Thực hiện chất lượng dịch vụ trong các mạng IP (P7)

7 Measurements 0NO2I6AYTON (Know thyself; Inscription on the fac¸ade of Apollo’s temple in Delphi) The traffic engineering process for IP networks includes obtaining feedback from the network as the basis of assessing the need to modify transport network parameters and to optimize its behaviour. Obtaining information in precise and meaningful form is imperative for being able to make the right adjustments to improve the network performance. This chapter deals with the means of monitoring service quality, the analysis methods applied to measurement data, and examples of network performance optimization based on processed measurement information. A formulation of the goals and methods of traffic engineering measurements in IP networks has been given in a recent Inter-net draft [LCT +02]. This draft will be used as an introduction to the topic in this chapter, quoting other sources and adding issues specific to multi-service networks as necessary. In the draft, ISPs are singled out as one likely user for the methods presented. The scope of the conceptual framework development therein is intra-domain operations, but the definitions are intended to be transferable also across operator domain boundaries. As such, Implementing Service Quality in IP Networks Vilho Raisanen  2003 John Wiley & Sons, Ltd ISBN: 0-470-84793-X 212 MEASUREMENTS they should be applicable to different per-domain technologies as well. The need for consistency, precision and effectiveness of traffic engineering methods is cited as the reason for applying an over-arching framework for traffic engineering. The ultimate goal of measurements is to serve the needs of the traffic engineering pro-cess, including forecasting, planning, dimensioning, control, and performance monitoring. The major tasks of traffic engineering measurements are defined in [LCT + 02] as • traffic characterization; • network monitoring; • traffic control. These will be discussed in more detail later in this chapter. Let us note in passing again that in multi-service networks, the char-acterization, monitoring, and traffic control tasks may need to be applied to multiple traffic aggregates on individual links. Three approximate timescales pertaining to the use of data ob-tained with measurements are identified in [LCT + 02], namely: • Months or longer. This timescale relates to network planning and upgrading. Forecast traffic volumes per service class are impor- tant here. • Hours to days. Capacity management is the primary use of mea-surement data at these timescales. Measurement data could be used to control default routing of traffic aggregates and resource allocation in network nodes. • Minutes or less. This timescale belongs to real-time control of the network. In [LCT +02], the example of temporary rerouting of traffic aggregates to circumvent congestion is cited. As we shall see later, measurements dealing with different timescales have different requirements and analyses associated with them. Some general requirements for measurements are: • Accuracy in capturing important phenomena at different timescales. The shortest timescale of relevance to network performance on the timescale of interest must be known and the measurement methods should be chosen accordingly. In a multi-service network, the required accuracy may be different for 7.1 TRAFFIC CHARACTERIZATION 213 different traffic aggregates. For example, millisecond-accuracy measurements for delay may be desirable for VoIP, but not necessary for best-effort traffic. • Network performance should not be degraded by measurements. This applies both to the elements being measured as well as the effect of measurements and transferring of measurement data to the normal operation of a production network. Interestingly, this is an issue not only for active measurements but passive ones as well. • The amount of data generated should be moderate. Regarding the last two points, these issues are typically more challenging in a multi-service network than in a best-effort network, since there may be multiple quality support aggregates involved for each link. Subsequently, both performing the actual measurements and transmitting measurement data in such a way as not to interfere with the normal network operation need to be carefully planned. Next, we shall take a look at the three tasks of traffic engineering measurements as defined by our IETF framework [LCT + 02], and then will discuss the definition of measured characteristics, sources of information, measurement methods, and the required measurement infrastructure in general. The present chapter will be concluded with case studies. 7.1 TRAFFIC CHARACTERIZATION Traffic characterization is the first task of traffic engineering mea-surements as defined by [LCT + 02], having the following goals: • Identifying variations in traffic patterns using statistical analysis, including development of traffic profiles to capture daily, weekly, or seasonal variations. • Determining traffic distributions in the network on the basis of flows, interfaces, links, nodes, node-pairs, paths, or destinations. • Estimation of the traffic load according to service classes in different routers and the network. • Observing trends for traffic growth and forecasting of traffic demands. 214 MEASUREMENTS The determination of traffic distributions in the network is partly related to the estimation of traffic matrix discussed in Chapter 4. In other words, direct measurement can be used to obtain the topological distribution of traffic in the network. However, per-link volumes need to be linked to traffic aggregates entering and exiting the network domain in order to influence the distribution with routing control. For a best-effort IP network domain, traffic pattern variations may relate to changes in the composition of protocol types in the totality of traffic, as well as information about traffic volumes in topological context. In multi-service networks, such information should be available per service quality support aggregate. In a multi-service network with service mapping onto service quality support aggregates on the network edge, traffic engineering benefits from the ability to compare characterizations of both incoming service types and service quality support aggregates, side by side. Such a comparison makes it possible to effectively evaluate the suitability of both the service/aggregate mapping at the network edge, and the service quality support for aggregates in the network core (see Figure 7.1). Depending on the measurement methods, discussed in more detail below, modelling of data may be needed to interpret the results in a context relevant for traffic engineering. For modelling, the alternatives are fitting of measurement results into an existing model and providing generic modelling for measurement results without reference to the use context. In certain situations modelling has a risk associated with it, since it makes assumptions about what the results should look like. Thus, when modelling is used, consistency checks should be constructed to check that the situation in the measured network Service distribution Traffic load for service support aggregates ER Figure 7.1 Modelling both incoming services at the network edge and loads of service quality support bearers in the network core 7.1 TRAFFIC CHARACTERIZATION 215 is consistent with the assumptions made in the model. Another useful technique is computation of the same characteristics using multiple different methods. According to [LCT +02], Internet traffic is bandwidth-limited but non-stationary; traffic can be heavy-tailed and have strong correlations at short timescales. This is often the case in best-effort Internet with no per-flow policing. The suggestion of the Internet draft is to use decomposition of measurement results into stationary and trend parts. Obviously the stationary part also needs to account for diurnal variations in traffic intensity. Regarding the scope of this book, this breakdown should be possible per service quality support aggregate. A more fine-grained temporal classification of the problem area of traffic decomposition could be as follows: • trend prediction (days or longer periods of time). This timescale is relevant for capacity management in traffic engineering. • busy-hour traffic characterization; • statistics and correlation at the timescale of seconds. The reported measurement data should be associated with information on the scale of applicability, partly stemming from the details of the actual measurement. This requirement is valid irrespective of the possible use of modelling as a part of the measurement. An example used in the cited draft, modelling of flow burstiness can be performed by fitting measurement results into a token bucket model as the probability that a flow can be accommodated by the token bucket, as estimated from a measurement. The result, in general, can be dependent on the length of the measurement period. Thus, a full description of measurements in this case is as follows: • token bucket rate; • token bucket depth; • probability; • timescale of applicability. The timescale is of importance especially when dealing with self-similar traffic patterns, where the average magnitude of variations ranges with the length of the observation period. ... - tailieumienphi.vn