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Lecture 9: Deterministic Fluid Models and Many-Server Heavy-Traffic Limits
IEOR 4615: Service Engineering
Professor Whitt
February 19, 2015
Outline
• Deterministic Fluid Models
– Directly From Data: Cumulative Arrivals and Departures
– Directly from Mt/M/st+M BD Model (deterministic view)
• Many-Server Heavy-Traffic Limits for QueueingModels
– Fluid Model Obtained in the Limit as Scale Increases
– Ultimately, limits explained by LLN and CLT
From Data to Fluid Models• To analyze data, we plot cumulative arrival and departure
functions:
• For large systems (bird’s eye view), the functions look smoother.
• Directly from event-based (call-by-call) measurements.
• For example, an isolated service-station:– A(t) = cumulative # arrivals from time 0 to time t;
– D(t) = cumulative # departures from system during [0, t];
– L(t) = A(T) − D(t) = # customers in system at t.
Arrivals and Departures from a Bank Branch Face-to-Face Service
From Data to Fluid Models
Deterministic Fluid Model
• Describe impact of Predictable Variability
– Time-Varying Arrival Rate
– Ignoring Stochastic Variability
• Idealistic Smooth Model
– Ordinary Differential Equation (ODE)
Phases of CongestionHall, textbook:
Four points of view
• Cumulative Arrivals and Departures
• Rates (⇒ Peak Load)
• Queues (⇒ Congestion)
• Outflows (⇒ end of rush-hour)
Phases of Congestion via Rates
• Time lag in congestion after peak arrival rate
• Changing Departure Rate
arrival rateα(t)
(maximum possible service rate)
Queueing System as a Tub (Hall, p.188)
• A(t) – cumulative arrivals function.• D(t) – cumulative departures function.• – arrival rate (dot = derivative d/dt)• – processing (departure) rate.• c(t) – maximal potential processing rate.• q(t) – total amount in the system at time t.
Mathematical Fluid Models: General Setup
)()( tDt
)()( tAt
Mathematical Fluid Model
Differential equation:
• λ(t) – arrival rate at time t ∈ [0,T].
• c(t) – maximal potential processing rate.
• δ(t) – effective processing (departure) rate.
• q(t) – total amount in the system at time t.
Then q(t) is a solution of
].,0[,)0();()()( 0 Ttqqtttq
Mathematical Fluid Model:Multi-Server Queue
• s(t) statistically-identical servers, each with service rate μ.
• c(t) = μs(t): maximal potential processing rate.
• : processing rate.
i.e.,
How to actually solve? Discrete-time approximation: Start with Then, for
].,0[,)0());(),(min()()( 0 Ttqqtqtsttq
.))(),(min()()()( 1111 ttqtstttqtq nnnnn
))(),(min()( tqtst
.)(,0 000 qtqt :1 ttt nn
.))(),(min()()0()(00 tt
duuqusduuqtq
Mathematical Fluid Model:Multi-Server Queue with Abandonment
• θ – Abandonment rate of customers in queue
• Processing rate:
• The fluid model:
• Deterministic View of Mt/M/st+M BD Model– Parameters: λ(t), μ, s(t),θ
].,0[,)0(
;)]()([))(),(min()()(
0 Ttqq
tstqtqtsttq
)]()([))(),(min()( tstqtqtst
Many-Server Heavy-Traffic Limit
Sequence of Mt/M/st+M Models Indexed by nLet n →∞.
Parameters:
• λn(t) = nλ(t) arrival rate at time t [heavy-traffic]
• sn(t) = ns(t) number of servers at time t [large scale]
• μn(t) = μ individual service rate (constant)
• θn(t) = θ individual abandonment rate (constant)
Stochastic Processes:
• An(t) number of arrivals in system n in [0,t]
• Dn(t) number of departures in system n in [0,t]
• Qn(t) number of customers in system n at time t
• Wn(t) potential waiting for arrival at time t (with infinite patience)
Fluid Approximation from Many-Server Heavy-Traffic Limit
Fluid Approximation from Many-Server Heavy-Traffic Limit
Fluid Approximation from Many-Server Heavy-Traffic Limit
Many-Server Heavy-Traffic LimitsSequence of Mt/M/st+M Models Indexed by n
Parameters:• λn(t) = nλ(t) arrival rate at time t [heavy-traffic]• sn(t) = ns(t) number of servers at time t [large scale]• μn(t) = μ individual service rate (constant)• θn(t) = θ individual abandonment rate (constant)Stochastic Processes:• An(t) number of arrivals in system n in [0,t]• Dn(t) number of departures in system n in [0,t]• Qn(t) number of customers in system n at time t• Wn(t) potential waiting for arrival (with infinite patience) Limits (Fluid Limit = Law of Large Numbers): As n →∞,• An(t)/n → Λ(t) = ∫
t λ(s) ds (integral of fluid arrival rate)
• Dn(t)/n → D(t) = ∫t δ(s) ds (integral of fluid departure rate)
• Qn(t)/n → q(t) fluid content at time t• Wn(t) → w(t) potential waiting time for atom of fluid
Refined Many-Server Heavy-Traffic LimitSequence of Queueing Models Indexed by n
Mt/M/st+M
Fluid Limits (Fluid Limit = Law of Large Numbers): As n →∞,
• An(t)/n → Λ(t) = ∫t λ(s) ds (integral of fluid arrival rate)
• Dn(t)/n → D(t) = ∫t δ(s) ds (integral of fluid departure rate)
• Qn(t)/n → q(t) fluid content at time t
• Wn(t)→ w(t) waiting time
Stochastic Limits (Gaussian Limit = Central limit Theorem):
As n →∞,
• √n[(An(t)/n) - Λ(t)] → XA(t)
• √n[(Dn(t)/n) - D(t)] → XD(t) Gaussian limit processes
• √n[(Qn(t)/n) - q(t)] → XQ(t)
• √n[Wn(t)- w(t)] → XW(t)
Three Many-Server Heavy-Traffic Limiting RegimesSequence of Stationary M/M/s+M Models
Parameters:• λn = nλ - c √n arrival rate at time t [No time-varying parameters]• sn = ns number of servers at time t [large scale]• μn = μ individual service rate (constant)• θn = θ individual abandonment rate (constant)Limiting Regimes• λ>s μ overloaded or Efficiency-Driven (ED)• λ<s μ underloaded or Quality-Driven (QD)• λ=s μ critically loaded – need to look more closelyExpanding the Critically Loaded Regime: λ=sμ• More general arrival rate scaling: • Quality-and-Efficiency-Driven (QED) regime = Halfin-Whitt (1981) regime
– λn = ns μ[1 – (β/√n)] (λ = s μ and c = -s μ β)– (1-ρn)√n =β where ρn = λn/snμ = λn/nsμ
Delay Probability Approximationin the M/M/s/∞ Model in the QED Regime
• Quality-and-Efficiency-Driven (QED) regime = Halfin-Whitt (1981) regime
– λn = ns μ[1 – (β/√n)]
– (1-ρn)√n =β where ρn = λn/snμ = λn/nsμ
– P(Wn > 0) → α with 0 < α < 1. (Wn steady state wait before starting service in model n)
P(Wn > 0) ≈ α(β) = HW(β) = 1/[βΦ(β)/φ(β)]
Where Φ(x) = P(N(0,1) < x) standard normal cdf and φ(β) is the associated density function
Implications for Staffingin the M/M/s/∞ Model
P(Wn > 0) ≈ Target = α = α (β)= HW(β) = 1/[βΦ(β)/φ(β)]
Where Φ(x) = P(N(0,1) < x) standard normal cdfand φ(β) is the associated density function
Use Square-Root Staffing Formula: Set
S = S(λ/μ) = (λ/μ) + β (λ/μ)1/2
For β = HW-1(α) (inverse function)
λ/μ = offered load (infinite-server model)
Implications for Staffingin the M/M/s+M Model
P(Wn > 0) ≈ Target = α = α(β, γ) = G(β, γ) = 1/[1 + γh(β/γ)h(-β)]
Garnett function from Garnett et al. (2002)
Where γ = (θ/μ)1/2, h(x) = φ(x)/[1- Φ(x)], Φ(x) = P(N(0,1) < x) standard normal cdf and φ(x) is the associated density function.
Use Square-Root Staffing Formula: Set
S = S(λ/μ) = (λ/μ) + β (λ/μ)1/2
For β = G-1(Target, γ)
ReferencesFluid Models
1. Chapter 6, especially Section 6.4, of Hall (1991) QueueingMethods for Services and Manufacturing, Prentice Hall.
2. Chapters 1 and 2 of Newell (1982) Applications of QueueingTheory, second edition, Chapman and Hall.
Many-Server heavy-Traffic Limits3. Halfin, S., W. Whitt. (1981) Heavy-traffic limits for queues with
many exponential servers. Operations Research, 29, 567-588.
4. Whitt, W. (1992) Understanding the Efficiency of Multi-Server Service Systems. Management Science, 38, 708-723.
More Advanced ReferencesFluid Models
1. Hampshire, R. C., W. A. Massey. (2010). A tutorial on dynamic optimization with application to dynamic rate queues. TutORials in Operations Research, presented at the INFORMS National Meeting in Austin Texas. (www.princeton.edu~wmassey)
Many-Server Heavy-Traffic Limits2. Garnett, O., A. Mandelbaum, M. I. Reiman. (2002) Designing a call
center with impatient customers. Manufacturing Service Oper. Management 4, 208–227. (http://iew3.technion.ac.il/serveng)
3. Pang, G., R. Talreja, W. Whitt. (2007) Martingale proofs of many-server heavy-traffic limits for Markovian queues. Probability Surveys, 4, 193-267.
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