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Queuing Theory

Queuing Theory. Basic properties, Markovian models, Networks of queues, General service time distributions, Finite source models, Multiserver queues. Kendall’s Notation for Queuing Systems. A/B/X/Y/Z: A = interarrival time distribution B = service time distribution

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Queuing Theory

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  1. Queuing Theory Basic properties, Markovian models, Networks of queues, General service time distributions, Finite source models, Multiserver queues Chapter 8

  2. Kendall’s Notation for Queuing Systems A/B/X/Y/Z: A = interarrival time distribution B = service time distribution G = general (i.e., not specified); M = Markovian (exponential); D = deterministic X = number of parallel service channels Y = limit on system pop. (in queue + in service); default is Z = queue discipline; default is FCFS (first come first served) Others are LCFS, random, priority Chapter 8

  3. Other Notation Random variables: X(t) = Number of customers in the system at time t S = Service timeof an arbitrary customer W* = Amount of time an arbitrary customer spends in the system WQ* = Amount of time an arbitrary customer spends waiting for service Often we are most interested in the averages or expectations: Chapter 8

  4. Little’s Formula is the time spent in the system by the nth customer. Assume these system times are uniformly finite and let be the customer average time spent in the system. Also, let be the time average number of jobs in the system. Then under very general conditions, where la is the arrival rate. This is usually written L = laW. The mean number of customers in the system is proportional to the mean time in the system! Chapter 8

  5. Heuristic Proof of Little’s Formula A(t) B(t) = cum. area between curves D(t) T Two ways to compute Mean time in system in (0,T): Mean number in system in (0,T): Then Chapter 8

  6. Other Little’s Formulae In queue: In service: and their implications… Expected number of busy servers Expected number of idle servers Single server utilization Single server prob. of empty system Chapter 8

  7. Observation Times an = Proportion of arriving customers that find n in the system dn = Proportion of departing customers that leave behind n in the system If customers arrive one at a time and are served one at a time then But these proportions may not match the limiting probability of having n in the system (long run proportion of time that n are in the system) However, if arrivals follow a Poisson process then This is known as PASTA (Poisson Arrivals See Time Averages) Chapter 8

  8. M/M/1 Model Single server, Poisson arrivals (rate ), exponential service times (rate ) CTMC (birth-death) model: Chapter 8

  9. M/M/1 Steady-State Probabilities Level-crossing equations: Solve for Pn in terms of P0 Then use the facts that and substitute to get Chapter 8

  10. M/M/1 Performance Measures Steady-state expected number of customers in the system Mean time in system Mean time in queue Mean number in queue Chapter 8

  11. M/M/1 Performance Measures Distribution of time in system (FCFS) Exponential with parameter (1-) Reversibility: Departure process is Poisson with rate  Chapter 8

  12. Finite Capacity: M/M/1/N Model Single server, exponential service times (rate ) Poisson arrivals (rate ) as long as there are  N in the system CTMC (birth-death) model: Chapter 8

  13. M/M/1/N Steady-State Probabilities Level-crossing equations: Solve for Pn in terms of P0 Then use the facts that to get Chapter 8

  14. M/M/1/N Performance Measures (In the unlikely event that l = m, for n = 1,…, N, Steady-state expected number of customers in the system, L, has a “messy” closed form Mean time in system but here, la is the rate of arrival into the system Chapter 8

  15. Tandem Queue l If arrivals to the first server follow a Poisson process and service times are exponential, then arrivals to the second server also follow a Poisson process and the two queues behave as independent M/M/1 systems: P{n customers at server 1 and m customers at server 2} = m1 m2 Chapter 8

  16. Open Network of Queues • k servers, customers arrive at server k from outside the system according to a Poisson process with rate rk, independent of the other servers • Upon completing service at server i, customer goes to server j with probability Pij, where For j = 1,…, k, the total arrival rate to server j is The number of customers at each server is independent and If lj < mj for all j, then P{n customers at server j} that is, each acts like an independent M/M/1 queue! Chapter 8

  17. Closed Queuing Network • m customers move among k servers • Upon completing service at server i, customer goes to server j with probability Pij, where • Let p be the stationary probabilities for the Markov chain describing the sequence of servers visited by a customer: Then the probability distribution of the number at each server is Chapter 8

  18. CQN Performance Computation of the normalizing constant Cm to get the stationary distribution can be lengthy; but may be mostly interested in the throughput where lm(j) is the arrival rate to (and departure rate from) j. Arrival Theorem: In the CQN with m customers, the system as seen by arrivals to server j has the same distribution as the whole system when it contains only m-1 customers. This leads to mean value analysis to find lm(j) along with Wm(j) = the average time a customer spends at server j, and Lm(j) = the average number of customers at server j. Chapter 8

  19. Mean Value Analysis Solve iteratively: throughput Begin with Chapter 8

  20. M/G/1Best combination of tractability & usefulness • Assumption of Poisson arrivals may be reasonable based on Poisson approximation to binomial distribution • many potential customers decide independently about arriving (arrival = “success”), • each has small probability of arriving in any particular time interval • Probability of arrival in a small interval is approximately proportional to the length of the interval – no bulk arrivals • Amount of time since last arrival gives no indication of amount of time until the next arrival (exponential – memoryless) Chapter 8

  21. M/G/1Best combination of tractability & usefulness • Exponential distribution is frequently a bad model for service times • memorylessness • large probability of very short service times with occasional very long service times • May not want to use one of the “standard” distributions for service times, either • in a real situation, collect data on service times and fit an empirical distribution • Distributions of number of customers in the system and waiting time depend on service time distribution to be specified Chapter 8

  22. M/G/1Best combination of tractability & usefulness • Assumption of Poisson arrivals may be reasonable based on Poisson approximation to binomial distribution • many potential customers decide independently about arriving (arrival = “success”), • each has small probability of arriving in any particular time interval • Distributions of number of customers in the system and waiting time depend on service time distribution Chapter 8

  23. M/G/1 PerformanceHow many customers? How much time? S is the length of an arbitrary service time (random variable) l is the arrival rate of customers; define  = E[S] and assume it is < 1. Expected values can be found from generalizing Little’s formula from # customers in the system to amount of work in the system: An arriving customer brings S time units of work: The time average amount of work in the system (V) = l * the customer average amount of work remaining in the system Chapter 8

  24. Work Content WQ* is the (random variable) waiting time in queue Expected amount of work per customer is If a customer’s service time is independent of own wait in queue, get average work in system Work remaining S Begin service Depart Enter Chapter 8

  25. Mean waiting time WQ = Customer mean waiting time = average work in the system when a customer arrives From PASTA, WQ = V. Therefore, (Pollaczek-Khintchine formula) And the other measures of performance are: Chapter 8

  26. Priority Queues Different types of customers may differ in importance. • Type i customers arrive according to a Poisson process with rate li and require service times with distribution Gi, i = 1, 2. • Type 1 customers have (nonpreemptive) priority: • service does not begin on a type 2 customer if there is a type 1 customer waiting. • If a type 1 customer arrives during a type 2 service, the service is continued to completion. What is the average wait in queue of a type i customer, Chapter 8

  27. Two customer types w/o priority M/G/1 model with Average work in system is If the server is not allowed to be idle when the system is not empty, this quantity is the same for the system with priority. Chapter 8

  28. Two customer types with priority Let Vi be the average amount of type i work in the system Now focus on a type 1 customer. Waiting time = amt. of type 1 work in system + amt. of type 2 work in service when this customer arrives, so in queue in service Chapter 8

  29. Two customer types with priority But a type 2 customer has to wait for everyone ahead, plus any type 1 customers who arrive during the type 2 wait, so Chapter 8

  30. M/M/k Model k identical machines in parallel, Poisson arrivals (rate ), exponential service times (rate ) CTMC (birth-death) model: Chapter 8

  31. M/M/k Steady-State Probabilities Level-crossing equations: Define =/k, solve for Pn in terms of P0 Then use the facts that to get r is the utilization of each server Chapter 8

  32. M/M/k Performance Measures Steady-state expected number of customers in the system Mean flow time Expected waiting time Expected number in the queue (k is expected number of busy servers) Chapter 8

  33. Erlang Loss System M/M/k/k system: k servers and a capacity of k: an arrival who finds all servers busy does not enter the system (is lost) Above is called Erlang’s loss formula, and it holds for M/G/k/k as well, if Chapter 8

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