BRAVO for QED Queues

Transcription

1 1 BRAVO for QED Queues Yoni Nazarathy, The University of Queensland Joint work with Daryl J. Daley, The University of Melbourne, Johan van Leeuwaarden, EURANDOM, Eindhoven University of Technology. Applied Probability Society Conference, Costa Rica, July, 2012.

2 Daryl J. Daley 2

3 Queues and Counting Processes: Q(t) = Q(0) + ( A(t) L(t) ) ( ) R(t) + D(t)

4 Queues and Counting Processes: Q(t) = Q(0) + ( A(t) L(t) ) ( ) R(t) + D(t) Why analysis of the output counting process D(t)? Orders Production Arrival process to a downstream queueing system

5 Queues and Counting Processes: Q(t) = Q(0) + ( A(t) L(t) ) ( ) R(t) + D(t) Why analysis of the output counting process D(t)? Orders Production Arrival process to a downstream queueing system Daryl Daley, Queueing Output Processes, Advances in Applied Probability, 1976.

6 Queues and Counting Processes: Q(t) = Q(0) + ( A(t) L(t) ) ( ) R(t) + D(t) Why analysis of the output counting process D(t)? Orders Production Arrival process to a downstream queueing system Daryl Daley, Queueing Output Processes, Advances in Applied Probability, Some performance measures of interest The law of {D(t), t 0} E[D(t)], Var ( D(t) ) λ := lim t E[D(t)] Var ( ) D(t) t, V := lim t Asymptotic normality: D(t) N ( λ t, V t t, D := V λ ), large t

7 Outline 4 A (new) formula for asymptotic variance of outputs, D := V λ Single servers (older BRAVO results) Many server scaling (new BRAVO results)

8 Asymptotic Variance of Outputs 5

9 Finite Birth-Death Asymptotic Variance 6 Irreducible birth-death process on finite state space Birth rates: λ 0,..., λ J 1 Death rates: µ 1,..., µ J Stationary distribution: π 0,..., π J D(t) is number of downward transitions (deaths) during [0, t], each filtered independently with state-dependent probabilities, q 1,..., q J. e.g. The departure process (served customers) in M/M/s/K+M systems Of interest: D = V λ = lim Var ( D(t) ) t E[D(t)]

10 Finite Birth-Death Asymptotic Variance Formula 7 Theorem: Daryl Daley, Johan van Leeuwaarden, Y.N ( ) Var D(t) D := lim = 1 2 t E[D(t)] with, J (P i Λ i ) (q i+1 λ ) (P i Λ i ), π i λ i i=0 i J P i := π j, λ := µ j q j π j, Λ i := j=0 j=1 i j=1 µ jq j π j λ. Note: In Weiss, Y.N. 2008, similar expression for case q i 1 Note: In case λ i λ, q i 1: π J D = π J J i=0 P i ( 1 π J P i π i )

11 Idea of Renewal Reward Derivation 8 Embed D(t) in a Renewal-Reward Process, C(t) 1 (X n, Y n ) (busy cycle, number served) in cycle n 2 N(t) = inf{n : n i=1 X i > t}, C(t) = N(t) i=1 Y i 3 Asymptotic variance rates of C(t) and D(t) are equal 4 Known: 1 Asymptotic variance rate of C(t) is E[X ] Var( Y E[Y ] E[X ] X ) Systems of equations for 1 st, 2 nd and cross moments of X and Y D t C t Y 3 Y 2 X 3 1 Y 1 X 2 t

12 Single Server BRAVO (older results) 9

13 M/M/1/K Queue 10 Here π i is truncated geometric distribution when λ µ and a uniform distribution when λ = µ Using D = 1 2 π ( J J 1 π J i=0 P P i 1 π i J π i ):

14 M/M/1/K Queue Here π i is truncated geometric distribution when λ µ and a uniform distribution when λ = µ ( P 1 π i J π i ): Using D = 1 2 π J J 1 π J i=0 P i { D = 1 + o K (1), λ µ, o K (1), λ = µ.

15 M/M/1/K Queue Here π i is truncated geometric distribution when λ µ and a uniform distribution when λ = µ ( P 1 π i J π i ): Using D = 1 2 π J J 1 π J i=0 P i { D = 1 + o K (1), λ µ, o K (1), λ = µ. 1 M M 1 K 10 1 M M 1 K 40 1 M M 1 K

16 M/M/1/K Queue Here π i is truncated geometric distribution when λ µ and a uniform distribution when λ = µ ( P 1 π i J π i ): Using D = 1 2 π J J 1 π J i=0 P i { D = 1 + o K (1), λ µ, o K (1), λ = µ. 1 M M 1 K 10 1 M M 1 K 40 1 M M 1 K We call this BRAVO: Balancing Reduces Asymptotic Variance of Outputs

17 M/M/1 Queue When K =, the formula for D does not hold. In this case, { 1, λ µ, D =?, λ = µ. A guess is 2 3, since for K <, D = o K (1)...

18 M/M/1 Queue When K =, the formula for D does not hold. In this case, { 1, λ µ, D =?, λ = µ. A guess is 2 3, since for K <, D = o K (1)... Theorem: Ahmad Al-Hanbali, Michel Mandjes, Y. N., Ward Whitt, 2011 For the M/M/1 queue with λ = µ and arbitrary initial conditions of Q(0) (with finite second moments), ( D = ) π Proof based on analysis of classic Laplace transform of generating function of D(χ) where χ is an exponential random variable.

19 G/G/1 Queue 12 Moving away from the memory-less assumptions, ca 2, λ < µ, D =?, λ = µ, cs 2, λ > µ. For M/M/1 it was 2(1 2 π )...

20 G/G/1 Queue Proof based on diffusion limit of (D(n ) λn )/ λn as n (Iglehart and Whitt 1971). Fourth moments are a technical condition used in establishing uniform integrability. Moving away from the memory-less assumptions, ca 2, λ < µ, D =?, λ = µ, cs 2, λ > µ. For M/M/1 it was 2(1 2 π )... Theorem: Ahmad Al-Hanbali, Michel Mandjes, Y.N., Ward Whitt, 2011 For the G/G/1 queue with λ = µ, arbitrary finite second moment initial conditions ( Q(0), V (0), U(0) ), and finite fourth moments of the inter-arrival and service times, ( D = (ca 2 + cs 2 ) 1 2 ). π

21 G/G/1/K Queue 13 ca 2 + o K (1), λ < µ, D =?, λ = µ, cs 2 + o K (1), λ < µ. For M/M/1/K it was o K (1), for G/G/1 it was (c 2 a + c 2 s )(1 2 π )...

22 G/G/1/K Queue 13 ca 2 + o K (1), λ < µ, D =?, λ = µ, cs 2 + o K (1), λ < µ. For M/M/1/K it was o K (1), for G/G/1 it was (c 2 a + c 2 s )(1 2 π )... Conjecture (numerically tested), Y.N., 2011 For the G/G/1/K queue with λ = µ and arbitrary initial conditions and light-tailed service and inter-arrival times, D = (ca 2 + cs 2 ) O( 1 ). K Numerical verification done by representing the system as PH/PH/1/K MAPs

23 Many Servers 14

24 M/M/s/ s 15 D p 1.0 s=10 4 s= s=100 s=9 0.6 D 0, r

25 Quality and Efficiency Driven (QED) Scaling Regime 16 A sequence of systems Consider a sequence of M/M/s/K queues with increasing s = 1, 2,... and with ρ s := λ sµ and K s such that, (1 ρ s ) s β (, ) K s s η (0, ) So for large s: ρ s 1 β/ s K s η s Halfin, Whitt, 1981, Garnett, Mandelbaum, Reiman 2002, Borst, Mandelbaum, Reiman, 2004, Whitt, 2004, Pang, Talreja, Whitt, 2007, Janssen, van Leeuwaarden, Zwart, 2011, Kaspi, Ramanan 2011, first session of this morning...

26 Favorable QED Properties 17 Probability of delay converges to a value (0, 1) Mean waiting times are typically O(s 1/2 ) Large queue lengths almost never occur Quick mixing times In applications: Call-centers (etc...) describes behavior well and allows for asymptotic approximate optimization of staffing etc... How about BRAVO?

27 BRAVO for QED Queues 18

28 M/M/s/K QED BRAVO Theorem: Daryl Daley, Johan van Leeuwaarden, Y.N Consider QED scaling with β 0: D β,η := lim lim Var ( D(t) ) s,k t E ( D(t) ), D β,η = 1 2β2 e βη h 2 ( 1 βe βη h Φ( u) ) Φ( u) du φ(β) β φ(u) ( ) + 2e βη h(1 + e βη h) 1 βη e βη + (1 2βηe βη e 2βη )h where h = lim s P ( Q s s) 1 e βη = 1 1 e βη + βφ(β) φ(β)

29 BRAVO viewed through the QED lens 20 Β Η Η 2 Η 1 Η Β

30 21 D p 1.0 s=10 4 s= s=100 s=9 0.6 D 0, r

31 M/M/s/K QED BRAVO with ρ 1 (β = 0) 22 Theorem: Daryl Daley, Johan van Leeuwaarden, Y.N D 0,η := K s s η (0, ) lim lim Var ( D(t) ) s,k t E ( D(t) ), D 0,η = 2 ( ) 6 3π 3 2 η 1 2 π π π(1 log 2) 3 ( η + ) π 3. 2

32 M/M/s/ η s s at ρ 1 (β = 0) Η

33 Summary 24 Known BRAVO constants: Single server finite buffer: 2/3 (for G/G replace 2 by c 2 a + c 2 s ) Single server infinite buffer 2(1 2/π): (for G/G replace 2 by c 2 a + c 2 s ) Memoryless many servers finite buffer: D 0,η [0.6, 2/3] Not yet known: Memoryless many servers infinite buffer. Many servers without memoryless assumptions Systems with reneging or other customer loss mechanisms Other questions: How can BRAVO be harnessed in practice? Why does BRAVO occur?

34 References 25 Daryl J. Daley, Johan van Leeuwaarden and Y.N., BRAVO for QED Finite Birth-Death Queues, preprint. Daryl Daley, Revisiting queueing output processes: a point process viewpoint, Queueing Systems, 68, pp , Y.N., The variance of departure processes: puzzling behavior and open problems, Queueing Systems, 68, pp , Ahmad Al-Hanbali, Michel Mandjes, Y.N. and Ward Whitt, The asymptotic variance of departures in critically loaded queues, Advances in Applied Probability, 43, pp , Y.N. and Gideon Weiss, The asymptotic variance rate of the output process of finite capacity birth-death queues, Queueing Systems, 59, pp , 2008.