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Let Bn ; n ! 1, be a sequence of random variables representing the total amounte of uid that is brought t b e to the system during the nth activity period, that is, Bn tbn AI dt, where tn and tn t n represent the beginning and end of the nth activity period, respectively Furthermore, def on de ne Dc;n Bn cln ; 0 < c r; note that Dn Dc;n is a nonnegative random variable If we imagine that AI represents the rate at which the uid is arriving to a t uid queue, and that c is the constant rate at which the queue drains, then Dn represents the queue increment during the nth activity period In order to derive the queueing asymptotics, we rst have to understand the asymptotic behavior of Dn A proof of the following result can be found in Jelenkovic and Lazar [35].



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Theorem 10414 Consider an M =GI =I arrival process with on periods being regularly varying P ton > x l x =xa ; a > 1, where a is noninteger If 0 < c r, then on P Dn > x $ eLEt P ton > x r rLEton c as x 3 I: 10:21.

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REMARK 10.4.15. Recently in Resnick and Samorodnitsky [50] it was shown that this result holds under a more general condition of ton being intermediately regularly varying and 0 < c r rLEton . 10.4.3.3 Queueing Asymptotics. Let QP;I be the queue size observed at the n beginning of the nth activity period of the M =GI =I arrival process. Theorem 10.4.16. Let r EAI LrEton < c. If c t varying with noninteger exponent a > 1, then r, and ton is regularly

marketing with claims of larger limits, it was nearly impossible to implement larger clusters With the advent of SANs, cluster sizes have grown immensely Today s products support clusters of 32 and 64 nodes, with larger clusters likely, especially as blade computers become more prominent (for more on blade computing, see 22, A Brief Look Ahead ) Although cluster configurations can grow quite large, some shops have been reluctant to grow clusters beyond a certain size The largest cluster in production that is running on commercial FMS that we are aware of is a 20-node cluster running VERITAS Cluster Server on Solaris Most clusters tend to stay smaller than that; with 7 to 10 nodes being the most common size after 2-node clusters (which are easily the most popular size) When clusters get larger than that, configuration files get large, and the clusters can become harder to manage.

off Proof. Denote with An Dc;n , Cn cln , use E Cn An eLEt c r =L and apply Theorem 10.3.1. j

Plus, in the unlikely event that the FMS itself causes an outage, the effects become much more significant when that likelihood is multiplied across many servers In terms of the technology covered in this chapter, very little actually changes when you discuss larger clusters Shared disks are still required, albeit SAN-based disks Each host still needs its three kinds of networks And, of course, critical applications are required; without them, there s little point to clustering at all Probably the biggest change due to the increase in cluster size is in the heartbeat networks When cluster sizes exceed two nodes, it becomes necessary to use hubs to build the heartbeat networks When you use hubs to manage your heartbeat networks, make sure that the hubs do not add latency to the traffic, and merely forward packets on to their appropriate destination.

In the next theorem, under more general assumptions, we obtain a tight lower bound for the uid queue asymptotics with M =GI =I arrivals (see Jelenkovic and Lazar [35]). For this uid queue we denote its queue content process as QI . It was t conjectured [35] that the following bound represents actually the exact asymptotics. Theorem 10.4.17. then Let r EAtI;s LrEton < c. If r r > c, and ton P ir, P QI > x Lr t : ! P ton > u du c r x= r r c

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