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-Tasks running on embedded systems are often associated with deadlines. While it is important to complete tasks before their associated deadlines, performance and energy consumption also play important roles in many usages of embedded systems. To address these issues, we explore the use of Dynamic Voltage and Frequency Scaling (DVFS), a standard feature available on many modern processors for embedded systems. Previous studies often focus on frequency assignment for energy savings and meeting definite task deadlines. In this paper, we present a heuristic algorithm based on convex optimization techniques to compute energy-efficient processor frequencies for soft real-time tasks. Our novel approach provides performance improvements by allowing definitions of multiple target deadlines for each task. We simulate two versions of our algorithm in MATLAB and evaluate their performance and efficiency. The experimental results show that our strategy leverages performance and energy savings, and can be customized to suit practical applications.

Abstract-Applications written by end-user programmers are hardly energy-optimized by these programmers. The end users of such applications thus suffer significant energy issues. In this paper, we propose CrowdAdaptor, a novel approach toward locating energy-efficient configurations to execute the applications hosted in virtual machines on handheld devices. CrowdAdaptor innovatively makes use of the development artifacts (test cases) and the very large installation base of the same application to distribute the test executions and performance data collection of the whole test suites against many different virtual machine configurations among these installation bases. It synthesizes these data, continuously discovers better energy-efficient configurations, and makes them available to all the installations of the same applications. We report a multi-subject case study on the ability of the framework to discover energy-efficient configurations in three power models. The results show that CrowdAdaptor can achieve up to 50% of energy savings based on a conservative linear power model.