Traditional traffic analysis is can be performed online only when detection targets are well specified and are fairly primitive. Local processing at measurement point is discouraged as it would considerably affect major functionality of a network device. When traffic is analyzed at flow level, the notion of flow timeout generates differences in flow lifespan and impedes unbiased monitoring, where only n-top flows ordered by a certain metric are considered. This paper proposes an alternative manner of traffic analysis based on source IP aggregation. The method uses flows as basic building blocks but ignores timeouts, using short monitoring intervals instead. Multidimensional space of metrics obtained through IP aggregation, however, enhances capabilities of traffic analysis by facilitating detection of various anomalous conditions in traffic simultaneously.

In NGN standards, End Host, also referred to as Terminal Equipment (TE), holds an important place in end-to-end path performance. However, most researchers neglect TE performance when considering performance of end-to-end paths. As far as the authors' knowledge goes, no previous study has proposed a model for TE performance. This paper proposes a method for measuring performance of TE and model extraction based on measurement data. The measurement was made possible with the use of a special NPU (Network Processing Unit) implemented as a programmable NIC. Along with the probing itself, a framework for removing the skew between the NPU and OS is developed in this paper. The multidimensional analysis includes method of probing, packet size and background traffic volume, and studies their effect on TE performance. A method for extracting a generic TE model is proposed. The outcome of this research can be used for modelling TE in simulations and in modelling end-to-end performance when considering QoS in NGN.

This paper proposes a new optimization problem and several implementation algorithms for energy-efficient clouds where energy efficiency is measured by the number of physical machines that can be removed from operation and turned off. The optimization problem is formulated is such a way that solutions are considered favorable not only when the number of migrations is minimized but also when the resulting layout has more free physical machines which can therefore be turned off to save electricity.