GPU - Accelerated fluid flow approximation of the Active Queues Management algorithms

• Autorzy: Domański A. , Domańska J. , Czachórski T.

Warianty tytułu

PL

Aproksymacja płynna algorytmów AQM - wspomagana przez GPU

• Języki publikacji: EN

Abstrakty

EN

In the article we study a model of TCP connection with Active Queue Management in an intermediate IP router. We use the fluid flow approximation technique to model the interactions between the set of TCP flows and AQM algoithms. Computations for fluid flow approximation model are performed in the CUDA environment.

PL

Artykuł opisuje zastosowanie aproksymacji płynnej do modelowania interakcji pomiędzy zbiorem strumieni TCP, a mechanizmami aktywnego zarządzania buforami (AQM). Obliczenia zostały przeprowadzone w środowisku GPU. Wyniki przedstawione w artykule potwierdzają przewagę algorytmu CHOKe nad standardowym algorytmem AQM: mechanizmem RED.

Słowa kluczowe

EN

computer networks; active queue management; CUDA environment

• Wydawca: Instytut Informatyki Teoretycznej i Stosowanej Polskiej Akademii Nauk
• Czasopismo: Theoretical and Applied Informatics
• Rocznik: 2013
• Tom: Vol. 25, No. 2
• Strony: 93--104
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Domański A.

• Institute of Informatics Silesian Technical University Akademicka 16, 44-100 Gliwice, Poland

autor

Domańska J.

• Institute of Theoretical and Applied Informatics Polish Academy of Sciences

autor

Czachórski T.

• Institute of Theoretical and Applied Informatics Polish Academy of Sciences

Bibliografia

• 1.D. R. Augustyn, A. Domański, J. Domańska, Active Queue Management with non linear packet dropping function, 6th International Conference on Performance Modelling and Evaluation of Heterogeneous Networks HET-NETs 2010.
• 2.D. R. Augustyn, A. Domański, J. Domańska, A Choice of Optimal Packet Dropping Function for Active Queue Management, Communications in Computer and Information Science, vol. 79, Springer 2010.
• 3.W. Chang Feng, D. Kandlur, and D. Saha, Adaptive packet marking for maintaining end to end throughput in a differentiated service internet, IEEE/ACM Transactions on Networking, vol. 7, no. 5, 1999.
• 4.J. Chen, F. Paganini, R. Wang, M.Y. Sanadidi, M. Gerla, Fluid-flow Analysis of TCP Westwood with RED, GLOBECOM 2004.
• 5.T. Czachórski, K. Grochla, F. Pekergin, Stability and Dynamics of TCP-NCR (DCR) protocol in presence of UDP Flows, in: Wireless Systems and Mobility in Next Generation Internet, LNCS no. 4396, pp. 241-254, Springer 2007.
• 6.J. Domańska, A. Domański, T. Czachórski, The Drop-From-Front Strategy in AQM, Lecture Notes in Computer Science, vol. 4712/2007, Springer Berlin/Heidelberg, 2007.
• 7.J. Domańska, A. Domański, T. Czachórski, Implementation of modified AQM mechanisms in IP routers', Journal of Communications Software and Systems, vol. 4, no. 1, March 2008.
• 8.A. Eshete, Y. Jiang, Generalizing the CHOKe flow protection, Computer Network Journal, 2012.
• 9.C. V. Hollot, V. Misra, D. Towsley, A control theoretic analysis of RED, IEEE/ INFOCOM, 2001.
• 10. L C. V. Hollot, V. Misra, D. Towsley, W.-B. Gong, On Designing Improved Controllers for AQM Routers Supporting TCP Flows, IEEE INFOCOM 2002.
• 11. C. V. Hollot, V. Misra, D. Towsley, W.-B. Gong, Fluid methods for modeling large heterogeneus. NTIS, 2005.
• 12. C. Kiddle, R. Simmonds, C. Williamson, B. Unger, Hybrid packet/fluid flow network simulation, Parallel and Distributed Simulation, 2003.
• 13. C. Liu, R. Jain, Improving explicit congestion notification with the mark-front strategy. Computer Networks, 35(2-3), 2000.
• 14. M. May, C. Diot, B. Lyles, J. Bolot, Influence of active queue management parameters on aggregate traffic performance, Technical report, Research Report, Instituí de Recherche en Informatique et en Automatique, 2000.
• 15.V. Misra, W.-B. Gong, D. Towsley, Fluid-based Analysis of a Network of AQM Routers Supporting TCP Flows with an Application to RED, ACM SIGCOMM, 2000.
• 16.R. Pan, B. Prabhakar, K. Psounis, CHOKe, A stateless AQM scheme for approximating fair bandwidth allocation, IEEE INFOCOM, 942-952, 2000.
• 17.Pengxuan Mao, Yang Xiao, Shaohai Hu, Kiseon Kim, Stable parameter settings for PI router mixing TCP and UDP traffic, IEEE 10th International Conference on Signal Processing (ICSP), 2010.
• 18.www.scipy.org.
• 19.S. Rahme, Y. Labit, F. Gouaisbaut, An unknown input sliding observer for anomaly detection in TCP/IP networks, Ultra Modern Telecommunications & Workshops, 2009.
• 20.L. Wang, Z. Li, Y.-P. Chen, K. Xue, Fluid-based stability analysis of mixed TCP and UDP traffic under RED, 10th IEEE International Conference on Engineering of Complex Computer Systems, 2005.
• 21.T. K. Yung, J. Martin, M. Takai, R. Bagrodia, Integration of fluid-based analytical model with Packet-Level Simulation for Analysis of Computer Networks, SPIE, 2001.
• 22.NVIDIA Corporation. CUDA Programming Guide. NVIDIA Corporation, 2012. http:// www.nvidia.com/
• 23.http://kered.org/blog/2009-04-13/easy-python-numpy-cuda-cublas/