Stochastic Petri Nets. Jonatan Lindén. Modelling SPN GSPN. Performance measures. Almost none of the theory. December 8, 2010

Transcription

1 Stochastic Almost none of the theory December 8, 2010

2 Outline 1 2

3 Introduction A Petri net (PN) is something like a generalized automata. A Stochastic Petri Net () a stochastic extension to Petri nets, for quantitative analysis. More expressive than queueing networks and easier to model a wider range of systems. Underlying mathematical model : stochastic processes.

4 Basic example A PN consists of: Places

5 Basic example A PN consists of: Places Arcs

6 Basic example A PN consists of: Places Arcs Transitions

7 Basic example A PN consists of: Places Arcs Transitions Tokens

8 Basic example A PN consists of: Places Arcs Transitions Tokens A transition fires

9 Basic example A PN consists of: Places Arcs Transitions Tokens A transition fires Tokens are moved!

10 Transitions in s All transitions t i are associated with a rate λ i, possibly marking dependent. A transition becomes enabled when none of its input places is empty. When a transition t i becomes enabled, an exponentially distributed random variate with parameter λ i is generated. When a transitions fires, one token is removed from each input place, and tokens are put into all of its output places. Each allocation of tokens to the places in the is called a marking.

11 Notation M i - some marking, M 0 - Initial marking. t i - Transition i. p i - Place i. m i - Number of tokens in place i. E(M i ) - transitions enabled by marking M i. G(t i ) - the set of markings in which t i is enabled.

12 Processor working in private memory Example p 1 A place can represent some logical state of the system that we want to model. Initial marking M 0 = 2, 0, 1, 0 Processor requesting shared resource t 1 m 1 λ p 2 p 3 t 2 θ Processor using shared resource, exclusively p 4 t 3 µ

13 Example p 1 A place can represent some logical state of the system that we want to model. Initial marking M 0 = 2, 0, 1, 0 t 1 m 1 λ p 2 p 3 M 1 = 1, 1, 1, 0 t 2 θ p 4 t 3 µ

14 Example p 1 A place can represent some logical state of the system that we want to model. Initial marking M 0 = 2, 0, 1, 0 t 1 m 1 λ p 2 p 3 M 1 = 1, 1, 1, 0 t 2 θ M 2 = 1, 0, 0, 1 p 4 t 3 µ

15 Example p 1 A place can represent some logical state of the system that we want to model. Initial marking M 0 = 2, 0, 1, 0 t 1 m 1 λ p 2 p 3 M 1 = 1, 1, 1, 0 t 2 θ M 2 = 1, 0, 0, 1 M 0 = 2, 0, 1, 0 p 4 t 3 µ

16 How to solve it - two approaches to get performance : Analytical - generate a corresponding CTMC and solve it. Simulation - When the state space is too large, or when a more advanced model is necessary (general firing distributions).

17 s and CTMCs Fact: s are isomorphic to CTMCs, k-bounded s are isomorphic to finite CTMCs.

18 s and CTMCs Fact: s are isomorphic to CTMCs, k-bounded s are isomorphic to finite CTMCs. Reachability set RS = RS(M 0 ) - the markings reachable starting from the initial marking M 0. The state space of the CTMC (the marking process) corresponds to the RS.

19 s and CTMCs Fact: s are isomorphic to CTMCs, k-bounded s are isomorphic to finite CTMCs. Reachability set RS = RS(M 0 ) - the markings reachable starting from the initial marking M 0. The state space of the CTMC (the marking process) corresponds to the RS. Home state: M RS(M 0 ), M RS(M ) A k-bounded is said to be ergodic if M 0 is a home state. ergodic ergodic CTMC guaranteed steady state solution

20 Obtaining the CTMC Convert the to a CTMC, where every marking of the can be associated with a state in the corresponding Markov chain. We get the generator matrix Q by: { t q ij = k E j (M i ) λ k(m i ) i j t k E(M i ) λ k(m i ) i = j NB. Mean sojourn time in any marking (why?): ST i = t k E j (M i ) λ k (M i ) 1

21 Obtaining the CTMC Reminder : each marking is written as M = m 1, m 2, m 3, m 4. 1, 0, 0, 1 p 1 m 1 λ µ θ λ p 2 p 3 2, 0, 1, 0 1, 1, 1, 0 2λ µ 0, 1, 0, 1 t2 θ λ 0, 2, 1, 0 θ p 4 t 3 µ

22 We extend the original model with: Immediate transitions transitions that fire immediately, before any timed transitions. (higher priority) Inhibitor arcs. (will be skipped) Motivation: can be used to model purely logical events or events whose duration is negligible. the state space (RS) of the resulting CTMC is reduced (when using an immediate transition instead of a timed transition). We increase modeling power.

23 , ctd. We will call markings m i G(t j ), where t j is immediate, for vanishing states, and we differ those from the tangible states (i.e., when the transition is timed). The marking process will no longer be a CTMC, but a Semi-Markov process. (The times between transitions have two different distributions) It is straightforward to find an EMC, from which it is possible to derive a reduced EMC, with a smaller state space.

24 Switching distributions Whenever two immediate transitions are enabled at the same time, we require some rule that specifies which transition should fire first (possibly marking dependent). P(t 2 )=P(t 3 )=0.5 t 2 t 3

25 Switching distributions However, there are some subtleties when defining the switching distributions of the immediate transitions: P(t 2 )=P(t 3 )=0.5 t 2 t 3 t 4 t 5

26 Switching distributions The probability measure is only well-defined if P(t 4 ) = P(t 5 ) = 0, which is probably not what we want. P(t 2 )=P(t 3 )=0.5 t 2 t 3 t 4 t 5

27 Switching distributions Hence, knowledge of the marking process is necessary when defining switches. P(t 2 )=P(t 3 )=0.5 t 2 t 3 t 4 t 5

28 Switching distributions Even worse! Solution: define weights instead (as Bengt said). P(t 2 )=P(t 3 )=0.5 t 2 t 3 t 4 t 5

29 Embedded MC EMC: Observe a stochastic process {X (t), t T } only at the moment the state changes. Given a, the obtained process will be a DTMC, with transition matrix u ij = { t k E j (M i ) λ k/ t k E(M i ) λ k i j 0 i = j From the routing probabilities that we get from the EMC and the switching probabilities, the average sojourn time in each state of the marking process, we can find the steady-state probability of the marking process.

30 Example, revisited. µ 1, 0, 0, 1 θ λ m 1 λ 2, 0, 1, 0 1, 1, 1, 0 2λ µ 0, 1, 0, 1 λ θ 0, 2, 1, 0 µ 2, 0, 1, 0 1, 0, 0, 1 2λ µ λ 0, 1, 0, 1 µ

31 Probability of a certain place/event can be obtained by summing up the probabilites of the corresponding markings: P(p i ) = M i S(p i ) where π i is the steady-state distribution of m i in the underlying CTMC. The pmf of the number of tokens in a place p i gives us the average number of tokens in that place. π i

32 Example, ctd. Assume M 0 = 2, 0, 1, 0, M 1 = 1, 0, 0, 1 and M 2 = 0, 1, 0, 1. We solve the system, and get P(M 0 ), P(M 1 ), P(M 2 ). We interpret the places in some way: P(waiting) = P(m 2 > 0) = P(M 2 ) Average number of tokens in m 2, E(m 2 ) = 0P(M 0 ) + 0P(M 1 ) + P(M 2 ) t2 p 1 m 1 λ p 2 p 3 p 4 t 3 µ

33 Throughput and delay The throughput, or the mean number of firings of a transition t j : f j = M i G(t j ) λ j (M i )π i Little s law gives the average delay of a token passing through a subnet of the model: E(T ) = E(N) E(γ)

34 Throughput and delay We solve the system, and get P(M 0 ), P(M 1 ), P(M 2 ). Rate into the subnet (throughput of transition t 1 ): E(γ) = 2λP(M 0 ) + λp(m 1 ) E(N) = E(m 4 ) + E(m 2 ) = P(M 1 ) + P(M 2 ) + P(M 2 ) Average delay of one token: E(T ) = E(N) E(γ) = P(M 1) + 2P(M 2 ) 2λP(M 0 ) + λp(m 1 ) t2 t 3 p 1 m 1 λ p 2 p 3 p 4 µ

35 Relation with PN All structural results obtained from the underlying PN are valid for the as well. Completely deterministic PN: Adding inhibitor arcs gives them the same modeling power as turing machines (they can mimic any turing machine) : Adding inhibitor arcs does not increase the modeling power. (Haas). In fact, any with one or more inhibitor arc can be converted into an equivalent without inhibitor arcs. (immediate transitions are used)