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Queueing Models Chapter 8 Queueing Models Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.1 Contents • Characteristics of Queueing Systems • QQgueueing Notation – Kendall Notation • Long-run Measures of Performance of Queueing Systems • Steady-state Behavior of Infinite-Population Markovian Models • Steady-state Behavior of Finite-Population Models • Networks of Queues Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.2 Purpose • Simulation is often used in the analysis of queueing models. • A simple but typical queueing model Calling population Waiting line Server • Queueing models provide the analyst with a powerful tool for designing and evaluating the performance of queueing systems. • Typical measures of system performance • Server utilization, length of waiting lines, and delays of customers • For relatively simple systems: compute mathematically • For realistic models of complex systems: simulation is usually required Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.3 Outline • Discuss some well-known models • Not developpqgy,ment of queueing theory, for this see other class! • We will deal with • General characteristics of queues • Meanings and relationships of important performance measures • Estimation of mean measures of performance • Effect of varying input parameters • Mathematical solutions of some basic queueing models Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.4 Characteristics of Queueing Systems Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.5 Characteristics of Queueing Systems • Key elements of queueing systems • Customer: refers to anything that arrives at a facility and requires service, e.g., people, machines, trucks, emails, packets, frames. • Server: refers to any resource that provides the requested service, e.g., repairpersons, machines, runways at airport, host, switch, router, disk drive, algorithm. System Customers Server Reception desk People Receptionist Hospital Patients Nurses Airpor t Airp lanes Runway Production line Cases Case-packer Road network Cars Traffic light Grocery Shoppers Checkout station Computer Jobs CPU, disk, CD NtNetwor k PktPackets RtRouter Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.6 Calling Population • Calling population: the population of potential customers, may be assumed to be finite or infinite. • Finite population model: if arrival rate depends on the number of customers being served and waiting, e.g., model of one corporate jet, if it is being repaired, the repair arrival rate becomes zero. n n-1 • IfiitInfinite populltiation modldel: if arriva l rate is not afftdffected by the number of customers being served and waiting, e.g., systems with large population of potential customers. ∞ Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.7 System Capacity • System Capacity: a limit on the number of customers that may be in the waiting line or system. • Limited capacity, e.g., an automatic car wash only has room for 10 cars to wait in line to enter the mechanism. • If system is full no customers are accepted anymore Waiting line Server • Unlimited capacity, e.g., concert ticket sales with no limit on the number of people allowed to wait to purchase tickets. Waiting line Server Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.8 Arrival Process • For infinite-population models: • In terms of interarrival times of successive customers. • Arrival types: • Random arrivals: interarrival times usually characterized by a probability distribution. • Most important model: Poisson arrival process (with rate λ), where a time represents the interarrival time between customer n-1 and customer n, and is exppyonentially distributed (with mean 1/λ). • Scheduled arrivals: interarrival times can be constant or constant plus or minus a small random amount to represent early or late arrivals. • Example: patients to a physician or scheduled airline flight arrivals to an airport • At least one customer is assumed to always be present, so the server is never idle, e.g., sufficient raw material for a machine. Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.9 Arrival Process • For finite-population models: • Customer is ppgending when the customer is outside the qqgueueing system, e.g., machine-repair problem: a machine is “pending” when it is operating, it becomes “not pending” the instant it demands service from the repairman. • Runtime of a customer is the length of time from departure from the queueing system until that customer’s next arrival to the qq,g,ueue, e.g., machine-repppair problem , machines are customers and a runtime is time to failure (TTF). (i) (i) (i) • Let A1 , A2 , … be the successive runtimes of customer i, and S1 , (i) S2 be the corresppgonding successive s ystem times: Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.10 Queue Behavior and Queue Discipline • Queue behavior: the actions of customers while in a queue waiting for service to begin, for example: • Balk: leave when they see that the line is too long • Renege: leave after being in the line when its moving too slowly • Jockey: move from one line to a shorter line • Queue discipline: the logical ordering of customers in a queue that determines which customer is chosen for service when a server becomes free, for example: • Firs t-in-firs t-out (FIFO) • Last-in-first-out (LIFO) • Service in random order (()SIRO) • Shortest processing time first (SPT) • Service according to priority (PR) Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.11 Service Times and Service Mechanism • Service times of successive arrivals are denoted by S1, S2, S3. • May be constant or random. • {S1, S2, S3, …} is usually characterized as a sequence of independent and identically distributed (IID) random variables, e.g., • Exponential, Weibull, Gamma, Lognormal, and Truncated normal distribution. • A queueing system consists of a number of service centers and interconnected queues. • Each service center consists of some number of servers (c) working in parallel, upon getting to the head of the line, a customer takes the 1st available server. Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.12 Queuing System: Example 1 • Example: consider a discount warehouse where customers may • serve themselves before paying at the cashier (service center 1) or • served by a clerk (service center 2) Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.13 Queuing System: Example 1 • Wait for one of the three clerks: • Batch service (a server serving several customers simultaneously), or customer requires several servers simultaneously. Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.14 Queuing System: Example 1 Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.15 Queuing System: Example 2 • Candy production line • Three machines seppyarated by buffers • Buffers have capacity of 1000 candies Assumption:Allways sufficient supply of raw material. Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.16 Queueing Notation The Kendall Notation Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.17 Queueing Notation: Kendall Notation • A notation system for parallel server queues: A/B/c/N/K • A represents the interarrival-time distribution • B represents the service-time distribution • c represents the number of parallel servers • N represents the system capacity • K represents the size ooagpopuaof the calling population • N, K are usually dropped, if they are infinity • Common symbols for A and B • M Markov,,p exponential distribution • D Constant, deterministic • Ek Erlang distribution of order k • H Hyperexponential distribution • G General, arbitrary • Examples • M/M/1/∞/∞ same as M/M/1: Single-server with unlimited capacity and call- popula tion. In terarr iva l and service times are exponentia lly dis tr ibu te d • G/G/1/5/5: Single-server with capacity 5 and call-population 5. • M/M/5/20/1500/FIFO: Five parallel server with capacity 20, call-population 1500, and service discipline FIFO Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.18 Queueing Notation • General performance measures of queueing systems: • Pn steady-state probability of having n customers in system • Pn(t) probability of n customers in system at time t • λ arrival rate • λe effective arrival rate • μ service rate of one server • ρ server utilization • An interarrival time between customers n-1 and n • Sn service time of the n-th arriving customer • Wn total time spent in system by the n-th customer Q • Wn total time spent in the waiting line by customer n • L(t) the number of customers in system at time t • LQ(t) the number of customers in queue at time t • L long-run time-average number of customers in system • LQ long-run time-average number of customers in queue • W long-run average time spent in system per customer • wQ long-run average time spent in queue per customer Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.19 Long-run Measures of Performance of Queueing Systems Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.20 Long-run Measures of Performance of Queueing Systems • Primary long-run measures of performance are • L long-run time-average number of customers in system • LQ long-run time-average number of customers in queue • W long-run average time spent in system per customer • wQ long-run average time spent in queue per customer • ρ server utilization • Other measures of interest are • Long-run proportion of customers who are delayed longer than t0 time units • Long-run proportion of customers turned away because of capacity constraints • Long-run proportion of time the waiting line contains more than k0 customers Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.21 Long-run Measures of Performance of Queueing Systems • Goal of this section • Majjpor measures of performance for a general G/G/c/N/K queueing system • How these measures can be estimated from simulation runs • Two types of estimators • Sample average • Time-integrated sample average Prof. Dr. Mesut Güneş ▪ Ch. 8 Queueing Models 8.22 Time-Average Number in System L Number of customers in the system Time Prof.
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