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routers could allocate resources for a ow to improve routing and forwarding performance For example, a router could cache next-hop routes for common destination addresses to reduce the number of routing computations and to forward related packets along a single route The router could even establish dedicated connections through the switching fabric for long-lived ows to avoid software processing of subsequent packets Each of these network mechanisms performs operations and allocates resources on the time scale of connection arrivals Therefore, the burstiness of the connection arrival process affects two separate provisioning tasks: the central processing unit (CPU) resources necessary to perform the algorithm and the network resources required to acheive a desired level of blocking The burstier the arrival process, the more CPU resources are necessary to execute the algorithm and the more network resources are needed to maintain a given level of blocking.

The nasal epithelia are the rst point of contact for respiratory toxicants. Because they contain xenobiotic metabolizing enzymes, they are susceptible to toxic effects caused by reactive intermediates.

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In this chapter we demonstrate that the TCP connection arrival process is bursty We show that the arrival process is asymptotic self-similar Self-similarity of the TCP connection arrival process implies that the use of standard models in evaluating the performance of resource allocation methods can yield misleading results Therefore, we characterize TCP connection interarrival times1 using heavy-tailed distributions We present statistical evidence that such distributions, especially the Weibull distributions, yield a better model for the interarrival times of TCP connections than exponential models Intuitively, a heavy-tailed interarrival time means that if no connection arrived for some time it becomes more and more unlikely that one will arrive soon This holds even if the underlying arrival process is nonstationary Finally, based on a simple resource allocation problem, we show that there are advantages to using Weibull distributions to model TCP connection interarrival times over a nonstationary Poisson process.

resulted in >99%, 79%, 84%, and 99% reduction in plaque formation for RCV, MAV, TMEV, and SV40, respectively ( p < 0.0001 in all cases). Of the viruses shown, TMEV demonstrated the highest absolute plaque formation subsequent to host cell modi cation with 5 mM BTCmPEG. This may relate to the fact that it is also the smallest ($20 nM) virus examined. To determine if the density and composition of the mPEG brush border could further decrease TMEV entry, BTCmPEG dose response curves were conducted using 5 and 20 kDa polymers as well as an equimolar combination (5 20 kDa) of the two species. As shown in Table 16.4, polymer size and derivatization concentration both dramatically affect the ef cacy of protection against TMEV plaque formation. While all polymer species demonstrated a strong BTCmPEG dose dependency, the equimolar combination of the 5 and 20 kDa BTCmPEGs yields clearly superior ( p < 0.0001) protection. Indeed, at host cell derivatization concentrations as low as 0.2 mM BTCmPEG, an 82% reduction in plaque formation was noted. At the 5 mM grafting concentration shown in Table 16.4, the 5 20 kDa combination yielded a >99% reduction in plaque formation versus a decrease of 84% noted with the 5 mM concentration of the 5 kDa polymer alone. mPEG-mediated antiviral prophylaxis was also demonstrated in a cytomegalovirus (CMV; herpesvirus) model as determined by IE72 expression at 24, 48, and

In addition to being in direct contact with airborne toxicants, the entire body blood volume passes through the lung one to ve times a minute, exposing the lung to toxicants and drugs within the systemic circulation. Thus the possibility of damage from both inhaled and circulating agents is enormous. As with the liver and kidney, the lungs possess signi cant levels of many xenobiotic metabolizing enzymes and thus can play a large role in the activation and detoxication of exogenous chemicals.

Our results are based on extensive analyses of multiple traces collected at Carnegie Mellon University in 1995, at AT&T Bell Laboratories in 1995 and 1996, and at AT&T Labs Research in 1996 In addition we augment our results with the analysis of traces collected at Lawrence Berkeley Laboratories (LBL) in 1993 and 1995, at Digital Equipment Corporation (DEC) in 1995, and within AT&T WorldNet in 1997 and 1998 The rest of the chapter is organized as follows In Section 152 we present, on an intuitive level, why TCP connection arrivals are bursty and what impact this may have on resource allocation problems A more detailed description of the TCP=IP traces on which we base our traf c characterizations is given in Section 153 The results in Section 154 indicate the self-similar nature of the arrival process Section 15.

5 outlines the methods used to analyze the interarrival time distribution and then shows that the Weibull distribution yields a good t for connection interarrival times Section 156 contrasts the use of the Weibull model to that of a nonstationary Poisson process using an example application Finally we conclude with a brief summary..

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