基于概率性分析的混合关键级系统节能调度算法

doi:10.16180/j.cnki.issn1007-7820.2024.03.012

Abstract

Abstract:

In view of the problem of energy saving of fixed priority tasks in mixed criticality system,a probabilistic analysis based energy-saving scheduling algorithm for mixed criticality system is proposed in this study.The real-time requirement of hybrid critical system makes the system modeling and analysis biased to the worse case.In this type of system, task overlimit is relatively rare, and resource overallocation is easy to occur.In this study, DVFS(Dynamic Voltage Frequency Scaling) technology and mixed criticality system scheduling algorithm are combined to mine idle time, so as to reduce the energy consumption of the system on the premise of ensuring the real-time performance of the system.The proposed algorithm is verified by MCSIMU(Mixed Criticality System Simulation) simulation software. The experimental results show that the energy saving rate of the fixed priority energy-saving scheduling algorithm can reach 45% compared with that of the unused energy-saving scheduling algorithm.

Key words: energy saving scheduling, mixed criticality system, energy consumption, fixed priority, real-time performance, analysis of probability, DVFS technology, MCSIMU simulation

CLC Number:

TP316

MAO Hang, ZHANG Fengdeng, LU Yu, ZHU Jiawei. Energy Saving Scheduling Algorithm of Mixed-Criticality System Based on Probability Analysis[J].Electronic Science and Technology, 2024, 37(3): 91-97.

Figures/Tables 6

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Table 1.

References 18

[1]	张凤登. 分布式实时系统[M]. 北京: 科学出版社, 2014:62-70.
	Zhang Fengdeng. Distributed real-time system[M]. Beijing: Science Press, 2014:62-70.
[2]	王天一, 高博. 分布式实时系统数据分发服务DDS技术研究[J]. 电子科技, 2020, 33(8):40-45.
	Wang Tianyi, Gao Bo. Research on data distribution service of distributed real-time system[J]. Electronic Science and Technology, 2020, 33(8):40-45.
[3]	Vestal S. Preemptive scheduling of multi-criticality systems with varying degrees of execution time assurance[C]. Piscataway: Proceedings of the Twentyeighth IEEE Real-Time Systems Symposium, 2007:551-560.
[4]	Diaz J L, Garcia D F. Stochastic analysis of periodic real-time systems[C]. Austin:Real-Time Systems Symposium, 2002:493-498.
[5]	Diaz J L. Safe extensions to the stochastic analysisof real-time systems[M]. Austin: Technical Report,Departament Ode Informatica,University of Oviedo, 2004:661-667.
[6]	Hyeongboo B, Jinkyu L. Incorporating security constraints into mixed-criticality real-time scheduling[J]. IEICE Transactions on Information & Systems, 2017, 100(9):2068-2080.
[7]	Miura F, Van E. Estimated incubation period for monkeypox cases confirmed in the Netherlands[J]. Eurosurveillance, 2022, 27(24):220-448.
[8]	Chen J J, Kuo C. Energy-efficient scheduling for real- time systems on dynamic voltage scaling platforms[C]. Daegu: International Conference on Embedded and Real-Time Computing Systems and Applications, 2007:735-740.
[9]	Pagani S, Chen J J. Energy efficiency analysis for the single frequency approximation scheme[C]. Berlin:International Conference on Embedded & Real-time Computing Systems & Applications, 2014:801-806.
[10]	Zhu D, Melhem R. The effects of energy managementon reliability in real-time embedded systems[C]. San Jose: Computer Aided Design, 2004:590-596.
[11]	陈刚, 关楠, 吕鸣松, 等. 实时多核嵌入式系统研究综述[J]. 软件学报, 2018, 29(7):14-23.
	Chen Gang, Guan Nan, Lv Mingsong, et al. State-of-the-art survey of real-time multicore system[J]. Journal of Software, 2018, 29(7):14-23.
[12]	Nelson A, Moreira O. Power minimisation for real-time dataflow applications[C]. Oulu: Euromicro Conference on Digital System Design, 2011:377-384.
[13]	Hyeongboo B, Jinkyu L. Incorporating security constraints into mixed-criticality real-time scheduling[J]. IEICE Transactions on Information & Systems, 2017, 100(9):2068-2080.
[14]	Mohan S, Man K Y. Real-time systems security through scheduler constraints[C]. Madrid: Real-Time Systems IEEE, 2014:706-712.
[15]	曹营. 嵌入式系统任务调度机制的研究与实现[D]. 大连: 大连理工大学, 2010:22-50.
	Cao Ying. Research and implementation of task scheduling mechanism based on system[D]. Dalian: Dalian University of Technology, 2010:22-50.
[16]	梅晶. 并行分布式系统中的节能调度策略与算法研究[D]. 长沙: 湖南大学, 2015:37-58.
	Mei Jing. The research of parallel distributed systems energy-aware scheduling strategies and algorithms[D]. Changsha: Hunan University, 2015:37-58.
[17]	蒋小文. 多核实时系统的节能和可靠性优化调度研究[D]. 杭州: 浙江大学, 2018:49-66.
	Jiang Xiaowen. Multiprocessor real-time systems sche-duling for energy and reliability optimization[D]. Hangzhou: Zhejiang University, 2018:49-66.
[18]	Chen G, Huang K, Knoll A. Energy optimization for real-time multiprocessor system-on-chip with optimal DVFS and DPM combination[J]. ACM Transactions on Embedded Computing Systems, 2014, 13(3):1-21.

任务集	P^L^→^H	S_LO
1	0.40	0.90
2	0.41	0.60
3	0.48	0.75
4	0.28	0.70
5	0.45	0.45
6	0.40	0.53
7	0.13	0.57

Energy Saving Scheduling Algorithm of Mixed-Criticality System Based on Probability Analysis

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 18

Related Articles 10

Metrics

Comments

Recommended 0

[1]	WANG Qinghai,YANG Fangyan. Design of Heterogeneous and Low-Power Following Robot System [J]. Electronic Science and Technology, 2022, 35(10): 8-14.
[2]	YU Jiawei,HU Haiyang. Energy Consumption Model of Cyber-Physical System Based on Object-Oriented Fuzzy Petri Net [J]. Electronic Science and Technology, 2021, 34(7): 19-25.
[3]	ZHOU Jia,WANG Ran. Weighted Maximum Network Lifetime Algorithm in Target Coverage [J]. Electronic Science and Technology, 2021, 34(7): 37-42.
[4]	WANG Runling. Adaptive Positions Fusion for Visual Tracking [J]. Electronic Science and Technology, 2019, 32(8): 12-16.
[5]	MAO Chenyue,WU Pengyong. UAV Path Planning Obstacle Avoidance Algorithm Based on Artificial Potential Field Method [J]. Electronic Science and Technology, 2019, 32(7): 65-70.
[6]	TANG Kunpeng,CHEN Qingkui. Design and Implementation of Multistream Hddecoder Based on Cuda [J]. , 2016, 29(4): 71-.
[7]	SHI Feifei,LU Xi,LIU Bin. Simulation and Optimization of Dynamic and Economic Performance for a Pure Electric Commercial Vehicle [J]. , 2016, 29(2): 25-.
[8]	LUO Mingqiang,LIANG Peng. Aluminum Oxide Energy Consumption Computing Method Based on Neural Network [J]. , 2015, 28(3): 38-.
[9]	TUO Shuqing,LIANG Peng. Research on Energy Consumption Monitoring and Prediction System [J]. , 2015, 28(1): 85-.
[10]	CHANG Ling. Influence of the Stepper Motor's Starting Frequency on the Efficiency of Solar Energy Power Generation [J]. , 2012, 25(6): 142-.