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Unlike raw code development, database development often has some constraints on it Database development generally takes place within a mature database application, such as Oracle, DB2, or SQL Server, and the actions performed by the code are more likely to be restricted to database-related activities If your database developers are writing code that might hang or crash the standby system, then their work should be classified as Bad If their work is only going to impact the database environment on the takeover server, rather than on the whole system, then it can be classified as Acceptable One of the leading arguments for putting developers on the standby server is that it s easy to kick them off when their system is needed for production.



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In the presence of active cell-discard, homogeneous delay-jitter bounds imply that all VCs have the same frame size. We consider the following two cases separately: (1) homogeneous cell-loss guarantees and (2) heterogeneous cell-loss guarantees. For the latter case, all virtual circuits with the same cell loss requirement are grouped into the same class. 19.4.1.1 Scenario 1: Equal Cell-Loss Probability Requirements This is the trivial case and is included for completeness. Let the number of connections be N , and let Ei E for all i. Let all frames be synchronized which is a worst-case scenario for cell losses. Let X1 ; . . . ; XN be the number of cell arrivals from connections 1 through N , in a given frame. Let SN X1 XN : Let fx x be the probability density function of Xk , k 1; . . . ; N . Let fSN x be the probability density function of SN . Assuming that traf c across connections are mutually independent, fSN x is the convolution of the fk x terms: fSN x f1 x f2 x fN x : 19:6





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Since the developers might be kicked off their server at any moment, it means that they must change the way they work, saving their work far more often than they might otherwise If they forget to save frequently, then they could lose their work when a failover occurs It also gives the perception that their work is of limited value to the overall operation, since they may be asked to stop working at any time without warning In organizations where we have seen this model implemented, it has a detrimental effect on the morale of the developers and causes them to place less value on their own work and contributions to the organization In most cases, it just makes them angry.

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Analyst and marketing optimization expert Jim Novo s review of Firm-Created Word-of-Mouth Communication: Evidence from a Field Test, Marketing Science, Vol. 28, No. 4, by David Godes and Dina Mayzlin, 2009. Posted on the Web Analytics Association web site at www.webanalyticsassociation.org/en/art/712.

10:22

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What s more, while critical services are running on their development server, the developers are unable to do any work, resulting in a slowdown in their performance, and delayed deliverables An additional cost to the business is the direct result of their idle time; they are collectively sitting around doing nothing, while collecting their wages and other benefits You re choosing one kind of downtime for another When evaluating the overall cost of.

where cN c N Eait , and LN N Eait = rEton . This approximation is termed an M =G=I approximation. This approximation is to be used when the queue is stable and r N 1 Eait > c is satis ed. For simulation purposes we consider a discrete-time `` uid'' queue. Correspondingly, we replace exponential off periods with geometrically distributed random variables P toff t p 1 p t 1 ; t 1; 2; 3; . . . : For on periods we consider the Pareto family P ton ! t 1=t a ; t 1; 2; . . . ; a > 0. Here, for the discrete Pareto case we use P QN x % t LN r r cN a 1 x a ; cN 10:23

building a cluster in this manner, be sure to include the cost of idling developers and delayed deliverables.

where cN , and LN are as de ned earlier. The ef cacy of the approximation (10.23) is illustrated in the following simulation experiment (for additional experiments see Jelenkovic and Lazar [35]). Example 10.4.18. Choose p 0:05, a 3, r 2, c 3. This gives Eton 1:202, and Eait 0:113. Then, for N 20; 25 processes, the approximations are given by b=x3, b 4:14, 48.04, respectively. The desirable closeness between the simulation results and the approximations is represented in Fig. 10.3. It is interesting to observe that in this case the peak rate of each individual process is smaller than the capacity of the server.

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