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Later we present the underlying generalized concepts and strategies of optimal hard constraint management. , 2008). It basically relies on automated profilingbased design-time analysis of run-time situations of the entire system from the cost perspective. It then classifies the run-time situations into a few frequently occurring dominant cases/clusters and a backup scenario (as illustrated in Figure 8). Clustering into system scenarios limits the possible dynamic behaviors within a scenario and thus enables simple form of future look-ahead and tighter bounds on uncertainties, which is sufficient in some systems.

But its performance advantage can bring us the extra slack that we need. As we know that most likely that extra slack is not used, we do not end up using this energy hungry mode eventually and hence no associated energy penalty. The CTM_ipLCO_UBR_OW5 row in Figure 11 shows the resultant schedule in the presence of mode M0 which allows running task T4 in mode M3 without endangering the deadline of T5 in the unlikely event of T4 turning out to be at its maximum load. It is possible that modes like M0 normally do not exist, as it is not essential to support the worst-case load and may involve other dangerous side-effects as discussed further.

ILLUSTRATIVE EXAMPLE: FINEGRAIN TASK MANAGEMENT IN LOW-POWER REAL-TIME SYSTEMS In this section we exemplify the time-deceptive nonlinearities and illustrate the proactive control concepts in the context of dynamic task management problem. In cost-conscious embedded 9 Systematic Design Principles for Cost-Effective Hard Constraint Management Figure 10. Systems of interest: run-time control of highly dynamic nonlinear systems with conflicting constraints and costs systems, the hardware platform is composed of multiple heterogeneous resources/processors with knobs that allow run-time trade-offs between various metrics such as computational power (say, operations per second) and energy efficiency.

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