A NEW REINFORCEMENT LEARNING BASED MULTI-AGENT METHOD FOR TRAFFIC SHAPING AND BUFFER ALLOCATION IN ROUTERS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

TAHERI TEHRANI R.; SAFAVI S.A.A.; KHARAZMI M.R.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY Year:2009 | Volume:1 | Issue:1-2 Page(s): 19-30

Download Full-Text

Persian Verion

View:

1,057

Download:

Cites:

Information Journal Paper

Title

A NEW REINFORCEMENT LEARNING BASED MULTI-AGENT METHOD FOR TRAFFIC SHAPING AND BUFFER ALLOCATION IN ROUTERS

Author(s)

TAHERI TEHRANI R. | SAFAVI S.A.A. | KHARAZMI M.R. | Issue Writer Certificate

Keywords

SIPQ4

PERFORMANCE EVALUATIONQ4

CONFIGURATIONQ4

MOBILITY MANAGEMENTQ4

Abstract

In this paper, realizing the distributed structure of computer networks, the random behaviors in such networks, and the time limitations for control algorithms, the concepts of reinforcement learning and multi-agent systems are invoked for traffic shaping and buffer allocation between various ports of a router. In fact, a new traffic shaper based on token bucket has been developed. In this traffic shaper, instead of a static token production rate, a dynamic and intelligent rate based on the network condition is specified. This results in a reasonable utilization of bandwidth while preventing traffic overload in other part of the network. Besides, based on the stated techniques a new method for dynamic buffer allocation in the ports of a router is developed. This leads to a reduction in the total number of packet dropping in the whole network. Simulation results show the effectiveness of the proposed techniques.

Cites

No record.

References

No record.

Cite

APA: Copy

TAHERI TEHRANI, R., SAFAVI, S.A.A., & KHARAZMI, M.R.. (2009). A NEW REINFORCEMENT LEARNING BASED MULTI-AGENT METHOD FOR TRAFFIC SHAPING AND BUFFER ALLOCATION IN ROUTERS. IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY, 1(1-2), 19-30. SID. https://sid.ir/paper/171496/en

Vancouver: Copy

TAHERI TEHRANI R., SAFAVI S.A.A., KHARAZMI M.R.. A NEW REINFORCEMENT LEARNING BASED MULTI-AGENT METHOD FOR TRAFFIC SHAPING AND BUFFER ALLOCATION IN ROUTERS. IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY[Internet]. 2009;1(1-2):19-30. Available from: https://sid.ir/paper/171496/en

IEEE: Copy

R. TAHERI TEHRANI, S.A.A. SAFAVI, and M.R. KHARAZMI, “A NEW REINFORCEMENT LEARNING BASED MULTI-AGENT METHOD FOR TRAFFIC SHAPING AND BUFFER ALLOCATION IN ROUTERS,” IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY, vol. 1, no. 1-2, pp. 19–30, 2009, [Online]. Available: https://sid.ir/paper/171496/en

Related Journal Papers