CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence

doi:10.1109/TCBB.2022.3207509

	CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence
	Cao, Kun 1; Cui, Yangguang 2; Li, Liying 3; Zhou, Junlong 3,4; Hu, Shiyan 5
	2024-07-01
发表期刊	IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
ISSN	1545-5963
卷号	21 期号:4 页码:521-533
摘要	In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especially in the area of digital healthcare. In digital healthcare, medical edge devices empowered by CPU-GPU (Graphics Processing Unit) cooperative multiprocessor system-on-chips (MPSoCs) have a great potential in processing and managing the massive amounts of health-related data. However, most of the existing works on CPU-GPU cooperative MPSoCs cannot maintain a high-precision workload estimation since they simply leverage the worst-case execution cycles to pessimistically predict the workload of digital healthcare applications. Besides, they neglect the personalized requirements of individual healthcare applications and the lifetime reliability demands of heterogeneous CPU-GPU cores. As a result, the normal functions of medical edge devices and the quality-of-services (QoS) of digital healthcare applications are likely to suffer from underlying failures and degradation. In this paper, we explore CPU-GPU cooperative QoS optimization of personalized digital healthcare applications running on reliability guaranteed edge devices with the help of machine learning and swarm intelligence techniques. We first develop two novel predictors: one is a machine learning based predictor for application workload estimation, and the other is a feature-driven predictor for application QoS estimation. We then incorporate the two predictors into a swarm intelligent application scheduling scheme upon the cooperative dual-population evolutionary algorithm (c-DPEA) to find optimal application mapping and partitioning settings. Experimental results show that our solution not only augments the average QoS of whole digital healthcare applications by 15.7%, but also balances the QoS of individual digital healthcare applications by 64.3%.
关键词	Electronic healthcare Quality of service Graphics processing units Central Processing Unit Reliability Machine learning Estimation CPU-GPU MPSoCs machine learning lifetime reliability personalized digital healthcare swarm intelligence
DOI	10.1109/TCBB.2022.3207509
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62102164] ; National Natural Science Foundation of China[62172224] ; Natural Science Foundation of Jiangsu Province[BK20220138] ; China Postdoctoral Science Foundation[2021T140272] ; China Postdoctoral Science Foundation[2021T140327] ; China Postdoctoral Science Foundation[2021M691240] ; China Postdoctoral Science Foundation[2021M680068] ; Science and Technology Project of Guangzhou[202201010573] ; Fundamental Research Funds for the Central Universities[30922010318] ; Fundamental Research Funds for the Central Universities[21621025] ; Postdoctoral Science Foundation of Jiangsu Province[2021K066A] ; Open Research Fund of the State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences[CARCHA202105] ; Future Network Scientific Research Fund Project[FNSRFP-2021-YB-6]
WOS研究方向	Biochemistry & Molecular Biology ; Computer Science ; Mathematics
WOS类目	Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:001290429100039
出版者	IEEE COMPUTER SOC
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39634
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, Junlong
作者单位	1.Jinan Univ, Coll Informat Sci & Technol, Guangzhou 510632, Peoples R China 2.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200062, Peoples R China 3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 5.Univ Southampton, Sch Elect & Comp Sci, Southampton SO17 1BJ, England
推荐引用方式 GB/T 7714	Cao, Kun,Cui, Yangguang,Li, Liying,et al. CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2024,21(4):521-533.
APA	Cao, Kun,Cui, Yangguang,Li, Liying,Zhou, Junlong,&Hu, Shiyan.(2024).CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,21(4),521-533.
MLA	Cao, Kun,et al."CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 21.4(2024):521-533.