- 17.Qiu, T., et al.: Modeling channel popularity dynamics in a large IPTV system. In: Proceedings of the 11th ACM SIGMETRICS, Seattle, WA, USA (2009)Google Scholar
- 18.Shi, et al.: An applicative study of Zipf’s law on web caches. Int. J. Inf. Technol. 12(4), 49–58 (2006)Google Scholar
- 20.Wolfram Research, Wolfram Language Tutorial (2015). https://reference.wolfram.com/language/tutorial/RandomNumberGeneration.html