METHOD AND APPARATUS FOR MODELING NETWORK TRAFFIC

• US 20110310768A1
• Filed: 06/17/2011
• Published: 12/22/2011
• Est. Priority Date: 06/18/2010
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: SHIN, Kwang Sik
• Application Number:
  US13/163,383
• Publication Number:
  US 20110310768A1
• Time in Patent Office: Days
• US Class Current: 370/253
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 43/026 using flow identification
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 43/50 Testing arrangements
• CPC Class Codes: Y02D 30/50 in wire-line communication networks, e.g. low power modes or reduced link rate

Abstract

A method for modeling network traffic includes: collecting traffic of data transmitted from a network; extracting a traffic density value based on any one of the data size, the packet size, and the IDT of the collected traffic and obtaining the probability density distribution on the raw domain; separating the data into the major dataset that is a group of data having the density value of the threshold value or more and the minor dataset that is a group of data having the data density value less than the threshold value; transforming the major dataset separated on the raw domain onto the major dataset domain formed to exclude a period corresponding to the data density value of a threshold value or less; and obtaining a major dataset analysis model by applying a graph fitting algorithm on the major dataset on the major dataset domain.

Claims

1. A method for modeling network traffic, comprising:

collecting traffic of data transmitted from a network;

extracting a traffic density value based on any one of the data size, the packet size, and the IDT of the collected traffic and obtaining the probability density distribution on the raw domain;

separating the data into the major dataset that is a group of data having the density value of the threshold value or more and the minor dataset that is a group of data having the data density value less than the threshold value;

transforming the major dataset separated on the raw domain into the dataset on the major dataset domain formed to exclude the minor dataset period; and

obtaining a major dataset analysis model by applying a graph fitting algorithm on the transformed major dataset.

2. The method of claim 1, further comprising obtaining data density distribution on the major dataset domain by using the major dataset analysis model and inversely transforming the obtained data density distribution into the raw domain to obtain the regenerated major traffic.

3. The method of claim 2, wherein the threshold value is a moving average value for the probability density distribution.

4. The method of claim 1, wherein the transforming includes generating a major dataset transformation table indicating the relationship when the major dataset on the raw domain is transformed into the major dataset on the major dataset domain.

5. The method of claim 1, further comprising:

separating the data of the minor dataset into a minor-major dataset having a density value of a new threshold value or more different from the threshold value and a minor-minor dataset that is a group of data having the data density value less than the new threshold value;

transforming the minor-major dataset on the raw domain into the dataset on the minor dataset domain formed to exclude a period corresponding to the data density value of the new threshold value or less; and

obtaining a minor-major dataset analysis model by applying a graph fitting algorithm on the transformed minor dataset.

6. The method of claim 5, further comprising:

obtaining data density distribution on the minor dataset domain by using the minor-major dataset analysis model and inversely transforming the obtained data density distribution onto the raw domain to obtain the minor traffic; and

obtaining regenerated traffic for test by integrating the regenerated major traffic and the regenerated minor traffic.

7. The method of claim 5, further comprising repeatedly performing at least once steps of separating into the minor-minor dataset;

transforming the dataset on the minor dataset domain; and

obtaining the minor-major dataset analysis model.

8. The method of claim 5, further comprising generating a minor dataset transformation table indicating the relationship when the minor dataset on the raw domain is transformed into the minor dataset on the minor dataset domain.

9. The method of claim 1, wherein the density of the traffic is any one of probability density of traffic amount per a data size, probability density of traffic amount per a packet size, and probability density of traffic amount per a unit time according to the selected reference.

10. An apparatus for modeling network traffic, comprising:

a collector collecting traffic of data transmitted from a network;

an extractor extracting a collected traffic density value based on any one of the data size, the packet size, and the IDT and obtaining the probability density distribution on the raw domain;

a traffic modeling module separating the data into the major dataset that is a group of data having the density value of the threshold value or more and the minor dataset that is a group of data having the data density value less than the threshold value and obtaining a mathematical analysis model by modeling the major dataset.

11. The apparatus of claim 10, further comprising a repeat execution determining unit that receives the major dataset, the minor dataset, and the mathematical analysis model to analyze at least of them in order to determine whether the modeling is repeatedly executed and if it is determined that the repeat execution is needed, provides the minor dataset to the extractor.

12. The apparatus of claim 11, wherein the extractor receives the minor dataset from the repeat execution determining unit and extracts the traffic density value based on any one of the data size, the packet size, and the IDT of the minor dataset and obtains the probability density distribution based on the raw domain if it is determined that the repeat execution is needed.

13. The apparatus of claim 11, further comprising a regenerator generating regenerated traffic for test by receiving mathematical analysis model and the transformation table from the traffic modeling module or the repeat execution determining unit to obtain the data distribution using the mathematical analysis model and inversely transform the obtained data distribution using the transformation table to generate the regenerated traffic for a test.

14. The apparatus of claim 11, wherein the repeat execution determining unit compares the collected traffic with the regenerated traffic for testing to determine whether they are similar to each other in order to determine whether the modeling is repeatedly executed and determine whether the repeat execution of the modeling is made according to the result.

15. The apparatus of claim 11, wherein the repeat execution determining unit performs the repeat execution when the difference between the largest density value of the major dataset and the smallest density value is a predetermined threshold value different from the threshold value.

16. The apparatus of claim 11, wherein the repeat execution determining unit is based on the repeat execution but may be configured so that the repeat execution ends when the total sum of the data density of the remaining dataset after the separation is the predetermined value or less.

17. The apparatus of claim 13, wherein the regenerator receives two or more mathematical analysis model according to the repeat execution of the modeling from the traffic modeling module or the repeat execution determining unit and the conversion table corresponding to each analysis model when the repeat execution is made to generate two or more regenerated data according to each analysis model and integrate them, thereby generating the regenerated traffic for test.

18. The apparatus of claim 10, wherein the traffic modeling module includes:

a separator that separates the data into the major dataset that is a group of data having the density value of the threshold value and the minor dataset that is a group of data having the data density value less than the threshold value;

a domain transformer that transforms the major dataset on the raw domain into a dataset on the major dataset domain formed to exclude the minor dataset; and

a graph fitting unit that obtains the mathematical analysis model represented by a sum of a plurality of random distribution functions by applying a graph fitting algorithm on the transformed major dataset.

19. The apparatus of claim 18, wherein the separator includes a moving average calculator that calculates a moving average value for the data density value, and separates the major dataset from the minor dataset by using the moving average value as the threshold value.

20. The apparatus of claim 18, wherein the traffic modeling module includes a transformation table generator generating a transformation table indicating the relation that the major dataset on the raw domain is transformed into the major dataset on the major dataset domain.