In this work, we investigate the potential utility of parallelization for meeting real-time constraints and minimizing energy. We consider malleable Gang scheduling of implicit-deadline sporadic tasks upon multiprocessors. We first show the non-necessity of dynamic voltage frequency regarding optimality of our scheduling problem. We adapt the canonical schedule for DVFS multiprocessor platforms and propose a polynomial-time optimal processor frequency-selection algorithm. We evaluate the performance of our algorithm via simulations using parameters obtained from a hardware testbed implementation. Our algorithm has up to a 60 watt decrease in power consumption over the optimal non-parallel approach.
Very little research has addressed both real-time parallelization and power-consumption issues @cite_2 @cite_0 . Furthermore, some basic fundamental questions on the potential utility of parallelization for meeting real-time constraints and minimizing energy have not been addressed at all in the literature.
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This paper studies the important interaction between parallelization and energy consumption in a parallelizable application. Given the ratio of serial and parallel portion in an application and the number of processors, we first derive the optimal frequencies allocated to the serial and parallel regions in the application to minimize the total energy consumption, while the execution time is preserved (i.e., speedup = 1). We show that dynamic energy improvement due to parallelization has a function rising faster with the increasing number of processors than the speed improvement function given by the well-known Amdahl's Law. Furthermore, we determine the conditions under which one can obtain both energy and speed improvement, as well as the amount of improvement. The formulas we obtain capture the fundamental relationship between parallelization, speedup, and energy consumption and can be directly utilized in energy aware processor resource management. Our results form a basis for several interesting research directions in the area of power and energy aware parallel processing. While much work has addressed energy-efficient scheduling for sequential tasks where each task can run on only one processor at a time, little work has been done for parallel tasks where an individual task can be executed by multiple processors simultaneously. In this paper, we develop energy minimizing algorithms for parallel task systems with timing guarantees. For parallel tasks executed by a fixed number of processors, we first propose several heuristic algorithms based on level-packing for task scheduling, and then present a polynomial-time complexity energy minimizing algorithm which is optimal for any given level-packed task schedule. For parallel tasks that can run on a variable number of processors, we propose another polynomial-time complexity algorithm to determine the number of processors executing each task, task schedule and frequency assignment. To the best of our knowledge, this is the first work that addresses energy-efficient scheduling for parallel real-time tasks. Our simulation result shows that the proposed approach can significantly reduce the system energy consumption.