Adaptive CPU-budget allocation for soft-real-time applications
Ahmed, Safayet N.
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The focus of this dissertation is adaptive CPU-budget allocation for periodic soft-real-time applications. The presented algorithms are developed in the context of a power-management framework. First, the prediction-based bandwidth scheduler (PBS) is developed. This algorithm is designed to adapt CPU-budget allocations at a faster rate than previous adaptive algorithms. Simulation results are presented to demonstrate that this approach allows for a faster response to under allocations than previous algorithms. A second algorithm is presented called Two-Stage Prediction (TSP) that improves on the PBS algorithm. Specifically, a more sophisticated algorithm is used to predict execution times and a stronger guarantee is provided on the timeliness of jobs. Implementation details and experimental results are presented for both the PBS and TSP algorithms. An abstraction is presented called virtual instruction count (VIC) to allow for more efficient budget allocation in power-managed systems. Power management decisions affect job-execution times. VIC is an abstract measure of computation that allows budget allocations to be made independent of power-management decisions. Implementation details and experimental results are presented for a VIC-based budget mechanism. Finally, a power-management framework is presented called the linear adaptive models based system (LAMbS). LAMbS is designed to minimize power consumption while honoring budget allocations specified in terms of VIC.