I.V. Sychev IEEE Conference Publishing
21 Ignatevskoe shosse Blagoveschensk, 675027 Russia
Abstract-This publication gives new results in short time measurement of networking flow. A new method for realistic measurements in short time experiments is designed.
I. INTRODUCTION
The main idea is that long memory property of self-similar (fractal) traffic is able to help forecasting traffic for the purpose of quality of service (QoS) provision. But for pragmatic mathematical forecasting and modeling it needs an accurate information of the real process, especially in measuring high resolution time. Common information about researched types of experiments is presented in part II. At the part III necessity and reasons of building new measurement system are presented. In the same part readers can find general results of measurements. Part IV describes main evidence of pseudo-fractal (caused by measuring error) property in short time experiment. Part V provides explanation of the experimental environment to ensure that the main idea of time measuring in networking has no influence from hardware. And general conclusions are presented in part VI.
II. NETWORKING MEASUREMENT EXPERIMENTS References [1-11] describe two kinds of networking traffic measurement experiments:
- Long time experiments, as in [6], [9]; collecting traffic for years (using a special database) with integral time resolution (e.g. all traffic every 300 seconds, when 300 seconds is the smallest fragment of measurement).
- Short time experiments, as in [1], [2], [3], [4], [7], [10];
when every traffic package is captured; in these experiments timestamps should have high resolution.
It is very attractive to use short time experiment, since it economizes human time and machine resources - but only if the timestamps are correct. Note that in modern personal computer (PC) it is possible to receive more than one package at 18,20648… Hz – this is the normal PC clock time resolution.
In the referenced articles conclusions are mostly made on the basis of short-time range experiments. Moreover, the experiments use only one measuring device.
There are two kinds of PC traffic measurement systems:
- standard OS equipment programs and hardware;
- special programs and hardware designed for measuring
purpose.
When data are measured, the majority of researchers build traffic simulation systems; then measurements are compared with simulated data and the difference is observed.
The author of this article proposes three stage experiments:
fractal traffic simulation, measurements using standard methods and experiments with programs and hardware especially designed for measuring purpose.
Main points of work on the experiments are:
To design a new traffic measuring method to reduce the disadvantages of well known means for network-flow analysis.
To design a new measuring system. Field experiments analysis might ensure traffic self-similarity property.
To design an imitation model of informational systems with client-server architecture, and take into account self-similar data flow.
To compare the designed model with traditional exponential models of queue theory and define fields of models preferred usage.
III. SPECIAL REAL TIME TRAFFIC MEASURING SYSTEM Results cannot be considered reliable if they are significantly influenced by the measuring device.
Multi-user multitasking operating systems are interrupted by running processes and thus delay networking tasks unpredictably. Furthermore, reprogramming hardware clocks (for monitoring purpose) for faster run may cause problems with other running programs. Possibly programmers avoid reprogramming the clock and simply randomize the timer resolution that is left (for identification purpose). Therefore there is no guarantee that timestamps have high resolution.
This may lead to non-realistic conclusions about self-similarity of traffic.
For traffic measuring purpose it’s suggested to use device with only one task – measurement.
A new measuring system was built for ISO/OSI level 2 and level 3 to determine whether the flow of traffic is realistic or not.
In experiments with the standard way of time measuring (using PC clocks) it was noted that the error of such measurements is additive by time. This error comes from processing the interrupt routine. Also an error appears as the result of extra interrupts (timer calls) while networking
T. Sobh et al. (eds.), Innovative Algorithms and Techniques in Automation, Industrial Electronics and Telecommunications, 171–173.
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interrupts are processed. The best solution of this problem is to get timestamp data at the moment of the networking event and to avoid the usage of the PC timer. The proposal is to use the Pentium instruction “Rdtsc”: it returns the count of ticks since processor reset (64 bit) and does not depend on the CPU load. For accurate measurements the program “M2” was built.
The program is copyrighted by Federal service for intellectual property patents and trademarks, Russian Federation, (Rospatent, www.fips.ru). The full name is “The program for research of properties of self-similar traffic in real time in Ethernet 10/100BaseT network, with time resolution 10-6 seconds”
.
Registration No of the program is: 2006613266.This M2 program functions to:
- capture time of received packages
- capture time of packages in the net card buffer - send measured data to the program-receiver The program also has the following properties:
- uses a counter of completed CPU ticks from the beginning of its run (that is much more reliable than using timer interrupts);
- uses 32-bit registers;
- interacts with the package driver of a net cards for 10 and 100 megabits per second.
Time is converted from CPU ticks to SI units; the time resolution is 10E-8 seconds. Conversion depends on the frequency of the CPU. Frequency does not give accurate values as it depends on temperature, power block voltage and electro magnetic emissions; apparently, there are three ways of possible correction:
- extra software for CPU frequency measurements.
Disadvantage of this method is that there is no guarantee that a measured value will not change during the experiment.
- “on the fly” measurement to calculate CPU frequency while running a measurement program. Disadvantages of this method are excessive data for the transfer of measured values and additionally the complexity of the measuring software.
- calculation of a correction by the approximating the flow dynamic; this is the most accurate method, especially in approximating the difference of two measuring systems.
It is proposed to compare the difference in time of the
“tcpdump” program and M2 after the conversion of tcpdump data to SI. A fragment of measured data is presented in table 1. Though clock start moment in M2 and tcpdump differs, it has no sense to synchronize the starting moment of both systems: in order to eliminate the difference we can use simple method of constant value subtracting.
Presented data fragment contains data about measured time from 10 packages (numbers of packages are 50 to 60) that can be seen on fig. 1 and fig. 2 (x-axis). This fragment was picked randomly, since including all the measured data would overwhelm frames of this publication. All the presented data are SI units (seconds).
Graphic shown on Fig. 1. is a linear increasing or decreasing function (before correction) of index i=1..N, where
“i” is the number of received/sent packages. The values of this function are the times of receiving/sending a package.
From these values we subtract the corresponding values of a linear approximation. Thus, as corrected CPU frequency we use the projection on the axis.
The result of projection is shown on Fig. 2, and presents a pseudo-fractal (self-similar) process caused by measuring error of the tcpdump system.
IV. EVIDENCE OF PSEUDO-FRACTAL PROPERTY Obviously pseudo-fractal property in short time experiment appears as a result of inaccurate time measuring. Wrong conclusions can be made when using the well-known
“tcpdump” as in works in [1],[2],[3],[4],[5]. Reference [4]
describes an algorithm of correction tcpdump package lose, but there is no information on time measuring. In the reference [5] the same form pulsations on very small time ranges (6 seconds) are described and named self-similar. That is most probably not realistic.
Fig. 2 shows pulsations that seem to be self-similar not only visually, but can be proved statistically on some segments, e.g. using the Hurst parameter described in [1],[4],[7],[9],[10],[11]. Description of Herst parameter mathematic runs out of this paper frames, but it is sufficiently represented in abovementioned references.
For calculating the Hurst parameter pi was taken as the proportional coefficient on a flow of 654 measurements.
x2
Package number x2 Time (104 seconds)
Fig. 1. Difference between two measurement systems.
SYCHEV 172
0 50 100 150 200 250 300 350 400 450 500 550 600
The data represented by Fig. 2, have Hurst parameter 0.12015, range (R) 4.283919 and standard deviation (S) 1.713238. The obtained data show obvious anti-persistent property of the flow. Thus first, the flow can be predicted with a degree of high probability and, second, the flow can be classified as self-similar.
The described error of traffic measurement is the most probable reason for wrong conclusions about traffic flows’
self-similarity property in short-time experiments.
V. THE EXPERIMENTAL ENVIRONMENT
A scheme of network components connection, shown on fig. 4, does not require special equipment and made of standard components.
Qualitatively new result described in part IV does not depend on the net topology. The major idea of presented installation is to use additional measuring systems functioning in the client-server channel. In case of accurate real time operating system usage proposed mechanism can be integrated in operating system of client and server.
Tests of hardware for installation were conducted in the following sequence: IBM GL300 PCs, then Advanced Logic Research (ALR) SD i486, then different laptops and finally Intel Pentium-4 based computers.
Software M2 on host-3 and tcpdump on host-4 are running in promiscuous mode and capture all transmitted packages between host-1 and host-2. The collected data is transferred by extra link. Then it is transmitted by portions in certain guaranteed time (this time is extremely small in comparison with time of experiment duration, and therefore can be disdained) to the data receiver machine that simultaneously can be a client of analyzed network connection. Fig. 3.
presents appearance of installation.
Fig. 3. Appearance of the installation.
Fig. 4. Measurement system.
VI. CONCLUSIONS.
Obtained knowledge can be effectively used for enhancing QoS in global networks. Moreover, it can assist scientists to defeat research based on non-realistic data.
Conducted work gave the following outcomes:
- possible networking experiments and their stages are analyzed;
- new method of accurate time measurement in networking traffic is designed, and the program product that realizes the method is described;
- the opportunity of usage of Herst parameter – well-known metric for determining self-similarity property of process – is shown;
- discovered possibility of wrong interpretation of obtained data;
- shown basic points for realistic conclusions in conducting small-time measurement experiments.
REFERENCES
[1] D.E.Sokolov, N.G.Trenogin. Net traffic fractal features in client-server informational system, SybSTU, 2003.
[2] V.V.Platov,V.V.Petrov. Research of self-similar structure of teletraffic, 2004.
[3] V.V.Petrov. Teletraffic structure and algorithm of quality of service with self-similar influence, dissertation, Moscow, 2004.
[4] Peter Haga, Peter Pollner, Gabor Simon, Istvan Csabai, Gabor Vattay, Self-generated Self-similar Traffic, Communication Networks Laboratory, Eotvos Lorand University, 2004.
[5] Vern Paxson, Experiences With Internet Traffic Measurement and Analysis, ICSI Center for Internet Research International Computer Science Institute and Lawrence Berkeley National Laboratory, 2004.
[6] Will E. Leland, Murad S. Taqqu, Walter Willinger, Daniel V. Wilson,
“On the Self-Similar Nature of Ethernet Traffic”, 1993.
[7] Vitaly Petroff, “Self-Similar Network Traffic: From Chaos and Fractals to Forecasting and QoS”, 2003.
[8] Sergejs Ilnickis, “M/M/1 and G/M/1 systems with a self-similar input traffic”, 2004.
[9] Sergejs Ilnickis, “Research of the network server in Self-similar traffic environment”, 2004.
[10] Kihong Park, Walter Willinger, “SelfSimilar Network Traffic: An Overview”, 2000.
[11] Pradeep Ramakrishnan, “Self-Similar Traffic Models”, 1999.
Host-1 Time (104 seconds)
Fig. 2.Difference after correction of theprocessorfrequency.
PROBLEM OF ACCURATE TIME MEASUREMENT 173
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