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Monotone Throughput Curve Although the unimodal, dome-shaped throughput curve (as a function of the aggressiveness threshold) is a representative shape, two other shapes monotonically increasing or decreasing are possible depending on the network con guration The shape of the curve is dependent on the relative magnitude of available resources (eg, bandwidth) versus the magnitude of aggressiveness as determined by the aggressiveness schedule E If resources are ``plentiful'' then aggressiveness is least penalized and it can lead to a monotonically increasing throughput curve On the other hand, if resources are ``scarce'' then aggressiveness is penalized most heavily and this can result in a monotonically decreasing throughput curve This phenomenon is shown in Fig 1812 Figure 1812 shows the throughput curves under the same network con guration except that the available bandwidth is decreased from the leftmost to the rightmost gure.





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This is affected by increasing the background traf c level from 25 Mb=s (Fig 1812(a)) to 5 Mb=s (Fig 1812(b)) to 75 Mb=s (Fig 1812(c)) We observe that the curve's shape transitions from monotone increasing to unimodal domeshaped to monotone decreasing In addition, due to the decrease in available bandwidth, overall throughput drops as the background traf c level is increased Figure 1813 shows the change in the shape of the throughput curve as the aggressiveness schedule E is shifted (or translated) upward that is, made overall more aggressive by 05, 20, 40, and 200 while keeping everything else xed We observe that an overall increase in the magnitude of aggressiveness can help improve throughput, transforming a monotone increasing throughput curve into a unimodal curve whose maximum throughput has increased.

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However, as the overall aggressiveness level is further increased, the cost of aggressiveness begins to outweigh its bene t and we observe a downward shift in the unimodal throughput curve SAC is designed to operate under all three network conditions, nding a nearoptimum throughput in each case The most challenging task arises when the.

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Remember that reliability engineering costs money; it s far more than what you ll spend on hardware or system software. It s an operating-systemindependent cost, and one that hasn t come down as a function of cheaper memory or larger disks. When you spend this money, you are not throwing good money after bad. You are investing money in the critical systems that run your operations so that they can run those operations more of the time. Those messages are probably the most important statements in this entire book. Together they build a message that balances system needs with the harsh realities of budget limitations.

Fig. 18.12 Shape of throughput curve as a function of aggressiveness threshold for three levels of background traf c: (a) 2.5 Mb=s, (b) 5 Mb=s, and (c) 7.5 Mb=s.

Introducing new technology, such as failover management, into your environment can bring on new problems. These issues are all unique to failover environments; they have no parallel in the single-host world. To be fair, they are quite rare, and you could run clusters for years without ever seeing them.

Fig. 18.13 Change in shape of throughput curve as the aggressiveness schedule E is shifted (upward) by 0.5, 2.0, 4.0, 20.0.

The first of these phenomena that are unique to clustered systems is called split-brain syndrome. The bane of FMS, split brain can occur when the servers in a clustered pair completely lose contact with each other while remaining otherwise operational. Both nodes believe that their partner has gone away

network con guration leads to a unimodal throughput curve for which nding the maximum throughput is least trivial That is, neither blindly applying aggressiveness nor absteining from it are optimal strategies SAC's adaptivity is also useful in nonstationary situations where the network con guration can shift from one quasistatic throughput state to another 18532 Perfect Prediction, Uncertainty, and Aggressiveness Now that we have shown that selective aggressiveness can help but indiscriminate aggressiveness can hurt, we seek to understand three further aspects of SAC: (1) how much performance is gained by applying selective aggressiveness (vis-a-vis not applying at all), (2) how much performance is lost due to prediction inaccuracies, and (3) what is a practical aggressiveness schedule to use since we cannot assume to know the aggressiveness threshold for which maximum throughput is achieved (when using a threshold schedule).

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