Software agents architecture for controlling long-range dependent network traffic

Recent measurements of local and wide-area network traffic have proven that the widely used Markovian process models cannot be applied for today's network traffic. If the traffic were Markovian process, the traffic's burst length would be smoothed by averaging over a long time scale contradicting with the observations of today's traffic characteristics. Measurements of real traffic also prove that traffic burstiness is present on a wide range of time scales. Traffic that is bursty on many or all time scales can be characterized statistically using the concept of long-range dependency. Long-range dependent traffic has noticeable bursts, long periods with extremely high values on all time creating traffic congestions. Several conventional methods have been implemented to avoid congestion, but they are not responsive enough to the varying transmission capacity and network delay in high-speed networks. A new research direction, programmable networking, offers a more promising solution for congestion control. Traditional packet networks perform only the processing necessary to forward packets toward their destination. As computing power becomes cheaper, more and more functionality can be deployed inside the network. Programmable networks support dynamic modification of the network software and hardware to manipulate the network's behavior. A special way of network programmability is when special programs called mobile or software agents are carried in the packets to the routers. Software agents are loaded, executed, migrated, and suspended in order to implement some network functions. Software agents provide the highest possible degree of flexibility in congestion control. They can carry congestion-specific knowledge into the network at locations where it is needed, rather than transferring information to the sending hosts as it happens in traditional flow control solutions. In our paper, we propose a new programmable network architecture using software agents that can reduce the harmful consequences of congestions due to aggregated bursty traffic, such as packet losses, extremely long response times, etc.

[1]  A. Udaya Shankar,et al.  Dynamical behavior of rate-based flow control mechanisms , 1990, CCRV.

[2]  W. Willinger,et al.  ESTIMATORS FOR LONG-RANGE DEPENDENCE: AN EMPIRICAL STUDY , 1995 .

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

[4]  Yuval Shavitt,et al.  An Active Network Approach to Efficient Network Management , 1999, IWAN.

[5]  Murad S. Taqqu,et al.  On the Self-Similar Nature of Ethernet Traffic , 1993, SIGCOMM.

[6]  Antonio Puliafito,et al.  Programmable agents for flexible QoS management in IP networks , 2000, IEEE Journal on Selected Areas in Communications.

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

[8]  Moshe Zukerman,et al.  Application of the M/Pareto process to modeling broadband traffic streams , 1999, IEEE International Conference on Networks. ICON '99 Proceedings (Cat. No.PR00243).

[9]  William A. Arbaugh,et al.  The SwitchWare active network architecture , 1998, IEEE Netw..

[10]  Thomas Magedanz,et al.  Intelligent agents: an emerging technology for next generation telecommunications? , 1996, Proceedings of IEEE INFOCOM '96. Conference on Computer Communications.

[11]  John V. Guttag,et al.  ANTS: a toolkit for building and dynamically deploying network protocols , 1998, 1998 IEEE Open Architectures and Network Programming.

[12]  Moshe Zukerman,et al.  Broadband traffic modeling: simple solutions to hard problems , 1998, IEEE Commun. Mag..

[13]  Rolf Stadler,et al.  Active distributed management for IP networks , 2000 .

[14]  Tibor Gyires.  Methodology for modeling the impact of traffic burstiness on high-speed networks , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[15]  R. D. Rockwell,et al.  Smart Packets for active networks , 1999, 1999 IEEE Second Conference on Open Architectures and Network Programming. Proceedings. OPENARCH '99 (Cat. No.99EX252).

[16]  Nicolas D. Georganas,et al.  Analysis of an ATM buffer with self-similar ("fractal") input traffic , 1995, Proceedings of INFOCOM'95.

[17]  T. Faber.  ACC: using active networking to enhance feedback congestion control mechanisms , 1998 .

[18]  John B. Nagle,et al.  On Packet Switches with Infinite Storage , 1987, IEEE Trans. Commun..

[19]  Moshe Zukerman,et al.  A Practical Approach for Multimedia Traffic Modeling , 1999, Broadband Communications.

[20]  Michael R. Genesereth,et al.  Software agents , 1994, CACM.