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President Obama stated that to achieve his vision, I will encourage the deployment of the most modern communications infrastructure In turn, that infrastructure can be used by government and business to reduce the costs of health care, help solve our energy crisis, create new jobs, and fuel our economic growth And an Obama administration will ensure America remains competitive in the global economy [6] Smart data and smart data strategy are strategic contributions toward this aim Emphasis here is on enabling mechanism and technology investments by government Although still unexplained is how performance will be improved and certainly how explicit measurable outcomes will be de ned Enterprise board members, shareholders, and government constituents (citizens) must demand more from executives Accountability and credibility should be measured by saying not only what will be accomplished, but how it will be accomplished Answering how-to question is the truth detector for senior management in commercial and government enterprise Figure 1 considers the power and use of these simple terms: Inputs, Controls, Outputs Mechanisms This technique can be employed to de ne all processes when processes are three to six activities Each activity can be broken down into subactivities if needed in de ning very complex processes Each of these terms will be de ned here, although suf ce it to say that a simple technique is needed o de ne and explain complex enterprise performance The DOD employed this technique to model the defense enterprise, which serves as a source of best practices in this regard Application of such an approach can be employed to model parts of an enterprise or all of the enterprise whereby having a. Barcode In VB.NET Using Barcode encoder for .NET .Related: Generate Barcode RDLC , Generate Barcode Word SDK, .NET Winforms Barcode Generating

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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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