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Energy Optimized Task Mapping for Reliable and Real-Time Networked Systems

ACM TRANSACTIONS ON SENSOR NETWORKS 2023年第4期19卷 76-76页

作者： Mo, Lei Zhou, Qi Kritikakou, Angeliki Cao, Xianghui Southeast Univ Sch Automat Sipailou Rd 2 Nanjing 210096 Jiangsu Peoples R China Southeast Univ Sch Cyber Sci & Engn Sipailou Rd 2 Nanjing 210096 Jiangsu Peoples R China Univ Rennes CNRS INRIA IRISA 263 Ave Gen Leclerc F-35042 Rennes Bretagne France

Energy efficiency, real-time response, and data transmission reliability are important objectives during networked systems design. This paper aims to develop an efficient task mapping scheme to balance these important but conflicting objectives. To achieve this goal, tasks are triplicated to enhance reliability and mapped on the wireless nodes of the networked systems with Dynamic Voltage and Frequency Scaling (DVFS) capabilities to reduce energy consumption while still meeting real-time constraints. Our contributions include the mathematical formulation of this task mapping problem as mixed-integer programming that balances node energy consumption, enhancing data reliability, under real-time and energy constraints. Compared with the State-of-the-Art (SoA), a joint-design problem is considered in this paper, whereDVFS, task triplication, task allocation, and task scheduling are optimized concurrently. To find the optimal solution, the original problem is linearized, and a decomposition-based method is proposed. The optimality of the proposed method is proved rigorously. Furthermore, a heuristic based on the greedy algorithm is designed to reduce the computation time. The proposed methods are evaluated and compared through a series of simulations. The results show that the proposed triplication-based task mapping method on average achieves 24.84% runtime reduction and 28.62% energy saving compared to the SoA methods.

关键词： Networked systems task triplication and mapping DVFS problem linearization and decomposition

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Mapping imprecise computation tasks on cyber-physical systems

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2019年第6期12卷 1726-1740页

作者： Mo, Lei Kritikakou, Angeliki Univ Rennes INRIA CNRS IRISA F-35042 Rennes France

By allocating a set of tasks onto a set of nodes and adjusting the execution time of tasks, task mapping is an efficient approach to realize distributed computing. Cyber-Physical Systems (CPS), as a particular case of distributed systems, raise new challenges in task mapping, because of the heterogeneity and other properties traditionally associated with Wireless Sensor and Actuator Networks (WSAN), including shared sensing, acting and real-time computing. In addition, many of the real-time tasks of CPS can be executed in an imprecise way. Such systems accept an approximate result as long as the baseline Quality-of-Service (QoS) is satisfied and they can execute more computations to yield better results, if more system resources is available. These systems are typically considered under the Imprecise Computation (IC) model, achieving a better tradeoff between QoS and limited system resources. However, determining a QoS-aware mapping of these real-time IC-tasks onto the nodes of a CPS creates a set of interesting problems. In this paper, we firstly propose a mathematical model to capture the dependency, energy and real-time constraints of IC-tasks, as well as the sensing, acting, and routing in the CPS. The problem is formulated as a Mixed-Integer Non-Linear Programming (MINLP) due to the complex nature of the problem. Secondly, to efficiently solve this problem, we provide a linearization method that results in a Mixed-Integer Linear Programming (MILP) formulation of our original problem. Finally, we decompose the transformed problem into a task allocation subproblem and a task adjustment subproblem, and, then, we find the optimal solution based on subproblem iteration. Through the simulations, we demonstrate the effectiveness of the proposed method.

关键词： Cyber-physical systems Task mapping Imprecise computation problem linearization and decomposition

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