Question:

A company is named with your last name (e.g. Miao). It has a finance department which requires a service to be high available. You will need to

1) complete the requirement analysis (with reasonable assumptions);

2) design the solution for the department;

3) implement the solution; and

4) adjust the design and implementation at session 8 to meet the new requirements.

The solution needs to include

a domain controller,

a storage server,

a failover cluster with at least two nodes,

an application server running the service,

a scenario of live migration of the application server from one cluster node to another, and a scenario of failover when the cluster node hosting the application server was down.

Example C.2Weak law of large numbers (convergence in pr. of the sample mean). Let {X} i = l, 2.. \"k be a sequence of uncorrelated scalar random variables with common mean u and uniformly bounded, not necessarily identical variances E[(X.- [02] s c 0 i=1 as k > no, where the second equality follows by the uncorrelatedness. So, X} is n1.s. convergent to 11.. By the implications in Figure Cl, is is also pr. convergent, which is the weak law of large numbers (eg, Laha and Rohatgi, 1979, pp. 6769). A special case of this law is that the sample mean of a sequence of independent, identically distributed (i.i.d.) random variables with nite variance converges in pr. to the true mean of the sequence. In fact, the sample mean for this special case will converge in the stronger as. sense (the strong law of large numbers), but the proof techniques for the strong law are more difcult than those for the weak law above- El For each n, let X,, be a binomial random variable with n trials and probability of success p. (a) Use the Weak Law of Large Numbers to show that is a consistent estimator of p. (b) Explain why it follows from (a) that (1 _ is a consistent estimator of p(1 - p) and that Y'n is a consistent estimator of p(1 - p) .(Keith Chugg) Draw connection lines in Fig. 1 that link the assumptions and mode of stochastic convergence (on the left) associated with each of the convergence theorems (on the right). independent, 3'3\"" identically distributed 1M\") uncorrelated Almost Sure Convergence Sure Convergence Convergence in Distribution Convergence in Pmbabilily Mean Square Same Convergence Figure 1: Connect the related concepts by drawing lines. 511mg Law of Large Numbers Weak Law of Large Numbers Central Limit Theorem 10 points Let X1, X2, . .. bei.i.d. U[0, 1] random variables. Define X(n) = max { X1, . .., Xn}. (a) Show that P (X()