Abstract
Content Delivery Networks are one of the most common services in order to overcome performance problems caused by massive data requests in popular web applications. CDNs improve clients’ perceived quality of service by placing replica servers scattered around the globe and consequently redirecting users to closer servers. While CDNs’ ultimate goal is to improve the performance of data delivery, their own efficiency can also be an issue to investigate. Due to the complexity of these services, plenty of factors can impact the performance of CDNs. As a result, the efficiency of CDNs can be measured using various metrics. In this paper we review some of the well-known performance metrics in the literature for evaluating CDNs. We also present some other measures including Fairness and Content Travel. In order to attain an overall insight about a CDN, a Cost Function is also presented which incorporates most of the metrics in a single formula.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vakali, A., Pallis, G.: Content delivery networks: status and trends. IEEE Internet Comput. 7(6), 68–74 (2003)
Pathan, A.-M.K., Buyya, R.: A taxonomy and survey of content delivery networks. Grid Computing and Distributed Systems Laboratory, University of Melbourne, Technical report, 4 (2007)
Buyya, R., Pathan, M., Vakali, A.: Content Delivery Networks. LNEE, vol. 9. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77887-5
Fiadino, P., D’Alconzo, A., Casas, P.: Characterizing web services provisioning via CDNS: the case of facebook. In: 2014 International Wireless Communications and Mobile Computing Conference (IWCMC), pages 310–315. IEEE (2014)
Qiu, L., Padmanabhan, V.N., Voelker, G.M.: On the placement of web server replicas. In: Proceedings of the IEEE Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2001, vol. 3, pp. 1587–1596. IEEE (2001)
Akhtar, Z., Hussain, A., Katz-Bassett, E., Govindan, R.: DBit: assessing statistically significant differences in CDN performance. Comput. Netw. 107, 94–103 (2016)
Hours, H., Biersack, E., Loiseau, P., Finamore, A., Mellia, M.: A study of the impact of DNS resolvers on CDN performance using a causal approach. Comput. Netw. 109, 200–210 (2016)
Calder, M., Flavel, A., Katz-Bassett, E., Mahajan, R., Padhye, J.: Analyzing the performance of an anycast CDN. In: Proceedings of the 2015 ACM Conference on Internet Measurement Conference, pp. 531–537. ACM (2015)
Chen, F., Sitaraman, R.K., Torres, M.: End-user mapping: next generation request routing for content delivery. ACM SIGCOMM Comput. Commun. Rev. 45, 167–181 (2015)
Sidiropoulos, A., Pallis, G., Katsaros, D., Stamos, K., Vakali, A., Manolopoulos, Y.: Prefetching in content distribution networks via web communities identification and outsourcing. World Wide Web 11(1), 39–70 (2008)
Ariyasinghe, L.R., Wickramasinghe, C., Samarakoon, P.M.A.B., Perera, U.B.P., Prabhath Buddhika, R.A., Wijesundara, M.N.: Distributed local area content delivery approach with heuristic based web prefetching. In: 2013 8th International Conference on Computer Science & Education (ICCSE), pp. 377–382. IEEE (2013)
Krishnan, R., Madhyastha, H.V., Srinivasan, S., Jain, S., Krishnamurthy, A., Anderson, T., Gao, J.: Moving beyond end-to-end path information to optimize CDN performance. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, pp. 190–201. ACM (2009)
Yu, M., Jiang, W., Li, H., Stoica, I.: Tradeoffs in CDN designs for throughput oriented traffic. In: Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies, pp. 145–156. ACM (2012)
MaxMind LLC. GeoIP (2010)
Jain, R., Chiu, D.-M., Hawe, W.R.: A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer System, vol. 38. Eastern Research Laboratory, Digital Equipment Corporation Hudson, MA (1984)
Veness, C.: Calculate distance and bearing between two latitude/longitude points using Haversine formula in Javascript. Movable Type Scripts (2011)
Jafari, S.J., Naji, H.: GeoIP clustering: solving replica server placement problem in content delivery networks by clustering users according to their physical locations. In: 2013 5th Conference on Information and Knowledge Technology (IKT), pp. 502–507. IEEE (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Jafari, S.J., Naji, H., Jannatifar, M. (2018). Investigating Performance Metrics for Evaluation of Content Delivery Networks. In: Beheshti, A., Hashmi, M., Dong, H., Zhang, W. (eds) Service Research and Innovation. ASSRI ASSRI 2015 2017. Lecture Notes in Business Information Processing, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-319-76587-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-76587-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76586-0
Online ISBN: 978-3-319-76587-7
eBook Packages: Computer ScienceComputer Science (R0)