Energy-Aware Design of Stochastic Applications With Statistical Deadline and Reliability Guarantees

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 38; no. 8; pp. 1413 - 1426
• Main Authors: Jiang, Wei, Pan, Xiong, Jiang, Ke, Wen, Liang, Dong, Qi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation; Approximation algorithm; Complexity; Design optimization; Dynamic programming; Embedded systems; energy; Energy conservation; Energy consumption; Energy costs; Energy efficiency; Energy management; Knapsack problem; Mathematical analysis; Multiple choice; Polynomials; Real time; Real-time systems; reliability; Reliability engineering; Statistical analysis; statistical guarantee; Stochastic processes; stochastic real-time application; Task analysis
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2018.2846652

Energy efficiency, reliability, and real-time are three key requirements of mission-critical embedded systems. Existing approaches over emphasize the worst case design of real-time embedded systems, which will lead to serious waste of resources. In this paper, we aim at the energy-efficient design of soft real-time and reliable applications on uniprocessor embedded systems. We consider soft real-time tasks with stochastic execution durations regarding certain distributions. Thereby, we provide real-time guarantee with probability consideration. We utilize dynamic voltage and frequency scaling (DVFS) for saving energy, and also take into account the impact of DVFS on reliability. Our objective is to minimize the expected energy consumption of the system subject to statistical reliability and deadline constraints. The design optimization problem is a typical multidimensional multiple-choice knapsack problem, which is NP-hard. We first propose a dynamic programming-based optimal algorithm to solve the problem. To reduce the time complexity, we then develop a (<inline-formula> <tex-math notation="LaTeX">1+{\beta } </tex-math></inline-formula>)-approximation algorithm based on a binary search approach, where <inline-formula> <tex-math notation="LaTeX">{\beta } </tex-math></inline-formula> is the approximating factor. The approximation algorithm can obtain the near-optimal solution with at most (<inline-formula> <tex-math notation="LaTeX">1{+\beta } </tex-math></inline-formula>) times of optimal energy cost under given real-time and reliability constraints and has fully polynomial time complexity. Extensive experiments and a real-life synthetic application are conducted to evaluate the performance of the proposed techniques. Compared with existing approaches, the approximation approach can save much energy with low time overhead while guaranteeing the statistical deadline and reliability constraints.