(a) Define an $M / M / \infty$ queue and write without proof its stationary distribution. State Burke's theorem for an $M / M / \infty$ queue.

(b) Let $X$ be an $M / M / \infty$ queue with arrival rate $\lambda$ and service rate $\mu$ started from the stationary distribution. For each $t$, denote by $A_{1}(t)$ the last time before $t$ that a customer departed the queue and $A_{2}(t)$ the first time after $t$ that a customer departed the queue. If there is no arrival before time $t$, then we set $A_{1}(t)=0$. What is the limit as $t \rightarrow \infty$ of $\mathbb{E}\left[A_{2}(t)-A_{1}(t)\right]$ ? Explain.

(c) Consider a system of $N$ queues serving a finite number $K$ of customers in the following way: at station $1 \leqslant i \leqslant N$, customers are served immediately and the service times are independent exponentially distributed with parameter $\mu_{i}$; after service, each customer goes to station $j$ with probability $p_{i j}>0$. We assume here that the system is closed, i.e., $\sum_{j} p_{i j}=1$ for all $1 \leqslant i \leqslant N$.

Let $S=\left\{\left(n_{1}, \ldots, n_{N}\right): n_{i} \in \mathbb{N}, \sum_{i=1}^{N} n_{i}=K\right\}$ be the state space of the Markov chain. Write down its $Q$-matrix. Also write down the $Q$-matrix $R$ corresponding to the position in the network of one customer (that is, when $K=1$ ). Show that there is a unique distribution $\left(\lambda_{i}\right)_{1 \leqslant i \leqslant N}$ such that $\lambda R=0$. Show that

$$\pi(n)=C_{N} \prod_{i=1}^{N} \frac{\lambda_{i}^{n_{i}}}{n_{i} !}, \quad n=\left(n_{1}, \ldots, n_{N}\right) \in S$$

defines an invariant measure for the chain. Are the queue lengths independent at equilibrium?