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On Control of Queueing Networks and The Asymptotic Variance Rate of Outputs Ph.d Summary Talk Yoni Nazarathy Supervised by Prof. Gideon Weiss Haifa Statistics Seminar, November 19, 2008
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2 PLANT OUTPUT The Problem Domain Finite Horizon [0,T] Desired: 1.Low Holding Costs 2.Low Resource Idleness 3.Low Output Variability
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3 Queues and Networks A Brief Survey
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4 Phenomena of Queues
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5 Key Phenomena Stability / instability Congestion increases with utilization Variability of primitives causes larger queues Steady state Little’s law Flashlight principle State space collapse …
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6 Queueing Networks
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7 Multi-Class = 2
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8 Infinite Inputs
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9 Miracles
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10 PLANT OUTPUT The Problem Domain Finite Horizon [0,T] Desired: 1.Low Holding Costs 2.Low Resource Idleness 3.Low Output Variability
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11 Server 1Server 2 1 2 3 Attempt to minimize: Near Optimal Finite Horizon Control
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12 s.t. Separated Continuous Linear Program (SCLP) Fluid Relaxation Server 1Server 2 1 2 3
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13 SCLP – Bellman, Anderson, Pullan, Weiss Piecewise linear solution Simplex based algorithm, finite time (Weiss) Optimal Solution: Fluid Solution
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14 Fluid Tracking
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15 seed 1 seed 2 seed 3 seed 4 Asymptotic Optimality
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16 PLANT OUTPUT The Problem Domain Finite Horizon [0,T] Desired: 1.Low Holding Costs 2.Low Resource Idleness 3.Low Output Variability
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17 2 job streams, 4 steps Queues at 2 and 4 Infinite job supply at 1 and 3 2 servers The Push-Pull Network 12 3 4 Control choice based on No idling, FULL UTILIZATION Preemptive resume Push Pull Push Pull
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18 Configurations Inherently stable network Inherently unstable network Assumptions (A1) SLLN (A2) I.I.D. + Technical assumptions (A3) Second moment Processing Times Previous Work (Kopzon et. al.): 12 3 4
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19 Policies Inherently stable Inherently unstable Policy: Pull priority (LBFS) Policy: Linear thresholds 12 3 4 Typical Behavior: 2,4 3 4 2 1 1,3 Typical Behavior: Server: “don’t let opposite queue go below threshold” Push Pull Push 1,3
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20 KSRS 12 3 4
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21 Push pull vs. KSRS Push Pull KSRS with “Good” policy
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22 Stability Result 12 3 4 QueueResidual is strong Markov with state space Theorem: Under assumptions (A1) and (A2), X(t) is positive Harris recurrent. Proof follows framework of Jim Dai (1995) 2 Things to Prove: 1.Stability of fluid limit model 2.Compact sets are petite Positive Harris Recurrence:
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23 PLANT OUTPUT The Problem Domain Finite Horizon [0,T] Desired: 1.Low Holding Costs 2.Low Resource Idleness 3.Low Output Variability
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24 Example 1: Stationary stable M/M/1, D(t) is PoissonProcess( ): Example 2: Stationary M/M/1/1 with. D(t) is RenewalProcess(Erlang(2, )): Variability of Outputs Asymptotic Variance Rate of Outputs For Renewal Processes:
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25 Taken from Baris Tan, ANOR, 2000. Previous Work: Numerical
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26 BRAVO Effect
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27 BRAVO Effect: A Phenomena Using a “renewal-reward” method for regenerative simulation for. Queues with Restricted Accessibility (Perry et. al.)
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28 Summary of Results Queueing System Without LossesFinite Capacity Birth Death Queue Push Pull Queueing NetworkInfinite Supply Re-Entrant Line
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29 Infinite Supply Re-entrant Line 4 2 1 3 5 6 7 8 10 9
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30 “Renewal Like” 4 2 1 3 5 6 7 8 10 9 1 6 8
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31 A Future Direction
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32 Finite Q Rate 1 Infinite Q Rate 2 α α 1 Steady State Total Mean Queue Sizes An Implication of BRAVO? ? IT DOESN’T “WORK”
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Finite Q Rate 1/4 Rate 1/4 Finite Q Infinite Q Rate 2 Rate 1/2 Infinite Q Poisson(α) Overflow Overflows Priority Infinite Q Rate 1 α Steady State Mean Queue Sizes 11/4 When rate exceeds ¼ overflows of first queue cause the second server to mostly give priority to the fast stream. Non Monotonic Networks ?
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34 Now Lets Do לחיים !
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