Commenced in January 2007
Paper Count: 30184
SMCC: Self-Managing Congestion Control Algorithm
Abstract:Transmission control protocol (TCP) Vegas detects network congestion in the early stage and successfully prevents periodic packet loss that usually occurs in TCP Reno. It has been demonstrated that TCP Vegas outperforms TCP Reno in many aspects. However, TCP Vegas suffers several problems that affect its congestion avoidance mechanism. One of the most important weaknesses in TCP Vegas is that alpha and beta depend on a good expected throughput estimate, which as we have seen, depends on a good minimum RTT estimate. In order to make the system more robust alpha and beta must be made responsive to network conditions (they are currently chosen statically). This paper proposes a modified Vegas algorithm, which can be adjusted to present good performance compared to other transmission control protocols (TCPs). In order to do this, we use PSO algorithm to tune alpha and beta. The simulation results validate the advantages of the proposed algorithm in term of performance.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330039Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1121
 V. Jacobson, Congestion avoidance and control, in: ACM SIGCOMM_88, Stanford, CA, 1988, pp. 314-329.
 L.S. Brakmo, L.L. Peterson, TCP Vegas: end to end congestion avoidance on a global Internet, IEEE J. Select. Areas Commun. 13 (8) (1995) 1465-1480.
 T. Bonald,Comparison of TCP Reno and TCP Vegas via fluid approximation, Tech. Rep. RR, 3563, 1998.
 J. Mo, R.J. La, V. Anantharam, J.C. Walrand, Analysis and comparison of TCP Reno and Vegas, in: INFOCOM, vol. 3, 1999, pp. 1556-1563.
 S.H. Low, L.L. Peterson, L. Wang, Understanding Vegas: a duality model, J. ACM 49 (2002) 207-235.
 E. H. Miller, "A note on reflector arrays (Periodical styleÔÇöAccepted for publication)," IEEE Trans. Antennas Propagat., to be published.
 J. Wang, "Fundamentals of erbium-doped fiber amplifiers arrays (Periodical styleÔÇöSubmitted for publication)," IEEE J. Quantum Electron., submitted for publication.
 R. Eberhart, and J. Kennedy, "A New Optimizer Using Particles Swarm Theory, Proc. Sixth International Symposium on Micro Machine and Human Science , IEEE Service Center, Piscataway, NJ, 39-43, 1995.
 J. Kennedy and R. Eberhart, Particle Swarm Optimization, IEEE International Conference on Neural Networks NJ, IV: 1942-1948, 1995.
 Y. Shi, R. Eberhart, Parameter Selection in Particle Swarm Optimization, The 7th Annual Conference on Evolutionary Programming, San Diego, USA, 1998.
 Ns-2.Network Simulator. http://www.isi.edu/nsnam/ns.