does fractal scaling at the ip level depend on tcp flow arrival processes?
In addition to the well known long-range dependence in time series of IP bytes and packets, evidence for scaling behaviour has also been found at small scales for these series, separated by a characteristic transition timescale. It is less well known that two scaling regimes are also commonly found in time series describing the arrivals of TCP flows, again with long-range dependence, and with a broadly similar scaling exponent at small scales. The transition timescale is also roughly similar to that found in the IP level case. We investigate the dependencies between the scaling behaviours of the IP and TCP arrival levels at both small and large scales. We also study the origin of scaling at small scales at the IP level. The arrival level process is important to study both for its potential impact on the IP level, and in its own right, for example for web server performance. Our findings are based on gigabytes of high precision packet level data collected at multiple locations. The analysis methodology combines models with real data in a 'semi-experimental' approach which reduces the need for modeling assumptions. Flows and packets are individually manipulated to selectively isolate the components of scaling due to packet dynamics within a TCP flow, the dependencies between flows, their durations and packet counts, and the flow arrival process. The scaling behaviour is analysed using wavelet based methods.
fractal scaling dependencies flow process time series evidence transit case examples web server performance precise data locatability analysis method methodology model component dynamic wavelets tcp arrival times internet data traffic modeling experimentation long range dependence