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Energy optimization of security-sensitive mixed-criticality applications for distributed real-time systems

机译:分布式实时系统中对安全敏感的混合关键性应用程序的能量优化

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摘要

Existing studies on mixed-criticality systems are usually safety-oriented, which seriously ignore the security and energy related requirements. In this paper we are interested in the design of security-sensitive mixed-criticality real-time systems. We first establish the system model to capture security-critical applications in mixed-criticality systems. Higher security-criticality protection always results in significant time and energy overhead in mixed-criticality systems. Thus, this paper proposes a system-level design framework for energy optimization of security-sensitive mixed-criticality system with hard real-time constraints. Since the time complexity of finding optimal solutions grows exponentially as problem size grows, a GA (Genetic Algorithm) based on efficient heuristic algorithm is devised to address the system-level optimization problem. Extensive experiments and a real-life case study have been conducted to show the efficiency of the proposed technique, which can obtain balanced minimal energy consumption while satisfying strict security and timing constraints. The proposed approach can save up to 28.9% energy consumption compared with other three candidates.
机译:现有的关于混合临界系统的研究通常以安全性为导向,严重忽视了安全性和能源相关的要求。在本文中,我们对安全敏感的混合临界实时系统的设计感兴趣。我们首先建立系统模型来捕获混合关键系统中的关键安全应用程序。更高的安全性-关键性保护始终会在混合关键性系统中导致大量的时间和能源开销。因此,本文提出了一个具有实时严格约束的安全敏感的混合关键系统能量优化的系统级设计框架。由于寻找最佳解决方案的时间复杂度随着问题规模的增长而呈指数增长,因此设计了一种基于高效启发式算法的GA(遗传算法)来解决系统级优化问题。已经进行了广泛的实验和实际案例研究,以证明所提出技术的效率,该技术可以在满足严格的安全性和时序约束的情况下获得平衡的最小能耗。与其他三个候选方案相比,该方案可节省多达28.9%的能耗。

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