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Does fractal scaling at the IP level depend on TCP flow arrival processes?

机译:IP级别的分形缩放是否取决于TCP流到达过程?

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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.
机译:除了IP字节和数据包的时间序列中众所周知的长期依赖关系之外,还发现了针对这些序列的小规模缩放行为的证据,并以特征性的过渡时间尺度隔开。鲜为人知的是,在描述TCP流到达的时间序列中也普遍存在两种缩放方式,它们又具有长期依赖性,并且在小范围内具有大致相似的缩放指数。过渡时间尺度也大致类似于IP级别的情况。我们研究了小规模和大规模IP和TCP到达级别的缩放行为之间的依赖性。我们还研究了在IP级别进行小规模扩展的起源。到达级别过程对于研究其对IP级别的潜在影响以及就其本身(例如,对于Web服务器性能)而言都是至关重要的。我们的发现基于在多个位置收集的千兆字节高精度数据包级数据。分析方法以“半实验”方法将模型与实际数据结合在一起,从而减少了对建模假设的需求。对流和数据包进行单独操作,以选择性地隔离由于TCP流中的数据包动态,流之间的依赖关系,流的持续时间和数据包计数以及流到达过程而导致的缩放组件。使用基于小波的方法分析缩放行为。

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