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Chapter 4

Chapter 4. λ. 2λ. 0. 1. 2. μ. 2μ. Non-nearest neighbor MC’s. Example. μ. Erlang Distribution. r = 1. 2μ. 2μ. Erlang Distribution. r = 2. rμ. rμ. rμ. 1. 2. r. r-stage Erlang Distribution. b(x). r = 1. 2. x. b(x). r = ∞. r = 1. μe -μ. 2. x. λ. departures. rμ.

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Chapter 4

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  1. Chapter 4

  2. λ 2λ 0 1 2 μ 2μ Non-nearest neighbor MC’s • Example

  3. μ Erlang Distribution r = 1

  4. 2μ Erlang Distribution r = 2

  5. rμ rμ 1 2 r r-stage Erlang Distribution

  6. b(x) r = 1 2 x b(x) r = ∞ r = 1 μe-μ 2 x

  7. λ departures rμ rμ rμ 1 2 r Queue λ λ λ 0, 0 1, 1 2, 1 3, 1 γμ γμ λ 1, 2 2, 2 γμ γμ γμ γμ γμ λ 1, r 2, r 3, r M/Er/1 • (k, i) stage of service customer is in # in system

  8. k-1, i γμ k-1, i λ k-1, i λ λ λ k-1, i λ λ λ λ λ 0 1 2 r-1 r r+1 r+2 γμ k-1, i rμ rμ rμ rμ rμ rμ rμ rμ λ λ λ λ λ λ j-r j-1 j j+1 j+r rμ rμ rμ rμ rμ rμ rμ rμ

  9. departures μ rλ rλ rλ 1 2 r Queue Arrival box rλ rλ rλ rλ rλ rλ rλ 0 1 2 r-1 r r+1 rλ rλ rλ rλ rλ rλ rλ rλ μ μ μ μ μ μ j-r j-1 j j+1 j+r μ μ μ μ μ μ μ μ Er/M/1 二維 or 一維: j = stages of arrival completed by all customers in system plus 〃 all customers in arrival box

  10. (r+1) roots 中有一個根 at Z=1 Im Z=1 Re

  11. λ λ λ λ λ λ λ 0 1 2 r-1 r r+1 r+2 μ μ μ μ μ μ μ μ Bulk Arrival System • M/M/1 Bulk Arrival • Bulk size = r • 與 M/Er/1 比較, 把 M/Er/1 中的 • rμ 改成 μ • ρ 改成

  12. λ λ λ λ λ λ λ 0 1 2 r-1 r r+1 μ μ μ μ μ μ 註: Bulk Service • M/M/1 Bulk Service • Bulk size = r • 與 Er/M/1 比較, 把 Er/M/1 中的 • rλ 改成 λ • ρ 改成

  13. rgk rgk-1 rg2 rg2 rgk-2 rg1 rg1 rg1 rg1 0 1 2 k-2 k-1 k k+1 k+1 μ μ μ μ μ μ μ μ μ Bulk Arrival System • M/M/1 Bulk Arrival • Bulk size = random • gk = P[Bulk size = k] • 與 M/M/1 Bulk Arrival, bulk size = k 比較

  14. λ λ λ λ λ λ λ 0 1 2 r-1 r r+1 μ μ μ μ μ μ Bulk Service System • M/M/1 Bulk Service • Bulk size = r • 與 Er/M/1 比較, 把 Er/M/1 中的 • rλ 改成 λ • ρ 改成

  15. λ λ λ λ λ λ λ 0 1 2 r-1 r r+1 μ μ μ μ μ μ μ μ • Suppose less than r customers can be served immediately • (no need to wait until full bulk = r)

  16. b(x) r = 1 rμ μr rμ μ1 rμ μ2 2 1 1 2 2 r r x r-stage Erlang dist (Er)

  17. μ1 α1 μ2 α2 αR μR 2 2 Hyperexponential Dist (HR)

  18. r1μ1 r1μ1 r1μ1 r1 α1 1 2 αk rkμk rkμk rkμk rk αR 1 2 rRμR rRμR rRμR service facilityone customer at one time rR 1 2 Series-Parallel Stage-Type device

  19. β1 βr β2 μ1 μr μ2 1-β1 1-βr 1-β2 Coxian Stage-Type Device

  20. Poisson ? Poisson Networks of Queues • Paul Burke(1954) • Burke’s Theorem: • The only(FCFS) queuing systems which give Poisson out for Poisson in is M/M/1/n

  21. Cn-1 Cn Cn+1 Cn+2 λ Xn+1 departures Xn+2 Xn idle μ Server Queue Cn Cn+1 Cn+2 Wn Wn+1 Wn+2=0 Queue Cn Cn+1 Cn+2 • M/M/1:

  22. Case 1: Case 2:

  23. p pλ λ λ λ 2 (1-p)λ 1-p (1-p)λ 1 4 3 Feed forward ??M/M/mi

  24. j i rij • J.R.Jackson(1957) • = External arrival rate to node i (Poisson) • mi = Number of parallel servers in node i (Exponential) with mean service (1/μi) sec. • rij = P[node j next after node i] • P[leave network after service in node i] = • λi = Total traffic handled by node i (sum of external + internal arrivals)

  25. p λ μ Poisson Poisson λ 1-p NOT Poisson

  26. (1963)force the system to always have K customers

  27. j i rij • Gordon and Newell(1967) • mi = # servers in node i (Exponential) with mean service (1/μi) sec. • rij = P[node j next after node i].

  28. Bottlenecknode Closed Queuing Networks

  29. terminal A K A B A B 1 1 0 0 1 SWAPPINGDEVICE 1 CPU B 不同 class of job have different probability to go

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