Selection of components in contemporary computer networks is extremely important, not only because of the project budget but also because of the network performance. Therefore, it is necessary to perform tests before deploying devices. In this paper the authors show that the performance of test network devices is dependent on the nature of network traffic including its statistical properties. Also a new hybrid model of the system for testing the network device based on the q-additivity of traffic flows was proposed

Bolanowski, Marek

Paszkiewicz, Andrzej

English

University of Maria Curie-Skłodowska (UMCS): Scientific e-Journals / Uniwersytet Marii Curie-Skłodowskiej: e-czasopisma naukowe

Annales UMCS Informatica AI XIII, 2 (2013) 29–36DOI: 10.2478/v10065-012-0044-9Performance test of network devicesMarek Bolanowski1∗, Andrzej Paszkiewicz1†1Department of Distributed Systems, Rzeszów University of Technology,al. Powstańców Warszawy 12, 35-959 Rzeszów, PolandAbstract – Selection of components in contemporary computer networks is extremely important, notonly because of the project budget but also because of the network performance. Therefore, it is nec-essary to perform tests before deploying devices. In this paper the authors show that the performanceof test network devices is dependent on the nature of network traffic including its statistical properties.Also a new hybrid model of the system for testing the network device based on the q-additivity oftraffic flows was proposed.1 IntroductionOne of the elements of an effective network devices choice for specific communicationtasks is to determine their performance. Currently, there are several applications andhardware solutions which are used to generate the test traffic which load the examinednetwork device. In this paper we check whether the statistical properties of the gen-erated traffic have an impact on performance of the tested device. The purchase priceof advanced, highly specialized network devices is very high. Such a purchase, withoutprevious performance tests simulating the conditions in which the equipment will work,can be very risky for the project budget. Performance testing can make a preliminaryassessment of the equipment suitability in the context of the challenges posed to it.Currently the measurements mainly relate to: one-way delay, maximum delay, delayvariation, packet loss rate, bandwidth, file transfer time, resource consumption (cpu,memory, buffers, queue) [1, 2, 3, 4, 5, 6, 7, 8].Modern methods of performance measurement can be divided into two types: theo-retical, using mathematical models of network traffic and practical - based on research∗marekb@prz.edu.pl†andrzejp@prz.edu.plPobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 20:59:01UMCS30 Performance test of network devicesresults. It is often necessary to use specialized measurement tools [9, 10]. The authorsof theoretical models consider only a simplified view of the real network, focusing on asmall number of its parameters [11, 12]. Currently used methodologies mainly focuson the topological aspects in performed tests, they often use the Gaussian distribution,analyze device performance from the point of view of a few chosen traffic parameters[13, 14]. However, it is necessary to check whether the holistic traffic has an impacton the test results of particular parameters of a device.2 Test traffic generationGeneration of traffic loading the tested device is often based on quantitative sum-mation of the partial streams, assuming a simple additivity of information streams [8].Let us assume that the intensity of streams flowing on the path i will be denoted asNPi . According to the classical approach, the total intensity of the flow will be SN :SN = NP1 +NP2 + . . .+NPi . (1)In this case, the use of non-equilibrium dynamics is more appropriate than the use,of non-extensive q-algebra which derives from the Tsallis’s entropy [15, 16]. It is basedon q–operations which lead to generalizations of the classic algebra. From the point ofview of our considerations, the most interesting is q–sum of two streams x and y whichis based on a non-extensive operator of sum ⊕q defined as:x⊕q y = x+ y + (1− q)xy. (2)Equation (2) shows that the q–sum contains an equilibrium, additive term (x + y)and dynamic non–equilibrium term (1−q)xy which symbolizes non–extensivity and thesensitivity to initial conditions [17, 18]. Taking these assumptions into account, we canconclude that the equation (1) is only a special case of the sum of simple streams withno memory effect. It is therefore purposeful to examine the sensitivity of the device totest flows which have different thermodynamic properties. Research of the real networktraffic detects the internal correlation in streams [19, 20]. The development of newnetwork services cause that traffic is characterized by a much stronger, long-rangecorrelation. Accordingly, new models of streams with a long memory were created. Toanalyze statistical, properties which are of interest to us the Hurst exponent is used.Hurst exponent (H) is an indicator used, inter alia, to classify time series. On its basis,it is possible to distinguish a random from non-random series. Its value may be withinthe range from 0 to 1. It is possible to define three ranges of the Hurst exponent:H = 0.5 – the series are random and uncorrelated events, 0 ≤ H < 0.5 – the series areantipersistent or ergodic, this range is very rare in the case of real world (real networktraffic) time series, 0.5 < H < 1, the series are persistent, have a memory effect, a lotof real world time series have such a value of H [6, 19, 20].The tests will be performed on a number of network devices with the connectionscheme presented in Fig. 1.Pobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 20:59:01UMCSMarek Bolanowski, Andrzej Paszkiewicz 31Fig. 1. Stress test – connection diagram.3 Results of the stress testsIn this scenario, on the tested network device mechanism for traffic shaping (Q◦S)was implemented and then the queues and CPU load were analyzed. The traffic wasgenerated at a constant speed equal to 10Gb/s and came from three different sources.3.1 Traffic generation using the hardware generatorIn this section the network devices were tested using the traffic coming from thehardware generator (JDSU) in which the streams and protocol parameters were definedup to 4 layers of the ISO/OSI model and varied pseudo-randomly. The JDSU generatedat the same time up to 4096 streams that totally gives traffic of 10Gb/s.The parameters of generated traffic are shown in Fig. 2 and Table 1.Fig. 2. Traffic generated with the hardware generator. The percentage ofbandwidth utilization.The traffic generated with the hardware generator JDSU is a series with a constantlevel of fluctuation. Traffic generated in this manner does not reflect fully the conditionsthat occur in real networks. The H exponent for particular process parameters receivedPobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 20:59:01UMCS32 Performance test of network devicesTable 1the minimum value for the average packet size (H = 0.099) and the maximum forthe percentage of the bandwidth use (H = 0.227). Specific Hurst exponents for thegenerated traffic are similar, within the range 0− 0.5, which indicates that there is anantipersistent or ergodic time series.Tested device was able to smoothly manage the traffic, the CPU load increase wasimperceptible, the band was formed accordance with the assumptions of the QOS rules.The main reason for such smooth traffic service by the device appears to be a simplecharacter of generated traffic in which there were no changes of the characteristicsduring the entire performance test.3.2 Traffic generation using the software generatorIn this section the network devices were tested using the traffic coming from thesoftware generator (Iperf) in which the streams and protocol parameters were definedup to 4 layers of the ISO/OSI, totally gives traffic of 10Gb/s.The parameters of generated traffic are shown in Fig. 3 and Table 2.Fig. 3. Traffic generated with the software generator. The percentage ofbandwidth utilization.The traffic generated with Iperf program traffic generator is characterized by agreater change than it was in the case of JDSU. The H exponent for particular processPobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 20:59:01UMCSMarek Bolanowski, Andrzej Paszkiewicz 33Table 2parameters received the minimum value for the average packet size (H = 0.233) andthe maximum for the number of TCP packets (H = 0.492). The values of the expo-nents H are greater than those which characterized the traffic form JDSU hardware,but still H < 0.5. The series are defined as "returning to the mean". During the testband was formed accordance with the assumptions of the QOS rules. In comparisonto the test with the JDSU generator, the network device CPU load increased in therange of 15 to 24%.3.3 Testing network device with a real trafficIn this section the network devices were tested using the real traffic coming from thereal computer network. Bandwidth saturation was significantly lower than previouscases and ranged at 1 Gb/s.The parameters of the generated traffic are shown in Fig. 4 and Table 3.Fig. 4. The real network traffic. The percentage of bandwidth utilization.Traffic from the real network has a high complexity. The frequent and major changesin its characteristics can be found. In the case of traffic from a real network H exponentfor particular process parameters received the minimum value for the number of TCPpackets (H = 0.569) and the maximum for the number of UDP (H = 0.684). Forthis traffic the value of the exponent H > 0.5, which indicates that it is persistent,keeping a positive correlation between states, indicates the existence of a trend. ItPobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 20:59:01UMCS34 Performance test of network devicesTable 3can be said that this traffic has a long-term memory. During the test a band wasnot formed in accordance with the assumptions of the QOS rules (QOS did not workproperly). Compared to the test with the JDSU generator, the network device CPUload increased in the range of 20 to 35%. Then on the test network device the QOSalgorithm was changed - from standard to Three-colour Marking. After this changethe QOS worked properly.4 ConclusionsThe research identified the relationship between the statistical properties of the trafficand the load of tested device components in particular QOS mechanisms. This allowsto conclude that apart from quantitative tests (for example the number of entries inthe routing table, the number of VPN sessions supported at the same time, the numberof supported queues, . . .) the statistical parameter of the traffic should be taken intoconsideration. The research found that the test traffic originating from a real networkled to overload the standard QOS mechanism, and enforced the use of TCM. Takinginto account the paper [18] and in particular, the properties of equation (2) the authorspropose a new model for network devices stress tests using the hybrid model shown inFig. 5.The use of real traffic, which is optimal for testing new equipment is subject to manyrestrictions: problems in its recording and playback, storage problems, the confiden-tiality of the data. The traffic from hardware and software generators is relativelysimple, contains no memory effect and poorly reflects the explosive nature of the TCPprotocol. However, it can easily provide significant load to switching matrix (or routingengine) of tested device. On the other hand, the real traffic allows to provide to a teststream a non-extensive long-term nature of the events occurring in the real networkenvironments. Therefore, the use of the model shown in Fig. 5 allows to control thecomponents of the test traffic (which is the q-sum of streams from different generatorsand real traffic) to get closer to the actual conditions in which the tested device willoperate. The task of the External Data Collector and Analyzer module is to gathertest results from all devices and make recapitulation. In the further work the authorswant to focus on developing software traffic generator that will create traffic with givenstatistical parameters.Pobrane z czasopisma Annales AI- Informatica http://ai.annales.umcs.plData: 04/08/2020 20:59:01UMCSMarek Bolanowski, Andrzej Paszkiewicz 35Fig. 5. The hybrid model of the system for testing the network device.References[1] Comer D. 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Performance test of network devices

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Performance test of network devices

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