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Each event under consideration has two states: the before state and the after state And each of those states is made up of likelihood (L), duration (D), and impact (I) Mathematically, the effect (E) of a particular event is as follows: E = L D I To be mathematically proper, we must break these effects into before and after cases and do so for each event being considered So, the before case formula would be the following: EBx = LBx DBx IBx and the after case formula would be: EAx = LAx DAx IAx The other critical factor for both the before and after cases is cost of downtime (CD) CD (expressed in dollars per minute) tells us how much downtime costs and, by extension, how much you can afford to spend on your systems to protect them against such downtime.



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Extract Text From Images With These Best OCR Software. ABBYY FineReader. When it comes to Optical Character Recognition, there's hardly anything that comes even close to ABBYY FineReader. FreeOCR. Microsoft OneNote. GOCR. 15 New MIUI 11 Features You Should Know. 15 New MIUI 11 Features You Should Know.

Denote the steady-state workload by V ; in the GI =G=1 FCFS queue (cf. Cohen [15, p. 296]), E e sV 1 r rb*fsgE e sW ; Re s ! 0: 6:10





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Many businesses do not know how much it costs when their systems are down Some of the industry consulting companies (Meta, Gartner, IDC, and so on) publish downtime costs, but those are generally businesswide values rather than for a single system An interesting mathematical aside is that the units associated with E are minutes Likelihood and impact are scalars (free-standing numbers without associated units) Duration is, of course, expressed in minutes So the effect of a particular outage is expressed in time When you include cost of downtime in these calculations, multiply minutes by dollars per minute, and you wind up with a value expressed in dollars To build the risk formula for the before case, you must add up all the effects and multiply that result by the cost of downtime.

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Hence, using Lemma 6.2.1, P B > P n ( P V > P 1 n . A REMARK 6.2.6. The goal of this remark is to indicate how results like Theorem 6.2.2 can readily be translated into results for uid queues. Consider a uid queue with an in nite buffer and an output rate equal to one. This queue is fed by a source that alternates between silence periods sn , n ! 1, during which it generates no input, and activity periods an , n ! 1, during which it generates uid according to the rate process rn t t!0 , n ! 1 (this could be the aggregated input process due to several on=off sources). A crucial assumption is that Vn ! 1; Vt ! 0: 1 rn t R where R is some given constant : 6:11

So the formula for risk in the before case looks like this: RB = CD (EB1 + EB2 + EB3 + .. + EBx) The formula for the after case is slightly more complicated because you must also consider the cost of implementation (CM) The cost of implementation must include the cost of all hardware and software purchased for the implementation, as well as any professional services, support, training, additional personnel, downtime required for the implementation, and any other costs to the business The after formula looks like the following: RA = CM + (CD (EA1 + EA2 + EA3 + .. + EAx)) Now that we have the formulas, let s plug in some numbers..

rn t 1 dt:

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