CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence
文献类型：期刊论文
| 作者 | Cao, Kun1; Cui, Yangguang2; Li, Liying3; Zhou, Junlong3,4; Hu, Shiyan5 |
| 刊名 | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 21期号:4页码:521-533 |
| 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 |
| ISSN号 | 1545-5963 |
| DOI | 10.1109/TCBB.2022.3207509 |
| 英文摘要 | 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%. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:001290429100039 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39633]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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