X. Hu, T. Lei, Y.-C. Gong, and X.-H. Zhou (PRC)
RWCET estimation, dynamic scheduling, and power management
Modern real-time applications often employ dynamic scheduling to offer better system qualities, e.g., lower power consumption, etc., while satisfying time constraints. Their runtime decisions are mostly made based on the tasks' remaining worst-case execution time (RWCET). This paper first develop a novel analytic model to formally express and solve the runtime RWCET estimation problem, then introduce a series of experiential methods to implement the formal solution by balancing the estimation precisions against the runtime overheads. By this means, it can be realized with a lower analytic complexity to transform source codes to an adaptive program, which can provide the scheduler at runtime with its own RWCET estimation. Experimental results show that the proposed method is effective and, expectably, can be used as a foundation for other dynamic scheduling techniques, e.g., adaptive power management, etc.
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