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We need to know the most likely values for the uncertainties to optimally guide the slack management. Mochocki et al. (2007) recommends the use of the average as likely values. This is again not suitable for highly dynamic contexts. We advocate, instead, the use of good predictors at run-time to predict the likely load of the tasks at hand given the context. The estimates are used to guide the slack allocation and thus to optimize the knob settings for the expected reality, in contrast to the statistical optimization and optimizing for the worst-/average-/typical-case.

The task load is as expected). This results in insufficient slack for the future task to ensure the deadline with the desirable mode and hence forced to switch to costlier mode (T4 and T5 in noCPF_UBR_OW5 row in Figure 11). Propagating and evaluating the effect of mode decisions of earlier tasks on mode decisions of future tasks to avoid sub-optimal future is the key - we call this play-forward mechanism. This can be achieved using explicit play-forward constraints: every task should finish before its deadline with the intended mode even when it requires upper-bound slack but all the previous tasks consume no more than their respective expected slack.

Increase knob range) in the system and to manage the associated constraints (such as temperature and lifetime) at the run-time. Controlled and efficient use of CTMs helps in closing the gap between retrospectively optimal and practically achievable solutions. Using CTMs optimally is very intriguing and challenging as the decisions of the optimization directly influence their duration of availability, but typically with certain time lag and usually with some amount of uncertainty. , in under-designed systems, or artificially created due to over slack consumption in the past) while simultaneously ensuring that such usage will not violate any other constraints.

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