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and assume that the off distribution tail is not too long. If the service rate r satis es the stability condition and r < 1, then we have for any initial conditions lim Fon L 1 Et L m: 1 r J 1 mon =m J



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In order to see what changes to make on your system to make it more resilient, you need to look at the recent history of your system Why does your system go down What are the most common causes Don t just rely on anecdotal information ( Well, it sure does seem to go down a lot on Thursdays ) Keep real records Check them often Look for patterns (Maybe the system really does go down a lot on Thursdays Now you need to figure out why!) Maintain availability statistics over time Account for every incident that causes downtime Understand the root cause of the failure and the steps needed to fix it If you ve invested in failure isolation, you should have an easier time finding root causes Look closely at the MTTR, and see if something can be done to improve it.





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The condition that the off distribution be not too long is needed for technical reasons and means there exist a distribution H* and p with 1 < p < a such that I

Did it take two days for key parts to arrive Was there a single person whose expertise was critical to the repair process and was he unavailable Use the evaluations to identify your most common problems, and attack them first Don t waste your time protecting the CPU if the most common cause of downtime is an application bug The old 80/20 rule of thumb works here Roughly 80 percent of your downtime is likely to be caused by 20 percent of the problems Fix that 20 percent and you should see a tremendous improvement in availability Then you should be able to reapply the 80/20 rule on the next 80 percent of downtime The other quote that we considered for this principle is the one from the mutual fund commercials: Past performance is no guarantee of future success.

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for every x ! 0 and y ! 0 such that Foff y > 0. The class of distributions satisfying this condition includes Gamma distributions, distributions with compact support, and others. For related nite buffer results, see Jelenkovic [29] and Zwart [50]. 7.4 AN INFINITE SOURCE MODEL

However, that is not quite the message that we wanted to get across On the other hand, that quote does help prove the adage that for every saying, there is an equal and opposite saying Consider the combination of look before you leap and he who hesitates is lost or out of sight, out of mind and absence makes the heart grow fonder..

enough will change over time. Tracking and trending on complaints gives your company real insight into prioritizing what needs to be addressed. Assume for a minute that you work for a car company. In an oversimpli ed example, Figure 6.3 shows the trends in conversation over time. Clearly, environmental issues are the biggest trending topic when it comes to automobiles or a particular brand or a particular 0078 model.

The dif culty in analyzing the J -channel on=off model of the previous section suggests letting J 3 I in order to achieve additional tractability. So suppose now there are an in nite number of nodes in a network. Sources turn on and initiate sessions at Poisson time points fGk ; k ! 1g. The rate of the Poisson process is l. The lengths of sessions are i.i.d. random variables fXn g, which have a common distribution Fon and assume E X1 mon . During a session, an active source transmits at rate 1 and whenever there is work, the single server works at rate r. An important quantity is N t number of sessions in progress at t I P 1 Gk <t Gk Xk

A gas will expand to fill all available space. The definition of a gas Computer utilization will expand to fill all available space. The definition of a gas, applied to IT (Evan Marcus)

the number of busy servers in the M =G=I queue: Traditional queueing theory recognizes N t as the number of busy trunklines in an in nite line telephone model. It is well known and easy to derive the following facts. 1. N t is Poisson distributed. At equilibrium the mean is lmon .

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