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2 is the level of availability that your system would attain if it had all of these outages and no others over the five-year life span of the systems It is the best possible level of availability, given these outages Additional outages would cause that level to drop even further In Table 33, we examine the same values after the clustering software has been installed We make the conservative assumption that we will recover from any outage in 5 minutes with a reliable failover In reality, failover times will vary depending on your application Some failovers will be longer than 5 minutes, whereas others will be shorter The duration of the failover will depend mostly on the time it takes to restart the application on a different host File service, shown in this example, is particularly adept at failing over quickly.



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with the property that DW r 5 0 for r 4 1. Proof. We refer to Boxma and Cohen [10] for a detailed proof of the theorem, albeit under slightly weaker conditions on the service time distribution. Here we sketch a different proof. Using the Pollaczek Khintchine formula (6.7) and Eq. (6.19) we can write, for Re s ! 0, E e sW 1 r 1 rb*fsg 1 1 r 1 b*fsg 1 r : 6:20

We assume that likelihood and impact values have not changed and that only the duration of the outage is affected by the presence of clustering software..





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Now replace s by rDW r , Re r ! 0. Since DW r 5 0 for r 4 1, it follows from Lemma 6.2.1 that 1 b*frDW r g $ G 2 n n 1 r DW r n 1 L rDW r n 1 for r 4 1:

For an in-depth and thoughtful look at how to measure your YouTube exposure, take a look at a blog post from Peter Kowalski, director of research strategy at KDPaine & Partners called How to Measure YouTube: Set up a simple measurement program, decide if particular videos are really problems, and prepare for the worst case scenario (www.themeasurementstandard.com/issues/51-08/kowalskiyoutube5-1-08.asp). There s also a good review of the tool YouTube Insight by Jake Matthews at www.10e20.com/blog/2008/07/15/video-optimizationand- analytics-a-closer-look-at-youtube-insight/.

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Table 3.2 Effects of Outages before Clustering Software Is Installed BEFORE DURATION (D) 60 minutes* 100% 75% EB2 = 900 EB3 = 900 EB4 = 2,880 EB5 = 480 EB6 = 1,200 EB7 = 2,400 EB8= 0 9,360 minutes (99.644 percent availability over 5 years) 50% 100% 100% 100% 50% 0 EB1 = 600 (during the day) 120 minutes 30 minutes 24 hours (1,440 minutes)# 2 2 20 20 0 4 hours (240 minutes)** 60 minutes 4 hours (240 minutes) 0 60| | 10 10 BEFORE LIKELIHOOD (L) BEFORE IMPACT (I) BEFORE EFFECT (D L I)

6:21

3

REMARK 6.3.2. In Boxma and Cohen [10] it is shown that Theorem 6.3.1 also holds for n 2. Furthermore, Rn 1 t is discussed in detail in Boxma and Cohen

[10]. Its relation to the Mittag Lef er function and (for n 3) to the complementary 2 error function is pointed out. One can write, for t ! 0, 1 Rn 1 t

Crash and reboot (off-hours)

1 n

Total effect of outages:

REMARK 6.3.3. Theorem 6.3.1 opens possibilities for approximating the waiting time distribution in the M =G=1 queue with heavy-tailed service time distribution; such possibilities are explored in Boxma and Cohen [9]. Replacing P W > x by 1 Rn 1 DW r x=b appears to yield remarkably accurate results, even if the traf c load r is much smaller than one (in particular when x is large). 6.3.3 The M=G=1 PS Queue

Assumes manual reboot, allowing time for the administrator to reach the system, reboot it, and deal with other hazards along the way. Twice a year for 5 years. The 60 minutes beyond the during the day case is to allow time for the administrator to get from home to the office, where the system is. Since the incident occurs off-hours, we assume that the impact is less than it would be during the workday. || Once a month for 5 years: a prudent rebooting schedule. # We assume that this will require opening a service call with your hardware support provider, waiting for an engineer to arrive, and an additional delay while waiting for parts to be shipped. Your mileage may vary. ** We assume that the replacement of a NIC card can be done by a good system administrator, without having to wait for an engineer or parts to be shipped. If the case is not so, then the duration will be longer than 4 hours. We are assuming that this occurs once per quarter over 5 years. This will vary on the basis of many factors, including the nature, complexity, and reliability of your application. The duration of the scheduled maintenance depends on many factors. 4 hours is a reasonable estimate. Because no failover mechanism is in place, no failover testing is required in the before case.

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Top 7 Free OCR Tools for Image to Text Conversion in 2018
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