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dispensing with its impact on queueing, and directly analyzing the transient or dynamic variability structure based on the predictability inherent to long-range dependent processes. A similar ``on-line'' framework is adopted in 18 where long-term predictability structure is exploited for feedback congestion control. Tuan and Park ( 18) show that in spite of the ``bad news'' associated with scale-invariant burstiness, there is ``good news'' in the sense of there being the potential of exploiting long-term correlation structure present in long-range dependent traf c for traf c control purposes. They advance the multiple time scale congestion control framework and show that nonnegligible correlations at large time scales can be effectively detected on-line and engaged to improve the performance of feedback congestion controls in rate-based settings. The central idea underlying the technology is selective aggressiveness control, which allows explicit prediction of large time scale network state to be used to modulate the aggressiveness of bandwidth consumption behavior exhibited by feedback congestion control acting at small time scales (i.e., time scale of RTT). An important consequence is the mitigation of the delay bandwidth product problem of reactive controls in broadband wide-area networks. Finally, 19 by Adas and Mukherjee addresses the problem of how resource reservation in a time-division multiplexing set up per-VC framing can be used to facilitate end-to-end quality of service (QoS) under long-range dependent traf c conditions. The asynchronous framing approach described follows the resource provisioning paradigm espoused for long-range dependent traf c, namely, that of bufferless queueing, which then allows computation of QoS guarantees by appealing to the central limit theorem and equivalent bandwidth computations. REFERENCES

1. P. Abry and D. Veitch. Wavelet analysis of long-range dependent traf c. IEEE Trans. Information Theory, 44(1):2 15, 1998. 2. 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. 3. R. Addie, M. Zukerman, and T. Neame. Fractal traf c: measurements, modelling and performance evaluation. In Proc. IEEE INFOCOM '95, pp. 977 984, 1995. 4. G. Amdahl. Validity of the single-processor approach to achieving large scale computing capabilities. In AFIPS Conf. Proc., pp. 483 485, 1967. 5. V. Anantharam. Queueing analysis with traf c models based on deterministic dynamical systems. In Proc. 25th Allerton Conference on Communication, Control and Computing, pp. 233 241, 1996. 6. M. Arlitt and C. Williamson. Internet Web servers: workload characterization and performance implications. IEEE=ACM Trans. Networking, 5(5):631 645, 1997. 7. P. Barford and M. Crovella. Generating representative workloads for network and server performance evaluation. In Proc. ACM SIGMETRICS '98, pp. 151 160, 1998. 8. M. Barnsley. Fractals Everywhere. Academic Press, New York, 1988.

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Understanding Key Design Considerations Summary 11 Why Federated Identity Schemes Fail Understanding Federated Identity Schemes

Welcome to the online version of Securing Java: Getting Down to Business with Mobile Code. Putting a 300+ page book online is no easy task -- but hopefully navigating through it is. The online version of this book has been designed to work with you rather than against you to find information and read the text; generally, navigation should be pretty self-explanatory, but if you need a hand, here is a quick rundown of how things are laid out:

where GX n and C0 n stand for the power spectrum of X and the Fourier transform of c0 , respectively. This can be understood as the classical interference formula of the linear lter theory and receives a spectral estimation interpretation: EdX j; k 2 is a measure of GX at frequency nj 2 j n0 (n0 depends on c0 ) through the constant relative bandwidth wavelet lter [1 3, 34]. In the speci c context of LRD processes, F1 and F2 together yield the two following key properties: P1 LRD: Using GX n $ cf jnj a , n 3 0 (2.15), we obtain EdX j; k 2 $ 2ja cf C a; c0 ; j 3 I; 2:16

where C a; c0 jnj a jC0 n j2 dn, a P 0; 1 . The case of a 0 is well de ned, corresponding to trivial scaling at large scales, leaving only shortrange dependence at small scales. Again, this asymptotic recovering of the underlying power law is not a trivial result. It would not, for instance, be obtained with periodogram-based estimates [3] and is due to F1. P2 LRD: It can also be shown [3] that the covariance function of any two wavelet coef cients is controlled by N and therefore can decay much faster than that of the LRD process itself and is no longer LRD as soon as N ! a=2. Since a P 0; 1 , this is in fact always satis ed. EdX j; k dX jH ; k H % j2j k 2j k H ja 1 2N ; j2j k 2j k H j 3 I:

There is a chapter bar at the bottom of each page that allows you to jump to the front of any chapter in the book as well as other main sections such as the table of contents. When you get into reading the book itself, each front chapter page has a list of subsections that you can jump to. You can also use the arrows at the top of each page to go to the previous page or next page in the book. Keep in mind that one web page does not equal one physical page of the book, rather one page is usually equal to one full subsection of a chapter. You can search the text of the book from almost anywhere via the search box at the bottom of the page. Clicking on a footnote number will bring up a new window with the footnote. This works in Netscape and Opera, though Internet Explorer will open the footnote in the same window -- if you use IE, use the "Back" button to return to the chapter.

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