PSO-Vegas: PSO-based enhanced Vegas

• Autorzy: Jamali S. , Eftekhari A.

Warianty tytułu

PL

PSO-Vegas: algorytm Vegas z poprawioną PSO

• Języki publikacji: EN

Abstrakty

EN

Biology is hosting many self-organizing processes. These processes can be studied by researchers to employ their principles as an inspirational metaphor to offer new solutions for different scientific problems. We follow such an inspiration here, to improve TCP Vegas algorithm. It has been confirmed that TCP Vegas has higher performance in compare with TCP Reno. However, TCP Vegas has several problems that affect its performance in congestion avoidance phase. Fixed values for α and β are one of the most important weaknesses of TCP Vegas. Ideally, α and β should be function of network conditions. For this purpose, this paper presents a PSO-based modified Vegas algorithm, which adjusts its parameters i.e. α and β to present good performance compared to Vegas. The simulation results show that the performance of the proposed algorithm is much better than TCP Vegas.

PL

W biologii występuje wiele samoorganizujących się procesów. Takie procesy mogą być studiowane jako metoda rozwiązywania wielu różnych problemów naukowych. Artykuł przedstawia ulepszenie algorytmu TCP Vegas. Zostało potwierdzone, że TCP Vegas ma lepsze parametry niż TCP Reno. Jednak samo TCP Vegas ma też słabości - jedną z nich jest stała wartość α i β. W warunkach idealnych α i β powinny być funkcją warunków sieci. W tym celu w pracy przedstawia się zmodyfikowany algorytm Vegas z możliwością ustawiania parametrów α i β. Symulacje wykazały, że nowy algorytm ma lepsze właściwości niż TCP Vegas

Słowa kluczowe

EN

communication network; congestion control; Vegas; PSO; Vegas and PSO

PL

sieci komunikacyjne; kontrola zatorów; Vegas

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2011
• Tom: R. 87, nr 1
• Strony: 199--203
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Jamali S.

autor

Eftekhari A.

• University of Mohaghegh Ardabili, Ardabil, Iran,

Bibliografia

• [1] F. Dressler, Ö. B. Akan, A survey on bio-inspired networking, Computer Networks, 2010.
• [2] M. Analoui, S. Jamali, Bio-Inspired Congestion Control: Conceptual Framework, Algorithm and Discussion, Advances in Biologically Inspired Information Systems: Models,Methods, and Tools, Studies in Computational Intelligence (SCI), Springer, 2007.
• [3] M. Analoui, S. Jamali, Congestion control in the internet: inspiration from balanced food chains in the nature, Journal of Network and Systems Management, 2008.
• [4] R. Eberhart, J. Kennedy, A New Optimizer Using Particles Swarm Theory, International Symposium on Micro Machine and Human Science , IEEE Service Center, 1995.
• [5] J. Kennedy, R. Eberhart, Particle Swarm Optimization, IEEE International Conference on Neural Networks, 1995.
• [6] Y. Shi, R. Eberhart, ,Parameter Selection in Particle Swarm Optimization, Conference on Evolutionary Programming, 1998.
• [7] L.S. Brakmo, L.L. Peterson, TCP Vegas: end to end congestion avoidance on a global Internet, IEEE Journal of Selected Areas in Communication, 1995.
• [8] V. Jacobson, Congestion avoidance and control, ACM SIGCOMM, 1988.
• [9] T. Bonald, Comparison of TCP Reno and TCP Vegas via fluid approximation, Technical Report, 1998.
• [10] J. Mo, R.J. La, V. Anantharam, J.C. Walrand, Analysis and comparison of TCP Reno and Vegas, INFOCOM, 1999.
• [11] S.H. Low, L.L. Peterson, L. Wang, Understanding Vegas: a duality model, Journal of ACM, 2002.
• [12] D. Kim, H. Bae, C.K. Toh, Improving TCP-Vegas performance over MANET routing protocols, IEEE Transactions on Vehicular Technology, 2007.
• [13] L. Ding, X. Wang, Y. Xu, W. Zhang, Improve throughput of TCP-Vegas in multihop ad hoc networks, Computer Communications, 2008
• [14] Y. Chan, C. Lin, Quick Vegas: Improving performance of TCP Vegas for high bandwidth-delay product networks, IEICE Transactions on Communications, 2008.
• [15] N. Bigdeli, M. Haeri, AQM controller design for networks supporting TCP Vegas: A control theoretical approach, ISA Transactions, 2008.
• [16] S. H. Zahiria, S. A. Seyedin, Swarm intelligence based classifiers, Journal of Franklin Institute, 2007.
• [17] H.I. Ali, S.B. Mohd Noor, S.M., Pso-based robust H∞ controller design using cascade compensation network, Journal of IEICE Electronics Express, 2010.
• [18] R.N. Ray, D. Chatterjee, S.W.Goswami, A PSO-based optimal switching technique for voltage harmonic reduction of multilevel inverter, Journal of Expert Systems with Applications, 2010.
• [19] D. A. Menasce, A. F. Almeida, Capacity Planning for Web Services, Metric, Models, and Methods, Prentice-Hall, 2002.
• [20] Ns-2. Network Simulator, http://www.isi.edu/nsnam/ns.
• [21] S. Floyd, V. Jacobson, Random early detection gateways for congestion avoidance, IEEE/ACM Transactions on Networking, 1993.