Analytical System Administration in Java

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Analytical System Administration
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best one can do is to rely on heuristic information from an experienced observer (in this case the system administrator). Only an experienced observer would be able to judge the value of a policy on the basis of incomplete data. Such information is difficult to trust, however, unless it comes from several independent sources. A better approach might be to test the policy with simulated data spanning the range from best to worst case. The advantage with simulated data is that the results are reproducible from those data, and thus one has something concrete to show for the effort. 11.6.9 Reliability
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Reliability cannot be measured until we define what we mean by it. One common definition uses the average (mean) time before failure as a measure of system reliability. This is quite simply the average amount of time we expect to elapse between serious failures of the system. Another way of expressing this is to use the average uptime, or the amount of time for which the system is responsive (waiting no more than a fixed length of time for a response). Another complementary figure is, then, the average downtime, which is the average amount of time the system is unavailable for work (a kind of informational entropy). We can define the reliability as the probability that the system is available:
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Mean uptime Total elapsed time
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This is clearly a number between 0 and 1. The meaning of parallelism, or redundancy, can be evaluated with an equal disregard for reality, if we treat the system as a simple linear facsimile of the Ohm's law problem: Rate of service (delivery) = change in information/rate of failure This is directly analogous to Ohm's law for the flow of current through a resistance:
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The analogy is captured in this table:
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Potential difference V Current / Resistance R Change in information Rate of service (flow of information) Rate of failure
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This relation is simplistic. For one thing it does not take into account variable latencies (although these could be defined as failure to respond). It should be clear that this simplistic equation is full of unwarranted assumptions, and yet its simplicity justifies its use for simple hand-waving. If we consider Figure 6.1, it is clear that a flow of service can continue, when servers work in parallel, even if one or more of them fails. In Figure 6.2 it is clear that systems which are dependent on other series are coupled in series, and a failure prevents the flow of service. Because of the linear relationship, we can use the usual Ohm's law expressions for combining failure rates:
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Rseries = R1 + R2 + R3 +
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These simple expressions can be used to hand-wave about the reliability of combinations of hosts. For instance, let us define the rate of failure to be a probability of failure with a value between 0 and 1. Suppose we find that the rate of failure of a particular kind of server is 0.1. If we couple two in parallel (a double redundancy) then we obtain an effective failure rate of
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the failure rate is halved. This estimate is clearly naive. It assumes, for instance, that both servers work all the time in parallel. This is seldom the case. If we run parallel servers, normally a default server will be tried first, and if there is no response, only then will the second backup server be contacted. Thus, in a fail-over model, this is not really applicable. Still, we use this picture for what it is worth, as a crude hand-waving tool. The Mean Time Before Failure (MTBF) is used by electrical engineers, who find that its values for the failures of many similar components (say light bulbs) has an exponential distribution. In other words, over large numbers of similar component failures, it is found that the probability of failure has the form
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or that the probability of a component lasting time t is the exponential, where r is the mean time before failure and t is the failure time of a given component. There are many reasons why a computer system would not be expected to have this simple form. One is dependency. Computer systems are formed from many interacting components. The interactions with third party components mean that the environmental factors are always different. Again, the issue of fail-over and service latencies arises, spoiling the simple independent component picture. Mean time before failure doesn't mean anything unless we define the conditions under which the quantity was measured. In one test at Oslo College, the following values were measured for various operating systems, averaged over several hosts of the same type:
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