Application of soft computing techniques to adaptive user buffer overflow control on the Internet

Two novel expert dynamic buffer tuners/controllers, namely, the neural network controller (NNC) and the fuzzy logic controller (FLC) are proposed in this paper. They use soft computing techniques to eliminate buffer overflow at the user/server level. As a result they help shorten the end-to-end service roundtrip time (RTT) of the logical Internet transmission control protocol (TCP) channels. The tuners achieve their goal by maintaining the given safety margin Delta around the reference point of the {0,Delta}2 objective function. Overflow prevention at the Internet system level, which includes the logical channels and their underlying activities, cannot shorten the service RTT alone. In reality, unpredictable incoming request rates and/or traffic patterns could still cause user-level overflow. The client/server interaction over a logical channel is usually an asymmetric rendezvous, with one server serving many clients. A sudden influx of simultaneous requests from these clients easily inundates the server's buffer, causing overflow. If this occurs only after the system has employed expensive throttling and overflow management resources, the delayed overflow rectification could lead to serious consequences. Therefore, it makes sense to deploy an independent user-level overflow control mechanism to complement the preventative effort by the system. Together they form a unified solution to effectively stifle channel buffer overflow

[1]  Wu-chun Feng,et al.  Dynamic Adjustment of TCP Window Sizes , 2000 .

[2]  Les E. Atlas,et al.  Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.

[3]  Michalis Faloutsos,et al.  A user-friendly self-similarity analysis tool , 2003, CCRV.

[4]  James Aweya,et al.  Multi-level active queue management with dynamic thresholds , 2002, Comput. Commun..

[5]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[6]  Robert A. Lordo,et al.  Learning from Data: Concepts, Theory, and Methods , 2001, Technometrics.

[7]  Allan K. Y. Wong,et al.  A convergence algorithm for enhancing the performance of distributed applications running on sizeable networks , 2001, Comput. Syst. Sci. Eng..

[8]  Donald F. Towsley,et al.  Modeling TCP throughput: a simple model and its empirical validation , 1998, SIGCOMM '98.

[9]  Lakhmi C. Jain,et al.  Intelligent Adaptive Control: Industrial Applications , 1998 .

[10]  Isi Mitrani Probabilistic modelling, Second Edition , 1998 .

[11]  Tharam S. Dillon,et al.  A fault tolerant model to attain reliability and high performance for distributed computing on the Internet , 2000, Comput. Commun..

[12]  QUTdN QeO,et al.  Random early detection gateways for congestion avoidance , 1993, TNET.

[13]  Tharam S. Dillon,et al.  M/sup 3/RT: an Internet end-to-end performance measurement approach for real-time applications with mobile agents , 2002, Proceedings International Symposium on Parallel Architectures, Algorithms and Networks. I-SPAN'02.

[14]  Tharam S. Dillon,et al.  M2RT: A tool developed for predicting the mean message response time of communication channels in sizeable networks exemplified by the Internet , 2001, Comput. Networks.

[15]  Li Chunlin,et al.  Design and implementation of a distributed computing environment model for object-oriented networks programming , 2002, Comput. Commun..

[16]  Tharam S. Dillon,et al.  An adaptive buffer management algorithm for enhancing dependability and performance in mobile-object-based real-time computing , 2001, Fourth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing. ISORC 2001.

[17]  Scott M. Lewandowski,et al.  Frameworks for component-based client/server computing , 1998, CSUR.

[18]  Wwk Lin,et al.  A novel fuzzy logic controller (FLC) for improving internet-based real-time application timeliness by eliminating user-level buffer overflow , 2003 .

[19]  Zhengding Lu,et al.  Design and implementation of a distributed computing environment model for object-oriented networks programming , 2002, Comput. Commun..

[20]  Kevin Jeffay,et al.  Tuning RED for Web traffic , 2000, TNET.

[21]  Tharam S. Dillon,et al.  A fault-tolerant data communication setup to improve reliability and performance for Internet based distributed applications , 1999, Proceedings 1999 Pacific Rim International Symposium on Dependable Computing.

[22]  Sally Floyd,et al.  Wide area traffic: the failure of Poisson modeling , 1995, TNET.

[23]  Tharam S. Dillon,et al.  Heuristic rule based neuro-fuzzy approach for adaptive buffer management for Internet-based computing , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[24]  Ibrahim Matta,et al.  On the origin of power laws in Internet topologies , 2000, CCRV.

[25]  P. Kachroo,et al.  Intelligent feedback control-based adaptive resource management for asynchronous, decentralized real-time systems , 2001 .

[26]  Ulf Bodin,et al.  Load-tolerant differentiation with active queue management , 2000, CCRV.

[27]  J. W. Modestino,et al.  Adaptive Control , 1998 .

[28]  Niki Pissinou,et al.  Performance analysis of a PCS network with state dependent calls arrival processes and impatient calls , 2002, Comput. Commun..

[29]  Wail Gueaieb,et al.  The hierarchical expert tuning of PID controllers using tools of soft computing , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[30]  Cui-Qing Yang,et al.  A taxonomy for congestion control algorithms in packet switching networks , 1995, IEEE Netw..

[31]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1997, TNET.

[32]  R. C. Berkan,et al.  Fuzzy systems design principles - building fuzzy IF-THEN rule bases , 1997 .

[33]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[34]  Martin May,et al.  Analytic evaluation of RED performance , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[35]  B. Barden Recommendations on queue management and congestion avoidance in the Internet , 1998 .

[36]  T. V. Lakshman,et al.  The drop from front strategy in TCP and in TCP over ATM , 1996, Proceedings of IEEE INFOCOM '96. Conference on Computer Communications.

[37]  Fengyuan Ren,et al.  Design of a fuzzy controller for active queue management , 2002, Computer Communications.

[38]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1996, SIGMETRICS '96.

[39]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[40]  T. V. Lakshman,et al.  The performance of TCP/IP for networks with high bandwidth-delay products and random loss , 1997, TNET.

[41]  Shan Xiuming,et al.  Design of a fuzzy controller for active queue management , 2002 .

[42]  Allan K. Y. Wong,et al.  Genetic Algorithm and PID Control Together for Dynamic Anticipative Marginal Buffer Management: An Effective Approach to Enhance Dependability and Performance for Distributed Mobile Object-Based Real-Time Computing over the Internet , 2002, J. Parallel Distributed Comput..

[43]  David Ott,et al.  Tuning RED for Web traffic , 2000, SIGCOMM.

[44]  Sun-Yuan Kung,et al.  Principal Component Neural Networks: Theory and Applications , 1996 .

[45]  A. Holt Long-range dependence and self-similarity in World Wide Web proxy cache references , 2000 .

[46]  Robert Tappan Morris,et al.  Dynamics of random early detection , 1997, SIGCOMM '97.

[47]  Liang Li,et al.  Nonlinear adaptive prediction of nonstationary signals , 1995, IEEE Trans. Signal Process..

[48]  Allan K. Y. Wong,et al.  An adaptive and aggressively bounded convergence algorithm for enhancing and measuring the performance of applications running on networks with heavy-tailed distributions , 2001, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001.