Capacity Allocation in Generalized Jackson Networks
Capacity Allocation in Generalized Jackson Networks
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The four values of D were chosen to correspond to values of p=. 6, . 7, . 8, and . 9, respectively. For all four cases, we performed simulation experiments on three capacity assignment rules: the Brownian approximation (denoted by B in Table 1) from equation (10), Kleinrock's assignment (denoted by K) from equation (1), and the proportional capacity cissignment 6 (denoted by P), where capacity is allocated in direct proportion to the effective arrival rates; that is, fil = 2'' D for A: = 1, ...... , K (13) (INSERT TABLE 1 HERE) The results are displayed in Table 1. Each combination of capacity assignment and traffic intensity was tested by 200 independent runs of 10, 000 customers each. For each case, we computed the average customer sojourn time (and 95% confidence interval), the average server utilization levels, and the average queue lengths at the three stations. For all values of the traffic intensity /?, the Brownian approximation outperformed the pro- portional assignment rule, which in turn outperformed Kleinrock's assignment rule.
What to read after Capacity Allocation in Generalized Jackson Networks?
You can find similar books in the "Read Also" column, or choose other free books by Lawrence M Wein to read onlineMoreLess
To quickly assess the difficulty of the text, read a short excerpt:
The four values of D were chosen to correspond to values of p=. 6, . 7, . 8, and . 9, respectively. For all four cases, we performed simulation experiments on three capacity assignment rules: the Brownian approximation (denoted by B in Table 1) from equation (10), Kleinrock's assignment (denoted by K) from equation (1), and the proportional capacity cissignment 6 (denoted by P), where capacity is allocated in direct proportion to the effective arrival rates; that is, fil = 2'' D for A: = 1, ...... , K (13) (INSERT TABLE 1 HERE) The results are displayed in Table 1. Each combination of capacity assignment and traffic intensity was tested by 200 independent runs of 10, 000 customers each. For each case, we computed the average customer sojourn time (and 95% confidence interval), the average server utilization levels, and the average queue lengths at the three stations. For all values of the traffic intensity /?, the Brownian approximation outperformed the pro- portional assignment rule, which in turn outperformed Kleinrock's assignment rule.
What to read after Capacity Allocation in Generalized Jackson Networks?
You can find similar books in the "Read Also" column, or choose other free books by Lawrence M Wein to read onlineMoreLess
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