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As a result, designers of computing and telecommunication systems are increasingly interested in employing heavy-tailed distributions to generate workloads for use in simulation. See, for example, s 14 and 18 in this volume. However, simulations employing such workloads may show unusual characteristics; in particular, they may be much less stable than simulations with less variable inputs. In this chapter we discuss the kind of instability that may be expected in simulations with heavy-tailed inputs and show that they may exhibit two features: rst, they will be very slow to converge to steady state; and second, they will show highly variable performance at steady state. To explain and quantify these observations we rely on the theory of stable distributions [4, 15]. These problems are not unique to simulation of telecommunications systems, arising also in risk and insurance modeling [2]. Solutions to certain aspects of these problems have been proposed, drawing on rare event simulation and variance reduction techniques [8, 14]. In general, however, many of the problems associated with the simulations using heavy-tailed workloads seem quite dif cult to solve. This chapter does not primarily suggest solutions but rather draws attention to these problems, both to yield insight for researchers using simulation and to suggest areas in which more research is needed. 3.2 3.2.1 HEAVY-TAILED DISTRIBUTIONS Background



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This means that ready-to-eat food, bottled water, eating utensils, soap, can openers, soft drinks, coffee, sugar, radios, flashlights (with spare batteries), first-aid kits, and other basic (and slightly more than basic) comforts should be provided, and freshened every once and a while, so that workers are not eating three-year-old bags of pretzels while recovering the business It might not be a bad idea to leave a small petty cash supply on hand as well, just in case Other things Since you cannot possibly know the nature of the disaster before it strikes, it is best to stock the DR site with some basic construction and repair equipment like hard hats, work gloves, hand tools (hammers, nails, screw drivers, crowbar, saw, etc), and even boots Plywood.





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Let X be a random variable with cdf F x P X x and complementary cdf (ccdf) F x 1 F x P X > x . We say here that a distribution F x is heavy-tailed if F x $ cx a ; 0 < a < 2; 3:1

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for some positive constant c, where a x $ b x means limx3I a x =b x 1. (We note that more general de nitions of heavy tails are common; see, for example, Goldie and Kluppelberg [6].) If F x is heavy tailed then X shows very high variability. In particular, X has in nite variance, and, if a 1; X has in nite mean. Section 3.2.2 will explore the implications of in nite moments in practice; here we note simply that if fXi ; i 1; 2; . . .g is a sequence of observations of X , then the sample variance of fXi g as a function of i will tend to grow without limit, as will the sample mean if a 1. The simplest heavy-tailed distribution is the Pareto distribution, which is power law over its entire range. The Pareto distribution has pmf p x ak a x a 1 ; and cdf F x P X x 1 k=x a ; 3:2 0<k x;

and tarpaulins can make it easier to affect temporary repairs, water pumps can help clean up nasty situations, and of course, large heavyduty trash bags can help with cleanup. Brightly colored police tape, oak tag, thick markers, whiteboards, and dry erase markers can help with inside communications, while orange traffic cones and road flares can help with outside communications.

As an illustration, consider the following function f x satisfying the functional equation: f x 1 f x=2 e x =2: 2 11:5

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Once the plan has been developed, it has to be tested. As we said earlier in the book, an untested plan is just a piece of paper. There is some semantic question as to whether or not you call a run-through of your DR plan a test. A test, after all, is graded, and the taker of the test either passes or fails. In a DR test the goal is not as simple as pass/fail, but rather to make the plan better for the next time. It s very important to remember, as a test proceeds, what is being tested. You are not testing the participants in the test, but rather the plan itself. If a participant makes a mistake during the test, the fault is most likely with the test, and not with the participant. Some instruction wasn t clear, a diagram was wrong, or there was some other error that caused the participant to interpret the plan improperly. The fix is with the plan, not with the person. As we discuss going through the plan, we use the term exercise rather than test, because we see the process much more as an ongoing attempt to make the plan better, rather than as a chance to fail.

This equation arises in the analysis of divide and conquer algorithms [10]. The solution is obtained by iteration: P i x=2i f x 2 e : 11:6

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