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It s the end of the world as we know it, and I feel fine. Michael Stipe (R.E.M.)

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There is so much to say about disaster recovery (DR) that we could surely write an entire book on the subject. Needless to say, we have not chosen to do so. Instead, we have elected to offer an overview about many of the topics that must be considered for a complex enterprise to survive a disaster. For our purposes, a disaster is an event that knocks an entire facility offline. Back in 3, The Value of Availability, we introduced the concept of the outage lifecycle timeline, and we used it to demonstrate how the right level of protection can allow an enterprise to reduce the effects of an outage to a tolerable level. It is important to note here that the timeline and all of its characteristics hold just as well for a disaster as they do for a local outage, such as a disk failure. We made the point then, and we make it once again: A disaster is no different from other kinds of outages, except that the disaster takes the outage s effects and spreads them over a wider area, and applies them to more components. Our entry into the realm of DR brings us to the top level, Level 10, of the Availability Index, as shown in Figure 20.1. This discussion continues into 21, A Resilient Enterprise, where we take a look at a most remarkable story: the real-life story of the New York Board of Trade, and their experiences on September 11, 2001, when their building, adjacent to the World Trade Center towers in New York, was destroyed, and they were able to resume trading less than 12 hours later.





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Queueing Analysis of Finite Buffer Systems Most queueing results with selfsimilar input are asymptotic in nature where either buffer capacity is assumed in nite and the tail probability of queue length distribution in steady state PrfQI > xg is estimated as x 3 I, or buffer capacity b is assumed nite but buffer over ow probability is computed as b becomes unbounded Little is known about the nitary case, and Grossglauser and Bolot [28] and Ryu and Elwalid [60] provide approximate, heuristic arguments regarding the impact of nite time scale implied by bounded x and b Large deviation techniques [64] are too coarse to be effectively applied to nite x and b, and not surprisingly, the unbounded case or bufferless queueing case (ie, b 0) is more easily amenable to tractable analysis.

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Amazon.com has allowed customers to comment on books and goods for years. Epinions.com started last century (May 1999) as the place where buyers could discuss the ins and outs of products they love and hate without being pummeled by the vendors who were trying to sell them things. Now, thanks to syndication services like Bazaarvoice, most ecommerce sites have a place for the voice of the customer.

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The bufferless case can provide indirect insight on performance with ``small'' buffer capacities and complements the conclusions advanced in the asymptotic case (cf 17 for a discussion of bufferless queueing with self-similar input) The divide between our understanding of unbounded and zero memory systems, on the one hand, and nitary systems of interest, on the other, limits the applicability of these techniques both quantitatively and qualitatively above and beyond polynomial decay of queue length distribution and its broad interpretation as ampli ed buffering cost to resource provisioning and control The dif culty underlying analysis of nite buffer systems with non-Markovian, in particular, self-similar input is a fundamental problem at the heart of probability theory and, perhaps, beyond the scope of applied probability Fundamental advancement in understanding, proof techniques, and tools is needed to overcome the challenges a longer term venture.

Disaster Recovery A V A I L A B I L I T Y Replication Failovers Services and Applications Client Management Local Environment Networking Disk and Volume Management Reliable Backups Good System Administration Practices INVESTMENT Figure 20.1 Level 10 of the Availability Index.

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