A Study of Policies of Bandwidth Assignment to HTTP Connections

A proper model for the bandwidth (or bit rate) that Web connections demand is essential in tailoring resources in Internet networks. This work investigates the impact of different factors on HTTP connection rates. By predicting the rates in l ong traces from a trans -Pacific link, it is shown that the size and the number of expected packets in the connections are more influent than round trip time and the time of the day in which the connections are performed. In contrast with the classical heavy-tailed nature of the distribution of Web objects, it is shown that exponential models can fit the rates of Web connections. SCHEMES FOR BANDWIDTH PREDICTION Due to the complex nature of Internet traffic, the bit rate at which Web objects are transmitted relies on a wide set of factors. Some of these factors are unpredictable but others can be determined during the set-up of the connections. To avoid the heuristic rules that are generally utilised to allocate the bandwidth in Internet, it has been proposed [1] to predict the bit rate of the connections in the access routers. To perform this prediction it is considered two policies, both based on the registered logs of the previous HTTP connections: -A fixed assignment of bandwidth, which allocates the same bandwidth for all the connections. -A parameter -based assignment of bandwidth. According to this policy, all connections are classified in N different groups as a function of the value of an associated parameter. So, the rate for an incoming connection is assigned depending on the value of its associated parameter. We separately consider four possible parameters associated to each connection: 1) The Round Trip Time (RTT), which can be estimated during the set-up (from the typical three packet handshake between the client and the Web server). 2) The time of the day in which the connection is initiated (which allows the prediction to be sensitive to the nonstationarity present in Internet traffic). 3) The size of the connection, which can be obtained from the HTTP server response. 4) The expected number of useful (non-empty) packets of the connections, which could be deduced from the connection size and the maximum transfer unit (MTU) of the path. EXPERIMENTS AND CONCLUSION As a testbed we utilised long actual traces captured in a transoceanic T1 link connecting USA and Japan [2]. In the traces, which include the traffic of 15 consecutive days of September 2000, more than 7 ·10 5 HTTP connections were detected, accounting for more than 50% of the overall traffic. Obviously the traces just incorporate remote Web transfers (between two continents), for which the RTT is expected to be high (some cents of milliseconds). The rates are computed from the ratio between size and duration of each connection. In the experiments, the rates of the connections of the last day are forecasted as a function of the traffic of the two precedent weeks. To evaluate the performance of the prediction methods it is studied the percentages of lost and wasted bandwidth for each connection [1]. Results show the benefits of predicting the rate basing on the size and the number of useful packets in the connections. This fact can be explained by the existence of the mechanism of slow start in TCP and the protocol latency necessary to open and close the connections, which is relatively higher in small sized connections. Oppositely, comparing with the fixed assignment policy, the predictions as a function of the RTT and the time do not introduce any significant improvement. To evaluate the impact of the particular value of the number of considered intervals (N), we repeated the predictions based on the number of packets using different values of N. It is shown illustrated that no substantial benefit is attained with values of N above 10. On the other hand, as it has been repeatedly verified in the literature [3], both the size and the duration present heavytailed distributions. However, the crossed-correlation between these parameters and a classification according to the number of packets allow to model the bit rate of web connections as a simple composition of exponential distributions. ACKNOWLEDGEMENTS