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The onset of the peak is a function of available resources and it can be further delayed by decreasing the overall aggressiveness of each connection. Note that the multiple connection throughput behavior is achieved for the network con guration shown in Fig. 18.8, which, due to its uniform link latencies, can be prone to synchronization effects [14]. Figure 18.20 plots the individual throughput achieved by each SAC connection when a total of 10 are present. We observe that fairness, for the network con guration shown in Fig. 18.8, is well-preserved. As the con guration becomes less

Figure 8.1 Visa has been active in the development of the Java Card. Visa's Open Platform classes interact with Java Card to provide a secure framework for which to develop smart card applications. (This figure used by permission from Visa.)

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defining what software is permitted for use. Needless to say, that does not go nearly far enough to assist in the required level of detail needed. Setting standards is hard work and requires involvement by persons knowledgeable in the various technical areas for which the standards are intended. Their comprehension of the technology must be deep enough for them to know, appreciate, and be able to articulate the consequences of choosing one alternative over others as choices for standardization are made. Because they are more detailed and closer to the products in use, standards call for more frequent reviews than policies do. Certainly if one software product is replaced by another, the standard that led to the original product choice needs to be reviewed.

uniform, access discrepancies as with TCP and other feedback congestion control algorithms are bound to arise, which is a generic problem not speci c to SAC. 18.6 CONCLUSION

In this chapter we have shown that a form of predictive congestion control called selective aggressiveness control (SAC) is able to exploit correlation structure present in long-range dependent traf c for performance improvement purposes. We have shown that predictability can be implemented on-line with suf cient accuracy such that when coupled with SAC mechanism, the cost associated with conservative bandwidth consumption during the linear increase phase of a generic linear increase=exponential decrease feedback congestion control algorithm can be offset to bring forth improved throughput. Furthermore, we show that the relative performance gain is higher the more long-range dependent the underlying network traf c. We have also shown that SAC preserves fairness to the degree expected of a feedback congestion control and its ef ciency property is desirable within a range. An important consequence of multiple time scale congestion control is its ability to mitigate the dely-bandwidth product problem of broadband wide area networks by endowing proactivity. We refer the reader to [46, 48] for this result. Current work is directed along four avenues: (1) in devising new mechanisms capable of exploiting predictability at longer time scales, (2) in further optimizing the various components of SAC, (3) in implementing the ideas of SAC in TCP, and (4) adopting the multiple time scale traf c control framework to the QoS-sensitive transport of real-time traf c using packet-level adaptive forward error correction. The TCP results are reported in [48], and the extension to quality of service is performed via multiple time scale redundancy control and described in [47]. REFERENCES

A smart card chip is actually a complete little computer with nonvolatile memory, storage, a card operating system (COS), and accompanying communication protocols. The most advanced smart cards on the market have the processing power once found in an IBM-XT (with less memory, of course). There are many different uses for smart cards. Smart cards can serve as:

1. A. Adas and A. Mukherjee. On resource management and QoS guarantees for long range dependent traf c. In Proc. IEEE INFOCOM'95, pp. 779 787, 1995. 2. R. Addie, M. Zukerman, and T. Neame. Fractal traf c: measurements, modelling and performance evaluation. In Proc. IEEE INFOCOM'95, pp. 977 984, 1995. 3. T. Anderson. The Statistical Analysis of Time Series. Wiley, New York, 1994. 4. J. Beran. Statistics for Long-Memory Processes. Monographs on Statistics and Applied Probability. Chapman and Hall, New York, 1994. 5. J.-C. Bolot and A. U. Shankar. Analysis of a uid approximation to ow control dynamics. In Proc. IEEE INFOCOM'92, pp. 2398 2407, 1992. 6. L. Brakmo and L. Peterson. TCP Vegas: end to end congestion avoidance on a global internet. IEEE J. Select. Areas Commun., 13(8):1465 1480, 1995. 7. I. Chlamtac and W. R. Franta. Rationale, directions, and issues surrounding high speed networks. Proc. IEEE, 78(1):94 120, 1990. 8. M. Crovella and A. Bestavros. Self-similarity in World Wide Web traf c: evidence and possible causes. In Proceedings of the 1996 ACM SIGMETRICS International

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