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) Comdisco had obtained and set up tables, chairs, phones, and workspaces This first upgrade greatly reduced tension throughout the organization, as it allowed members a space to work that was not on the trading floor, or in the way of active traders Since trading was not scheduled to resume until Monday, few members had any plans to visit the Queens site before then However, Pat, Steve, and the Board realized that Monday morning would be total chaos, what with most of the 1,000 members arriving at once, all looking for their familiar booths, trading pits, phones, and other facilities Many of them would not be familiar with the staggered schedule and would expect to trade all day as normal As this.



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Apr 17, 2018 · You may be able to use third-party OCR software to retain more of the text format styles that are present in the source document image. Note As ...

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constant rate, its peak rate, taken as unity. Given E A 1=a and E B 1=b, the activity probability of a source is then n a= a b and we require that C > N n so that the queue has a stationary regime. Provided that the probability density of duration A B satis es a simple regularity condition, we rst show in this chapter that, once properly centered and normalized, the aggregate input rate and traf c processes produced by the superposition of N i.i.d. on=off sources converge as N 4 I toward continuous Gaussian processes. This is the content of our ``functional central limit'' theorems. In contrast with the classical heavy load approximation, note that the limit input rate process obtained this way is generally non-Markovian for arbitrary distributions of on and off durations A and B. N Denote further by V0 the corresponding queue content in stationary conditions. N Our essential aim is to obtain estimates for the limiting distribution of V0 as N 4 I, the capacity C scaling with N as p C Nn g N 5:1





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firms on Wall Street, the number often quoted for downtime during the trading day is $2 million per trader per 20-minute outage. One assumption in that $2 million number is that all trades made on the trading floor are profitable. Consider that the outage could have prevented a trader from making a moneylosing deal, thus saving the firm money. Wall Street firms are oddly loath to discuss this possibility. Of course, the traders salaries and carrying costs are lost, and while many traders do make a lot of money for both themselves and their firms, very few of them can consistently bring in $2 million dollars every 20 minutes. A trading firm s loss is not as easily quantifiable as the loss to the development organization, because the trading firm s losses are also composed of opportunity losses.3 Other industries direct costs of downtime, as reported by various services, are listed in Table 3.1. Over time, of course, these values will change; the actual values are less important than the orders of magnitude of the values, and how much the costs can vary by industry. You don t need to precisely quantify a loss to know that a significant loss has occurred.

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for some positive constant g. Equation (5.1) is clearly identi ed as a heavy load condition since the queue load N n=C tends to 1 for increasing N (with speed p 1= N . Now, by considering so-called heavy-tailed distributions for on and=or off periods, the input process becomes long-range dependent (LRD). Based on the above convergence results for input processes, our analysis then subsequently shows that the tail of the limiting distribution of the scaled queue content N p V0 = N is not exponential but Weibullian. Speci cally, de ning

Table 3.1 The Direct Costs of Downtime INDUSTRY Brokerage services Energy Credit card Telecomm Financial Manufacturing Financial institutions Retail Pharmaceutical Chemicals Health care Media Airline reservations AVERAGE DOWNTIME COST PER HOUR $6.48 million $2.8 million $2.58 million $2 million $1.5 million $1.6 million $1.4 million $1.1 million $1.0 million $704,000 $636,000 $340,000 $90,000

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Opportunity losses are the loss of a chance to do something, regardless of its outcome. The something could be trading shares of stock, verifying a credit card transaction, or something more mundane like selling a product on the Internet. Without close monitoring, it is almost impossible to know what the opportunity losses are when a system goes down.

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