## 5.5.2 Workload Estimation

The last conjecture brings us to feature 4 of the entire approximation procedure, in which we obtain a set of N simultaneous equations relating the N unknown n's to the dispatch policy and the arrival rates from the various geographical atoms. Suppose that we again equate the mean service time to unity (i.e., -1 = 1), thus making the unit of time equal to one mean service time unit. Then, the net rate Rn at which server n is assigned to customers, measured over a very long time period, is equal to the workload n of server n. Thus, if we can determine a value for Rn, we have determined a value for n. We describe mathematically the dispatch preference policy by defining the following:

 Gnj set of geographical atoms for which server n is the jth preferred dispatch alternative, n, j = 1, 2, . . . N maj identification number of the jth preferred server for atom a

For instance, if G23 = {7, 8, 10, 12}, server 2 is the third preferred dispatch alternative for atoms 7, 8, 10, 12 and, for instance, m7 3 = 2. Now an exact expression for Rn can be written as follows:

 Rn = (5.50)

where the term D accounts for delayed dispatches from a queue, which are apportioned equally among all N servers; thus,

D = (assuming an infinite-capacity queue)

In words, (5.50) states that the net rate at which server n is assigned to customers is equal to (1) the rate at which customers arrive from server n's primary response area, multiplied by the fraction of time server n is available to start servicing such customers immediately; plus (2) the rate at which customers arrive from server n's second-preferred areas, multiplied by the fraction of time that both the first preferred server is busy and server n is available to start servicing such customers immediately; plus (3) similar terms for the remaining j-preferred areas; plus (4) a term that apportions equally to all servers a fraction (1/N) of customers delayed in queue.

Given our approximation conjecture, we can simplify (5.50) by estimating the dispatch probabilities as products of utilization or availability factors and the appropriate correction term. For instance, we approximate P{B3B6F5} Q(N, , 2)36(1 - 5). Given this approximation and substituting Rn = n, we can rewrite (5.50) as

 n = (5.51)

We notice that each of the summation terms on the right-hand side of (5.51) contains (1 - n) as a factor. If we remove this factor, then the sum of these N terms is not a function of n, a desirable property in an iterative procedure whose purpose is to compute an estimate for n.

 RnF (5.52)

has an intuitive interpretation. It is the (approximate) customer assignment rate for server n during periods when server n is free. Simple manipulation of (5.51) yields

 n = n = 1, 2, . . . , N (5.53)

Here we assume that D < 1, implying that n < 1; otherwise, the system would be overloaded. Equation (5.53) [together with (5.52)] represent a set of N simultaneous nonlinear equations relating the N unknowns n, n = 1, 2, . . . , N. They can be solved iteratively to obtain values for each of the n's. A specific iterative algorithm is displayed in Figure 5.19. As you will discover in Problem 5.12, (5.53) yields a very reasonable approximation for the workload even on the first iteration (in which all workloads on the righthand side of the equation are set equal). Rarely are more than three or four iterations required for even very stringent convergence criteria.