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Complexity In comparison to contention aware list scheduling, the time complexity under the involvement contention model does not increase The additional scheduling of the edge on the processors increases the effort to implement the algorithm, but it does not modify its time complexity For example, the complexity of the second part of simple list scheduling (Algorithm 9) with start/ nish time minimization and the end technique is O(P(V + EO(routing))) (see Section 751) Also, the complexity does not increase for the insertion technique (Section 61), even though the individual scheduling of a node and an edge on a processor is more complex In the involvement contention model, there are more objects on the processors, namely, nodes and edges, which must be searched to nd an idle time interval On all processors there are at most O(V + E) nodes and edges; hence, the total complexity for the scheduling of the nodes increases from O(V2 ), under the contention model, to O(V(V + E)) The scheduling of the edges on the processors is O(E(V + E)), compared to O(PE) with the end technique, and on the links it remains O(PE2 O(routing)) Hence, the total complexity of the second part of simple list scheduling with start/ nish time minimization and the insertion technique is O(V2 VE + PE2 O(routing)), that is, O(V2 + PE2 O(routing)), as under the contention model (Section 751) 842 Two-Phase Heuristics The generic form of a two-phase scheduling algorithm was already outlined in Algorithm 13 The rst phase establishes the processor allocation and the second phase constructs the actual schedule with a simple list scheduling heuristic, as described in Section 52 The next paragraphs discuss how such an algorithm is applied under the involvement contention model Phase 1 Processor Allocation The processor allocation can originate from any heuristic or can be extracted from a given schedule For example, a schedule produced under the classic or contention model might serve as the input (see also Section 52) Using the rst two steps of a clustering based heuristic, that is, the clustering itself and the cluster-to-processor mapping (Algorithm 14, Section 53), is very promising, because clustering tries to minimize communication costs, which is even more important under the involvement contention model The clustering itself can be performed under the classic or, as described in Section 753, under the contention model Even the involvement contention model can be used with a straightforward extension of the considerations made in Section 753 Genetic Algorithm In Sinnen [172] and Sinnen et al [180], a genetic algorithm, called GICS (genetic involvement contention scheduling), is proposed for the determination of the processor allocation The simple idea is that the genetic algorithm searches for an ef cient processor allocation, while the actual scheduling is performed with a list scheduling heuristic as discussed later for phase 2.

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 lgorithm 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 to 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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