On the 95-Percentile Billing Method
The 95-percentile method is used widely for billing ISPs and websites. In this work, we characterize important aspects of the 95-percentile method using a large set of traffic traces. We first study how the 95-percentile depends on the aggregation window size. We observe that the computed value often follows a noisy decreasing trend along a convex curve as the window size increases. We provide theoretical justification for this dependence using the self-similar model for Internet traffic and discuss observed more complex dependencies in which the 95-percentile increases with the window size. Secondly, we quantify how variations on the window size affect the computed 95-percentile. In our experiments, we find that reasonable differences in the window size can account for an increase between 4.1% and 42.5% in the monthly bill of medium and low-volume sites. In contrast, for sites with average traffic rates above 10Mbps the fluctuation of the 95-percentile is bellow 2.9%. Next, we focus on the use of flow data in hosting environments for billing individual sites. We describe the byte-shifting effect introduced by flow aggregation and quantify how it can affect the computed 95-percentile. We find that in our traces it can both decrease and increase the computed 95-percentile with the largest change being a decrease of 9.3%.
Unable to display preview. Download preview PDF.
- 1.Webhostingtalk Forum: 95th percentile billing polling interval (2008) (last accessed: 09/23/2008), http://www.webhostingtalk.com/showthread.php?t=579063
- 2.The Tcpdump team: tcpdump, http://www.tcpdump.org/
- 3.Cisco: IOS NetFlow, http://www.cisco.com/en/US/products/ps6601/products_ios_protocol_group_home.html
- 5.Paxson, V., Floyd, S.: Wide-area traffic: the failure of poisson modeling. In: SIGCOMM 1994: Proceedings of the conference on Communications architectures, protocols and applications, pp. 257–268. ACM, New York (1994)Google Scholar