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##### Queuing Networks

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**Queuing Networks**Jean-Yves Le Boudec 1**Contents**• The Class of Multi-Class Product Form Networks • The Elements of a Product-Form Network • The Product-Form Theorem • Computational Aspects • What this tells us 2**1. Networks of Queues are Important but May Be Tough to**Analyze • Queuing networks are frequently used models • The stability issue may, in general, be a hard one • Necessary condition for stability (Natural Condition) server utilization < 1 at every queue 3**Instability Examples**• Poisson arrivals ; jobs go through stations 1,2,1,2,1 thenleave • A job arrives as type 1, thenbecomes 2, then 3 etc • Exponential, independent service times withmeanmi • Priorityscheduling • Station 1 : 5 > 3 >1 • Station 2: 2 > 4 • Q: Whatis the naturalstability condition ? • A: λ (m1 + m3+ m5) < 1 λ (m2 + m4) < 1 4**λ = 1m1 = m3 = m4 = 0.1 m2 = m5 = 0.6**• Utilization factors • Station 1: 0.8 • Station 2: 0.7 • Network is unstable ! • If λ (m1 + … + m5 ) < 1 network is stable; why? 5**Bramson’s Example 1: A Simple FIFO Network**• Poisson arrivals; jobs go through stations A, B,B…,B, A then leave • Exponential, independent service times • Steps 2 and last: mean is L • Other steps: mean is S • Q: What is the natural stability condition ? • A: λ ( L + S ) < 1λ ( (J-1)S + L ) < 1 • Bramson showed: may be unstable whereas natural stability condition holds**Bramson’s Example 2A FIFO Network with Arbitrarily Small**Utilization Factor • m queues • 2 types of customers • λ = 0.5 each type • routing as shown, … = 7 visits • FIFO • Exponential service times, with mean as shown S S L S S L S S L S S L • Utilization factor atevery station ≤ 4 λS • Network isunstable for S ≤ 0.01L≤ S8m = floor(-2 (log L )/L) 7**Take Home Message**• The natural stability condition is necessary but may not be sufficient • We will see a class of networks where this never happens 8**2. Elements of a Product Form Network**• Customers have a class attribute • Customers visit stations according to Markov Routing • External arrivals, if any, are Poisson 2 Stations Class = step, J+3 classes Can youreduce the number of classes ? 9**Chains**• Customers can switch class, but remain in the same chain ν 10**Chains may be open or closed**• Open chain = with Poisson arrivals. Customers must eventually leave • Closed chain: no arrival, no departure; number of customers is constant • Closed network has only closed chains • Open network has only open chains • Mixed network may have both 11**3 Stations**4 classes 1 open chain 1 closedchain ν 12**Bramson’s Example 2A FIFO Network with Arbitrarily Small**Utilization Factor S S L S S L S S L S S L 2 Stations Many classes 2 open chains Network is open 13**Visit Rates**14**2 Stations**5 classes 1 chain Network is open Visit ratesθ11 = θ13 =θ15 =θ22 =θ24 =λ θsc = 0 otherwise 15**ν**16**Constraints on Stations**• Stations must belong to a restricted catalog of stations • We first see a few examples, then give the complete catalog • Two categories: Insensitive (= Kelly-Whittle) and MSCCC • Example of Category 1 (insensitive station): Global Processor Sharing • One server • Rate of server is shared equally among all customers present • Service requirements for customers of class c are drawn iid from a distribution which depends on the class (and the station) • Example of Category 1 (insensitive station): Delay • Infinite number of servers • Service requirements for customers of class c are drawn iid from a distribution which depends on the class (and the station) • No queuing, service time = service requirement = residence time 17**Example of Category 2 (MSCCC station): FIFO with B servers**• B servers • FIFO queueing • Service requirements for customers of class c are drawn iid from an exponential distribution, independent of the class (but may depend on the station) • Example of Category 2 (MSCCC station): MSCCC with B servers • B servers • FIFO queueing with constraints At most one customer of each class is allowed in service • Service requirements for customers of class c are drawn iid from an exponential distribution, independent of the class (but may depend on the station) 18**A**• Say which network satisfies the hypotheses for product form B (FIFO, Exp) C (Prio, Exp) 19**A station of Category 1 is any station that satisfies the**Kelly-Whittle property • Examples: Global or per-class PS, Global or per-class LCFSPR, Delay 20**Stations of Category 2 must have Exponential, class**independent service requirements 21**3. The Product Form Theorem**• Stationary distrib of numbers of customers has product formEach term depends only on the station 23**Stability**• Stability depends only on every station in isolation being stable • When service rates are constant, this is the natural condition 25**Product form and independence**• In an open network • Product form => independence of stations in stationary regime • No longer true in a closed or mixed network 28**Phase Type Distributions**• Product form theorem requires service times to be • Either exponential (category 2 stations) i.e MSCCC including FIFO) • Or Phase type (category 1 stations) • Phase type distributions can approximate any distribution (for the topology of weak convergence) • Stationary Distribution depends only on mean service time(Insensitivity of category 1) • Therefore, it is reasonable to assume that the product form theorem applies if we replace a phase type distribution by any distribution (even heavy tailed) • Was done formally in some cases [8] • Take home message: • Stations of category 1 may have any service time distribution, class dependent • Stations of category 2 must have exponential distrib, class independent 30**4. Computational AspectsStation Function**• The station function, used in the Product Form theorem, is the stationary distribution of the station in isolation 31**Different Stations may have same station equivalent service**rate • FIFO single server, global PS and global LCFSPR with class independent mean service time have same station functions • Check this • Therefore they have the same equivalent service rate and have the same effect in a network as long as we are interested in the distribution of numbers of customers • Hence mean response times are the same • But distributions of response times may differ • Compare PS to FIFO 38**Algorithms for Mixed Networks**• Open networks: all stations are independent; solve one station in isolation • Mixed Networks: suppress open chains (suppression theorem) • Closed networks: the problem is computing the normalizing constant; • Many methods exist, optimized for different types of very large networks • Convolution algorithms: fairly general, applies to tricky cases (MSCCC), requires storing normalizing constant (large) 39**Example**• N = nb customers at GateK = total population • Product Form theorem: μ ν 42**Algorithms for Mixed Networks**• Open networks: all stations are independent; solve one station in isolation • Mixed Networks: suppress open chains (suppression theorem) • Closed networks: the problem is computing the normalizing constant; • Many methods exist, optimized for different types of very large networks • Convolution algorithms: fairly general, applies to tricky cases (MSCCC), requires storing normalizing constant (large) • Mean Value Analysis does not require computing the normalizing constant, but does not apply (yet ?) to all cases 44**The Arrival Theorem and Mean Value Analysis (MVA) version 1**45**The Arrival Theorem and Mean Value Analysis (MVA) version 1**• MVA version 1 uses the arrival theorem in a closed network where all stations are • FIFO or Delay • or equivalent • Based on 3 equations and iteration on population: • Mean response time for a class c customer at a FIFO station (arrival theorem): • Little’s formula: • Total number of customers gives : 46**MVA Version 2**• Applies to more general networks; • Uses the decomposition and complement network theorems 49**is equivalent to:**where the service rate μ*(n4) is the throughput of 50