Network availability prediction can it be done

NAP automatically determines the allowable paths between a source and target as well as the minimal cut sets that determine the availability of the network. If required, you can restrict the analysis to network paths traversing a limited number of network elements limiting the 'hop' number to eliminate unrealistic 'snake-like' paths in complex networks.

ARMS Reliability is an authorised distributor of those products, and a trainer in respect of their use. Talk to us about how we can help you improve network operability. We're ready to help. Leading innovation and digitization for world-class Asset Management. This unsatisfactory performance of wavelet-based prediction may be due to the effect of boundary conditions when applying wavelet transform to a finite length time series [ 27 ].

We did not study the boundary conditions in this paper, and interested readers can refer to [ 27 ] for a detailed study on effects of boundary conditions. Figure 7 shows the performance of different traces from the University of Auckland. Most of the predictors have NMSE less than 1 which indicates that traffic in these traces is also predictable. The last-value predictor performs very badly.

Its NMSE value is greater than 1 for many traces. The best prediction performance for these traces is shown by the ARMA predictor. This result agrees with the previous research [ 31 , 39 ] which has argued that the ARMA predictor is suitable for the traffic since it is capable of capturing both short- and long-range dependence.

ANN also performs well for these traces. It consumes more resources without providing any additional benefits.

The third best predictor in these traces is DES. DES is the cheapest among predictors except LV. So, the situations where the cost of predictor matters, DES seems to provide a very good balance between cost and complexity.

Also, note that, for traces au5, au6, and au18, none of the predictors show reasonable performance. In fact, these three are the traces which show limited predictability when we studied the ACF characteristics. The plot in Figure 5 c is for au18 trace. Figure 8 shows how the predictors perform for BC traces. For the fourth trace, the NMSE value is very close to zero for all the predictors. Trace bc4 captures only external traffic and contains long periods of inactivity.

So, most of the predictors show very good behavior for this trace. Figure 9 shows the effect of increasing the prediction interval on performance of the predictor for au7 trace. Increasing the interval size works as a low-pass filter and filters out high-frequency noise. Short-term traffic variations are smoothed out and thus, the performance of all the predictors increases with increasing the prediction interval.

All other traces also exhibit the same behavior. Previous study [ 31 ] reported that the predictability graphs show concave behavior, i. We did not observe any such behavior, and all the traces consistently exhibit behavior of Figure 9. Increasing the prediction interval decreases the number of data points in the training portion which might affect accuracy. Researchers have provided a detailed study of effect of changing size of training data on prediction results [ 21 ].

One of our main objectives in this paper is to find a predictor which accurately predicts traffic while consuming the minimum amount of power. Table 1 shows the computations and data storage requirements of these predictors.

In this study, we focus only on power and performance overhead during the prediction phase. A predictor needs to be trained only once and that overhead can be ignored.

In other situations, where traffic behavior changes over time, we may need to retrain the predictors. But, this training is required very rarely as previous research has shown that traffic behavior remains steady over time [ 31 , 39 ]. We implemented these predictors in software and measured the performance and energy overhead of these software predictors on a simple 2-issue processor.

Table 2 shows the specification of the processor. We used a one-hour long trace and measured the performance and power using a GEMS full-system simulator. Table 3 shows instructions per prediction and energy per prediction for each type of predictor when the predictors are implemented in software. We see that ANN-based and wavelet-based predictors require considerably more instructions than other predictors.

We measured the total energy consumed for the execution of traffic trace and divided the total energy by the number of predictions to get the energy per prediction. But, when comparing energy consumption, we can see that DES is the lowest power-consuming predictor.

It is also comparable in performance to the high-cost predictors like ANN which makes this very useful for applications like one-step-ahead traffic prediction for power management.

Although ANN performs well in most situations, the power and performance cost associated with it make it suitable only for offline applications like network design and capacity planning. The accuracy and energy consumption results for all the studied predictors are presented in Sections 5. This metric is defined based on a general technique of combining multiple metrics into a global measure described in [ 42 ]. For example, gives equal weight to energy and error. Relative weights can be set based on the particular application and scenario.

For our application, we give equal weight to accuracy and energy consumption. As we are trying to minimize both energy and error, a predictor with the lowest value of EE-Score will be the winner. Figure 10 shows the result of the EE-Score for all the techniques studied. This number is calculated based on average NMSE and average energy consumption per prediction across all the traces. These numbers are normalized and combined using equation 2 to get the EE-Score. Note that EE-Score of 0 means the performance of the average predictor.

Our goal is to minimize the EE-Score so that both error and energy consumption metrics are minimized. Note that LV performs poorly because it has least accuracy and wavelet performs badly because it has the highest cost without giving any benefit in accuracy. We have provided a performance and power comparison of three different classes of predictors using a large number of real network traces.

Our results indicate that network traffic is generally predictable. Furthermore, the choice of the predictor is dependent on the characteristics of the network.

We found different predictors suitable for traces from different sources. The same predictor performs consistently well for all the traces from the same source. The ANN-based predictor performed consistently well but has high power and computation overhead. We have proposed a new metric to combine accuracy and power consumption into a single number.

Based on this metric, DES emerged as the predictor of choice when accuracy and energy consumption are viewed collectively. The network traces used in this study are taken from three different sources. The authors declare that there are no conflicts of interest regarding the publication of this paper.

This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles. Journal overview. Special Issues. Academic Editor: Zhiyong Xu. Received 15 Aug Accepted 08 Jan Published 04 Feb Abstract Accurate real-time traffic prediction is required in many networking applications like dynamic resource allocation and power management.

Introduction Internet traffic has grown tremendously in the past decade due to advent of new technologies, industries, and applications. Related Work Accurate traffic prediction is useful in numerous networking applications. We can categorize these predictors into three broad classes: 2. Time Series Predictors Many researchers have used time series models for predicting network [ 20 , 21 ].

Neural Networks-Based Predictors Artificial intelligence and neural networks have also found applications in network traffic prediction.

Wavelet Transform-Based Predictors Some applications require prediction of traffic at different resolutions in different situations. Traffic Prediction Techniques In this section, we briefly describe representative predictors in the three categories of traffic prediction techniques.

Classic Time Series Predictors 3. Windowed Moving Average MA In this technique, the average of n past observations is used as the prediction for the next interval. Table 1. Figure 1. Figure 2. Figure 3. Figure 4. Figure 5.

Auto-correlation factor analysis of different traces. B is the ACF plot of majority of predictors in our traces. Figure 6. The prediction interval used is milliseconds. Figure 7. Job Title. Country Please Select Industry Please Select Do you consent to Isograph storing your contact details? Please note that we only send installation passwords by email and to a verifiable email address. Powerful pagination in NAP Multiple direction flow of data in the networks.

Parts library and network element libraries are included. Multiple import and export facilities. Benefits NAP will automatically determine the allowable paths between a source and target.