Real-Time Imprecise Computation Tasks Mapping for DVFS-Enabled Networked Systems

• Published in: IEEE internet of things journal Vol. 8; no. 10; pp. 8246 - 8258
• Main Authors: Mo, Lei, Kritikakou, Angeliki, Sentieys, Olivier, Cao, Xianghui
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automatic Control Engineering; Computational modeling; Computer Science; Computing time; Constraints; Data processing; Energy consumption; Heuristic methods; Imprecise computation (IC); Mapping; networked systems; Nodes; Optimization; Quality of service; Quality of Service (QoS); Real time; Real-time systems; Resource management; Routing; Task analysis; task mapping; Quality-of-Service; Imprecise Computation; Networked Systems; Task Mapping
• ISSN: 2327-4662; 2372-2541; 2327-4662
• DOI: 10.1109/JIOT.2020.3044910

Networked systems are useful for a wide range of applications, many of which require distributed and collaborative data processing to satisfy real-time requirements. On one hand, networked systems are usually resource constrained, mainly regarding the energy supply of the nodes and their computation and communication abilities. On the other hand, many real-time applications can be executed in an imprecise way, where an approximate result is acceptable as long as the baseline Quality of Service (QoS) is satisfied. Such applications can be modeled through imprecise computation (IC) tasks. To achieve a better tradeoff between QoS and limited system resources, while meeting application requirements, the IC-tasks must be efficiently mapped to the system nodes. To tackle this problem, we first construct an IC-task mapping problem that aims to maximize system QoS subject to real-time and energy constraints. Dynamic voltage and frequency scaling (DVFS) and multipath routing are explored to further enhance real-time performance and reduce energy consumption. Second, based on the problem structure, we propose an optimal approach to perform IC-task mapping and prove its optimality. Furthermore, to enhance the scalability of the proposed approach, we present a heuristic IC-task mapping method with low computation time. Finally, the simulation results demonstrate the effectiveness of the proposed methods in terms of the solution quality and the computation time.