Traffic measurement is a critical component for the control and engineering of communication networks. We argue that traffic measurement should make it possible to obtain the spatial flow of traffic through the domain, i.e., the paths followed by packets between any ingress and egress point of the domain. Most resource allocation and capacity planning tasks can benefit from such information. Also, traffic measurements should be obtained without a routing model and without knowledge of network state. This allows the traffic measurement process to be resilient to network failures and state uncertainty. We propose a method that allows the direct inference of traffic flows through a domain by observing the trajectories of a subset of all packets traversing the network. The key advantages of the method are that (i) it does not rely on routing state, (ii) its implementation cost is small, and (iii) the measurement reporting traffic is modest and can be controlled precisely. The key idea of the method is to sample packets based on a hash function computed over the packet content. Using the same hash function will yield the same sample set of packets in the entire domain, and enables us to reconstruct packet trajectories. I.

Abstract. Sampling has become an integral part of passive network measurement. This role is driven by the need to control the consumption of resources in the measurement infrastructure under increasing traffic rates and the demand for detailed measurements from applications and service providers. Classical sampling methods play an important role in the current practice of Internet measurement. The aims of this review are (i) to explain the classical sampling methodology in the context of the Internet to readers who are not necessarily acquainted with either, (ii) to give an account of newer applications and sampling methods for passive measurement and (iii) to identify emerging areas that are ripe for the application of statistical expertise. Key words and phrases: Traffic measurement, network management, sampling methods, estimation, packets, flows.

Non-intrusive one-way delay measurements require the transport and correlation of measurement data from multiple observation points. The amount of data that needs to be transferred and processed increases proportionally to the number of packets measured. High packet rates elevate the resource consumption and with this the costs for the measurement process. Since measurement costs should be only a small fraction of the service provisionlng costs itself, there is a strong incentive for Hmiting the needed measurement resources and lowering the associated costs.

The advent of class-based networks has brought new needs for network traffic control in order to assure a certain QoS level. Despite the existing proposals, achieving a generic admission control (AC) strategy for traffic entering these networks is still an open issue. This paper provides new insights on how AC shall be accomplished proposing an encompassing AC model for multi-service class-based networks, which covers both intra-domain and end-to-end operation, without requiring changes in the network core and complex AC signaling. For each service type, AC is distributed and based on both on-line edge-to-edge monitoring of relevant QoS parameters and SLSs utilization. Service monitoring, performed at egress nodes, provides adequate metrics to ingress nodes which take implicit or explicit AC decisions based on service-dependent criteria. Although being oriented to flow AC, the model can easily be applied to SLS AC. SLS auditing and SLS traffic conditioning are tasks also covered.

High-performance networks require sophisticated management systems to identify sources of bottlenecks and detect faults. At the same time, the impact of network queries on the latency and bandwidth available to the applications must be minimized. Adaptive techniques can be used to control and reduce the rate of sampling of network information, reducing the amount of processed data and lessening the overhead on the network. Two adaptive sampling methods are proposed in this paper based on linear prediction and fuzzy logic. The performance of these techniques is compared with conventional sampling methods by conducting simulative experiments using Internet and videoconference traffic patterns. The adaptive techniques are significantly more flexible in their ability to dynamically adjust with fluctuations in network behavior, and in some cases they are able to reduce the sample count by as much as a factor of two while maintaining the same accuracy as the best conventional sampling interval. The results illustrate that adaptive sampling provides the potential for better monitoring, control, and management of high-performance networks with higher accuracy, lower overhead, or both.