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(ie di erent selection methods, eg negative or clonal selection) The proposed multi-layered AIS consists of the following layers: the free-antibody layer (F B-Cell layer (B) and the memory layer (M) The set of training patterns, DT , is seen as antigen Each layer has an a nity threshold (aF , aB , aM ), and a death threshold ( F , B , M ) The death threshold is measured against the length of time since a cell was last stimulated within a speci c layer If the death threshold does not exceed this calculated length of time, the cell dies and is removed from the population in the speci c layer The a nity threshold (aF , aB , aM ) determines whether an antigen binds to an entity within a speci c layer The a nity, fa , between an antigen pattern and an entity in a layer is measured using Euclidean distance Algorithm 194 summarizes the multi-layered AIS The di erent parts of the algorithm are discussed next An antigen, zp , rst enters the free-antibody layer, F In the free-antibody layer, the antigen pattern is then presented to nf free-antibodies The number of free-antibody bindings is stored in the variable nb The antigen, zp , then enters the B-Cell layer, B, and is randomly presented to the B-Cells in this layer until it binds to one of the B Cells After binding, the stimulated B-Cell, uk , produces a clone, uk , if the stimulation level exceeds a predetermined stimulation threshold, B The stimulation level is based on the number of free-antibody bindings, nb , as calculated in the free-antibody layer The clone is then mutated as uk and added to the B-Cell layer The stimulated BCell, uk , produces free antibodies that are mutated versions of the original B-Cell The number of free antibodies produced by a B-Cell is de ned in [468] as follows: fF (zp , uk ) = (amax fa (zp , uk )) (192).

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length is 3k (Figure 52(c)) For any C a k with k > 1, k > C can be chosen so that sl(S) = 3k > 3C = C sl(Sopt ) In particular, list scheduling with start time minimization does not even guarantee a schedule length shorter than the sequential time of a task graph Still, numerous experiments have demonstrated that list scheduling produces good schedules for task graphs with low to medium communication (eg, Khan et al [103], Kwok and Ahmad [111], Sinnen and Sousa [177]) 512 With Dynamic Priorities The simple list scheduling outlined in Algorithm 9 establishes the schedule order of the nodes before the actual scheduling process During the node scheduling in the second part, this order remains unaltered, so the node priorities are static To achieve better schedules, it might be bene cial to consider the state of the partial schedule in the schedule order of the nodes Changing the order of the nodes is equivalent to changing their priorities, hence the node priorities are dynamic It remains that the node order must be compatible with the precedence constraints of the task graph, which is obeyed if only free nodes are scheduled A list scheduling algorithm for dynamic node priorities is given in Algorithm 11 Algorithm 11 List Scheduling Dynamic Priorities (G = (V, E, w, c), P) 1: Put source nodes {ni V : pred(ni ) = } into set S 2: while S = do S only contains free nodes 3: Calculate priorities for nodes ni S 4: Choose a node n from S; S S n 5: Choose a processor P for n 6: Schedule n on P 7: S S {ni succ(n) : pred(ni ) Scur } 8: end while Instead of iterating over the node list, Algorithm 11 calculates in each step the priority of all free nodes and only then selects a node and a processor for scheduling After the scheduling of node n, the set S of free nodes is updated with the nodes that became free by the scheduling of n (line 7) Obviously, this approach has higher complexity in general than the simple list scheduling presented in Algorithm 9, because the priority of every node is determined several times Algorithm 11 is a generalization of the simple list scheduling, which becomes apparent by considering line 3 If this line is moved to the beginning of the algorithm and the riorities are calculated for all nodes, not only for the free nodes, the simple list scheduling is obtained This becomes even more distinct in Section 513, where the construction of node lists is explained Choosing Node Processor Pair Some algorithms dynamically compute the priorities of the free nodes by evaluating the start time (see Eq (51)) for every free.

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It s highly likely that you will want to deploy multiple clients at the same time; although it is possible to run 10 deployments at the same time, this means the image is pulled down over the network 10 times, slowing all the deployments as they ght for network bandwidth Windows Server 2008 WDS introduces multicast support, which enables multiple clients to ubscribe to a single transmission of data That means you send the image of the OS over the network once, and up to 1000 clients can download that single transmission, providing a fast deploy time There is little needed to get multicast running By default, WDS is con gured to use its own multicast DHCP scope, but if you have a MADCAP server, then that can be used instead, as discussed via the Network Settings tab of the WDS server properties The clients need to be running the Windows Server 2008 boot image for multicast support, and no other con guration is required If you con gure a client to install Windows Vista Business, and a multicast session exists for that image, it automatically joins the multicast session Nothing else is needed The easiest way to create a new multicast transmission is to right-click on the install image you want to multicast and select Create Multicast Transmission from its context menu You are prompted for a friendly name for the transmission, such as Windows Vista Business MCT, and when you click Next, you are prompted for the multicast type (see Figure 16-45) If you want a multicast to start whenever needed, leave the default setting of Auto-Cast, which starts a transmission as soon as a client needs the image However, you may prefer to use a Scheduled-Cast, which is where clients will see that the multicast transmission exists and will wait for it to start A WDS administrator via the WDS MMC interface can perform the actual starting of the multicast by right-clicking on a Multicast Transmission and selecting Start Or check one or both of the auto-start options, which are based on starting the transmission after you have x number of clients waiting for the image for example, start only after 10 clients are waiting for Windows Vista Business or schedule to start at a certain date and time, which would be useful if you wanted to turn on machines.

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Algorithm 12 Create Node List 1: Assign a priority to ach n V 2: Put source nodes n V : pred(n) = into priority queue Q 3: while Q = do Q only contains free nodes 4: Let n be the node of Q with highest priority; remove n from Q 5: Append n to list L 6: Put {ni succ(n) : pred(ni ) L} into Q 7: end while which costs O(log V) and the determination of the free nodes amortizes to O(E) Removing the node with the highest priority from the heap is O(1) An example for a metric that is not compatible with the precedence order is tl + bl a metric that gives preference to the nodes of the critical path Table 51 displays several node orders of the sample task graph obtained with node level based priority metrics The values of the node levels are given in Table 41 As analyzed in Section 44, calculating these levels has a complexity of O(V + E) For all these metrics the rst part of the simple list scheduling is thus O(V log V + E) (calculating levels: O(V + E) + sorting: O(V log V) or O(V log V + E)) Already the few examples in Table 51 illustrate the large variety of possible node orders In experiments, the relevance of the node order for the schedule length was demonstrated (eg, Adam et al [2], Sinnen and Sousa [177]) Unfortunately, most comparisons of scheduling algorithms (eg, Ahmad et al [6], Gerasoulis and Yang [76], Khan et al [103], Kwok and Ahmad [111], McCreary et al [136]) analyze entire algorithms: that is, the algorithms differ not only in one but many aspects This generally impedes one from drawing conclusions on discrete aspects of the algorithms (eg, on the node order) Still, most algorithms apply orders based on node levels and the bottom level order exhibits good average performance (eg, Adam et al [2], Sinnen and Sousa [177]) Breaking Ties Table 51 also shows examples where the node order is not fully de ned, indicated in the table by parentheses around the respective nodes Incidentally,.

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