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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 2 + 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.

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the node ni V, Vcur to be scheduled next on processor procS (ni ) = P, it holds / that tdr,Sinsert,cur (ni , P) tdr,Send,cur (ni , P) tdr,S (ni , P), with the above argumentation With the end technique, ni is scheduled at the end of the last node already scheduled on P But this cannot be later than in S, because the nodes on P in Send,cur are the same nodes that are executed before ni on P in S, due to the schedule order of the nodes, and, according to the assumption, no node starts later than in S With the insertion technique, ni is scheduled in the worst case (ie, there is no idle period between two nodes already scheduled on P complying with Eq (61)) also at the end of the last node already scheduled on P Thus, ts,Sinsert,cur (ni , P) ts,Send,cur (ni , P) ts,S (ni , P) for node ni By induction this is true for all nodes of the schedule, in particular, the last node, which proves the theorem Theorem 51 of Section 51 establishes that an optimal schedule is de ned by the processor allocation and the nodes execution order List scheduling with the end technique can construct an optimal schedule from these inputs While this result is included in Theorem 61, it also establishes that the insertion technique might improve a given nonoptimal schedule Rescheduling a given schedule with list scheduling and the insertion technique, using the processor allocation and the node order of the original schedule, might improve the schedule length In particular, schedules produced with the end technique might be improved What rst sounds like a contradiction to Theorem 51, after all it states that the end technique is optimal given a processor allocation and the nodes execution order, becomes clear when one realizes that rescheduling with the insertion technique can only reduce the length of a schedule by reordering the nodes Inserting a node in an earlier slot changes the node order on the corresponding processor Complexity Regarding the complexity of list scheduling with the insertion technique, the second part of the corresponding Algorithm 9 is examined Determining the data ready time remains O(PE) for all nodes on all processors (see Section 51) What changes is the time complexity of the start time calculation In the worst case, it must be checked for every consecutive node pair on a processor, if the time period between them is large enough to accommodate the node to be scheduled At the time a node is scheduled, there are at most O(V) nodes scheduled on all processors So if the start time is determined for every processor as in the typical case of start time minimization this amortizes to O(max(V, P)), which is of course O(V), because it is meaningless to schedule on more processors than the task graph has nodes The start time is calculated for every node; thus, the nal complexity of the second part of simple list scheduling with start time minimization is O(V2 + PE) For comparison, with the end technique it is O(P(V + E)) (see Section 51) The insertion technique is employed in arious scheduling heuristics, for example, ISH (insertion scheduling heuristic) by Kruatrachue [105], (E)CPFD ((economical) critical path fast duplication) by Ahmad and Kwok [4], BSA (bubble scheduling and allocation) by Kwok and Ahmad [114], and MD (mobility directed) by Wu and Gajski [207].

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