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REMARK 6.5.6. Relation (6.10) between the LSTs of the actual waiting time W and the virtual waiting time V readily implies that Theorems 6.5.2 6.5.5 also hold for the virtual waiting time. 6.6 A HEAVY TRAFFIC LIMIT THEOREM FOR THE WORKLOAD PROCESS IN THE GI=G=1 FCFS QUEUE In the previous section we have considered the limiting distribution for r 4 1 of the contracted waiting time, D r W (or equivalently D r V, cf. Remark 6.5.6). In the



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We discuss the cost of downtime in other places around the book, but no discussion of the business impact of downtime and availability is complete

present section we consider the workload process fvt ; t ! 0g of the GI =G=1 queue in heavy traf c. In order to get a proper limiting process, we not only apply the same coef cient of contraction D r , but we also scale time by a factor D1 r : 1 r D r . It can be shown that w t; r : D r vt=D1 r 6:64

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converges in distribution for r 4 1, for every t > 0. It can further be shown that the thus scaled and contracted workload process fw t; r ; t ! 0g converges weakly to the workload process of a queueing model of which the input is described by a stable Levy motion if 1 < n < 2 and by Brownian motion if n 2 (with n the index of the heaviest tail). As a preparation for discussing these results in some detail, we consider the noise traf c nt kt rt and the workload backlog ht kt t, with kt the amount of traf c generated in 0; t . It is shown in Theorem 4.1 of Cohen [21] that nt and ht, when scaled similarly as vt in Eq. (6.64), have limiting distributions for r 3 1. In that theorem, the same cases are considered as in Theorems 6.5.2 6.5.5 and for the coef cient of contraction D r we take precisely the contraction factors of those theorems: 1. The tail of B t is heavier than that of A t : D r DB r . 2. The tail of A t is heavier than that of B t : D r DA r . 3. A t and B t have similar tails: D r DAB r . De ne N t; r : D r nt=D1 r ; Theorem 6.6.1 (i) The stochastic variables N t; r and H t; r converge in distribution for r 4 1. (ii) Let N t and H t be stochastic variables with as distribution the limiting distribution of N t; r and H t; r , respectively; then for t ! 0, Re r 0, E e rN t =a er t=a e e

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H t; r : D r ht=D1 r :

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