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Performance Analysis of Spillover-Partitioning Call Admission ControlAuthor Proof473474475476477478479480481482483484485efficiency by spillover-partitioning makes it practically possible to obtain at least near-optimalsolutions to be applied at runtime, compared with threshold-based CAC algorithms,which, because of high computational complexity, would take days to obtain a solution.4.2.1 Sensitivity Analysis of Solution QualityRecall that fast spillover CAC consists of two phases. The first phase searches for a feasibleinitial solution. The second phase performs a greedy search to increase the solution quality.The greedy search continues until the algorithm cannot improve the solution quality any further.Therefore, the initial solution conceivably may have a high impact on the final solutionquality. Below we perform a sensitivity analysis to test the sensitivity of the solution qualityof the initial solution (relative to the solution found by pure spillover CAC) with respect tothe traffic demand and QoS constraints, and see whether it affects the solution quality of thefinal solution of fast spillover CAC.Figure 8 shows the solution quality of the initial solution as the traffic demand increases,going from case 1 with λ 1 486 h = 3.66,λ1 n = 7.32,λ2 h = 2.69, and λ2 n = 3.59 to case 12 withλ 1 487 h = 3.66,λ1 n = 7.32,λ2 h = 5.99, and λ2 n = 7.98. We observe that the solution quality of488 the initial solution relative to that of the pure spillover algorithm remains about the same and489 is largely insensitive to the traffic demand, with the minimum solution quality ratio at 0.96490 and the maximum solution quality ratio at 0.98. Figure 8 also shows the solution quality of491 the final solution found by fast spillover CAC. We see that the solution quality of the final492 solution found by fast spillover CAC is largely insensitive to the initial solution found in the493 first phase of the algorithm.494 Figure 9 shows the solution quality of the initial solution as we reduce QoS constraints,going from case 1 with B 1 495 h t = 0.01, B1 n t = 0.025, B2 h t = 0.02, and B2 nt = 0.05 to case 5with B 1 496 h t = 0.05, B1 n t = 0.125, B2 h t = 0.1, and B2 nt = 0.25. The figure shows three traffic497 demand cases, namely, high, medium, and low. We see that in all traffic demand cases when498 QoS constraints are reduced (made less stringent), the solution quality of the initial solution499 decreases because less stringent QoS constraints allow the find_feasible function to find a500 solution more easily. This indicates that the initial solution is sensitive to QoS constraints.501 However, the final solution result found by the greedy search remains the same regardless of502 the solution quality of the initial solution. We conclude that the solution quality of the finaluncorrected proofFig. 8 Sensitivity of initial solution quality with respect to traffic demand123Journal: 11277 MS: WIRE823 CMS: 11277_2009_9673_Article TYPESET DISK LE CP Disp.:2009/2/19 Pages: 21 Layout: Small