Modeling and Performance Comparison of Caching Strategies for Popular Contents in Internet
The paper is devoted to caching of popular multimedia and Web contents in Internet. We study the Cluster Caching Rule (CCR) recently proposed by the authors. It is based on the idea to store only popular contents arising in clusters of related popularity processes. Such clusters defined as consecutive exceedances of popularity indices over a high threshold are caused by dependence in the inter-request times of the objects and, hence, their related popularity processes. We compare CCR with the well-known Time-To-Live (TTL) and Least-Recently-Used (LRU) caching schemes. We model the request process for objects as a mixture of Poisson and Markov processes with a heavy-tailed noise. We focus on the hit probability as a main characteristic of a caching rule and introduce cache effectiveness as a new metric. Then the dependence of the hit probability on the cache size is studied by simulation.
KeywordsCaching Cluster Caching Rule TTL LRU Hit/miss probability Popularity process Clusters of exceedances Inter-request times
The first author acknowledges the financial support by DAAD scholarship 91619901.
- 1.Che, H., Tung, Y., Wang, Z.: Hierarchical web caching systems: modeling, design and experimental results. IEEE JSAC 20(7), 1305–1314 (2002)Google Scholar
- 3.Berger, D.S., Gland, P., Singla, S., Ciucu, F.: Exact analysis of TTL cache networks: the case of caching policies driven by stopping times. In: 2014 ACM International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2014, pp. 595–596 (2014)Google Scholar
- 4.Fofack, N.C., Nain, P., Neglia, G., Towsley, D.: Analysis of TTL-based cache networks. In: 6th International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS), pp. 1–10 (2012)Google Scholar
- 5.Friecker, C., Robert, P., Roberts, J.: A versatile and accurate approximation for LRU cache performance. In: Proceedings of ITC 2012, pp. 1–8 (2012)Google Scholar
- 8.Markovich, N.M., Krieger, U.R.: A caching policy driven by clusters of high popularity. In: 7th IEEE International Workshop on TRaffic Analysis and Characterization (TRAC 2016), 5–9 September, Paphos, Cyprus (2016)Google Scholar
- 10.Breslau, L., Cao, P., Fan, L., Phillips, G., Shenker, S.: Web caching and Zipf-like distributions: evidence and implications. In: IEEE Proceedings of Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 1999), vol. 1, pp. 126–134 (1999)Google Scholar
- 16.Dehghan, M., Massoulie, L., Towsley, D., Menasche, D., Tay, Y.C.: A utility optimization approach to network cache design, pp. 1–11 (2016). arXiv: 1601.06838v1
- 19.Großmann, M., Eiermann, A., Renner, M.: Hypriot cluster lab: an ARM-powered cloud solution utilizing docker. In: 23rd International Conference on Telecommunications (ICT 2016), 16–18 May, Thessaloniki, Greece (2016)Google Scholar
- 20.Großmann, M., Eiermann, A.: Security of distributed container based service clustering with hypriot cluster lab. In: Proceedings of ITC 28, September 12–16, Würzburg, Germany (2016)Google Scholar
- 21.Pahl, C., Lee, B.: Containers and clusters for edge cloud architectures - a technology review. In: 3rd International Conference on Future Internet of Things and Cloud (FiCloud), 24–26 August 2015, pp. 379–386 (2015)Google Scholar