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Vn (N ) = Cf + Vn 1 (N + 1), Vn (N + 1) = 0 + Vn 1 (1) We have applied the standard value-iteration algorithm to the umerical data from Table 641 For each stationary policy the associated Markov chain {Xn } is aperiodic Taking V0 (i) = 0 for all i and the accuracy number = 10 3 , the algorithm is stopped after n = 28 iterations with the stationary policy R(n) = (0, 0, 0, 1, 2, 2) together with the lower and upper lower bounds mn = 04336 and Mn = 04340 The average cost of policy R(n) is estimated by 1 (mn + Mn ) = 2 04338 and this cost cannot deviate more than 01% from the theoretically minimal average cost In fact policy R(n) is optimal as we know from previous results obtained by policy iteration To get a feeling of how strongly the required number of iterations depends on , we applied standard value-iteration for = 10 2 and = 10 4 as well For these choices of the accuracy number , standard valueiteration required 21 and 35 iterations respectively Example 661 A nite-capacity queue with deterministic arrivals Consider a single-server queueing system having a nite waiting room for K customers (including any customer in service) The arrival process of customers is deterministic Every D time units a customer arrives A customer nding a full waiting room upon arrival is lost The service times of the customers are independent random variables having an Erlang (r, ) distribution What is the long-run fraction of customers who are lost Taking the constant interarrival time as time unit, the fraction of lost customers can be seen as an average cost per time unit when a cost of 1 is incurred each time an arriving customer nds the waiting room full The queueing process embedded at the arrival epochs can be described by a Markov process by noting that the Erlang (r, ) distributed service time can be seen as the sum of r independent phases each having an exponential distribution with mean 1/ A customer is served by serving its phases one at a time The queueing problem can now be converted into a Markov decision model with a single action in each state The state of the system is observed at the arrival epochs and the set of possible states of the system is given by I = {0, 1, , Kr} State i corresponds to the situation that i uncompleted service phases are present just prior to the arrival of a new customer In each state i there is a single action to be denoted by a = 0 The action a = 0 in state i corresponds to the acceptance of the newly arriving customer when i Kr r and corresponds to the rejection of the customer otherwise The one-step costs ci (a) are given by ci (a) = 0 1 if i Kr r, if i > Kr r.

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723 Scheduling Network Model Sections 721 and 722 analyzed, from the perspective of scheduling, the topologies of communication networks and their routing policies It remains merely to summarize this analysis to establish a network model based on the topology graph (Sinnen and Sousa [172, 179]) De nition 75 (Network Model) A communication network of a parallel system is represented by a topology graph TG = (N, P, D, H, b) (De nition 73) The routing algorithm carried out n the topology graph is that of the represented network (or its closest approximation) Furthermore, routing has the following properties: 1 Static Routing in the network is static; the selected route does not depend on the state of the network If the represented network employs adaptive routing, the default behavior (ie, the behavior without traf c con icts) is reproduced 2 Circuit Switching The communication route between two processors of the network is the same for the entire message 3 Cut-through A message cuts through the communication stations (processor or switch) on its route; that is, all links of the route are utilized at the same time 4 No Routing Delay per Hop No delay is in icted by the transition between links on the communication route Edge scheduling, which is investigated in the next section, employs this model for the representation of the network.NET framework Control to generate, create Data Matrix ECC200 image in Visual Studio .NET pplications. ECC200 In .NET Framework Using Barcode printer for ASP .Related: Create UPC-A VB.NET , Interleaved 2 of 5 Generator Java , Excel Data Matrix Generator

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GICS follows the outline of Algorithm 20, Section 65 Naturally, the chromosome encodes the processor allocation, which is an indirect representation (Section 652) Hence, the construction of the schedule corresponding to each chromosome, which is necessary for the evaluation of its tness, requires the application of a heuristic As already mentioned, this is performed by a list scheduling heuristic under the involvement contention model (see later discussion) Strictly speaking, this means that a two-phase heuristic is applied multiple times in GICS once for each new chromosome To reduce the running time of the evaluation, the node order is determined only once at the beginning of the algorithm, namely, according to their bottom levels Like most GAs, GICS starts with a random initial population, enhanced with a chromosome representing a sequential schedule (all nodes are allocated to a single processor) The pool is completed by one allocation extracted from a schedule produced with a list scheduling heuristic (Section 841) Most other components are fairly standard In fact, the algorithm can even be employed for scheduling under the classic or the contention model, using a modi ed evaluation (ie, scheduling heuristic) Phase 2 List Scheduling From Section 52 it is known that the second phase can be performed with a simple list scheduling algorithm The processor choice of list scheduling is simply a lookup from the given mapping A established in the rst phase Even though list scheduling under the involvement contention model was already studied in Section 841, it must be revisited as there are three alternatives for the scheduling of the edges when the processor allocation is already given (Section 832) Scheduling of the Edges The rst alternative corresponds to direct scheduling, and the procedures for the scheduling of a node and the calculation of the DRT are presented in Algorithms 23 and 24 The scheduling of an edge on the source processor must be modi ed (line 16 in Algorithm 23), since it is no longer necessary to assume the worst case Lines 1 5 (removing local edges) and line 9 (correcting the nish time) can be dropped completely, since the provisional scheduling of the edges is not necessary with the given processor allocation The other two alternatives are only distinguished from this procedure through the place where the edges are scheduled on the links and the destination processor So, for the second alternative, where the edge is scheduled on the links as a leaving edge, lines 8 and 10 of Algorithm 23 move o the for loop on lines 15 17 In the third alternative, the edge is also scheduled on the destination processor within this loop, and the for loop for the entering edges (lines 6 13) is completely dropped Of course, in the determination of the DRT the entering edges do not need to be tentatively scheduled, as the processor allocation is already given Complexity The complexity of list scheduling with a given processor allocation decreases slightly in comparison to list scheduling with start/ nish time minimization It is not necessary to tentatively schedule a node and its edges on every processor Thus,.

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