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MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning Research Articles

Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900121

Abstract: With the growing amount of information and data, s have been widely used in many applications, including the Google File System, Amazon S3, Hadoop Distributed File System, and Ceph, in which load balancing of plays an important role in improving the input/output performance of the entire system. Unbalanced load on the server leads to a serious bottleneck problem for system performance. However, most existing load balancing strategies, which are based on subtree segmentation or hashing, lack good dynamics and adaptability. In this study, we propose a (MDLB) mechanism based on (RL). We learn that the algorithm and our RL-based strategy consist of three modules, i.e., the policy selection network, load balancing network, and parameter update network. Experimental results show that the proposed MDLB algorithm can adjust the load dynamically according to the performance of the servers, and that it has good adaptability in the case of sudden change of data volume.

Keywords： 面向对象的存储系统；元数据；动态负载均衡；强化学习；Q_learning    

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Minimax Q-learning design for H∞ control of linear discrete-time systems Research Articles

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 438-451 doi: 10.1631/FITEE.2000446

Abstract: Different from existing -learning methods, a novel gradient-based policy improvement scheme is proposedconverges to the saddle solution under initially admissible control policies and an appropriate positive learning

Keywords： H∞ control     Zero-sum dynamic game     Reinforcement learning     Adaptive dynamic programming     Minimax Q-learning    

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Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking Article

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Engineering 2021, Volume 7, Issue 9,   Pages 1248-1261 doi: 10.1016/j.eng.2021.04.027

Abstract: A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is oftenUnlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requires

Keywords： Interface tracking     Object tracking     Occlusion     Reinforcement learning     Uniform manifold approximation    

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Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving Research Articles

Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.1900637

Abstract: suffer from increased complexity with large-scale inter-coupled rules, so many researchers are exploring learning-based

Keywords： 自主驾驶；自动驾驶车辆；强化学习；监督学习    

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Behavioral control task supervisor with memory based on reinforcement learning for human–multi-robot Research Article

Jie HUANG, Zhibin MO, Zhenyi ZHANG, Yutao CHEN,yutao.chen@fzu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1174-1188 doi: 10.1631/FITEE.2100280

Abstract: The proposed RLTS with memory provides a detailed integration of the deep Q-network (DQN) and long short-term

Keywords： multi-robot coordination systems     Null-space-based behavioral control     Task supervisor     Reinforcement learning    

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Engineering DNA Materials for Sustainable Data Storage Using a DNA Movable-Type System Article

Zi-Yi Gong, Li-Fu Song, Guang-Sheng Pei, Yu-Fei Dong, Bing-Zhi Li, Ying-Jin Yuan

Engineering 2023, Volume 29, Issue 10,   Pages 130-136 doi: 10.1016/j.eng.2022.05.023

Abstract:

DNA molecules are green materials with great potential for high-density and long-term data storage. However, the current data-writing process of DNA data storage via DNA synthesis suffers from high costs and the production of hazards, limiting its practical applications. Here, we developed a DNA movable-type storage system that can utilize DNA fragments pre-produced by cell factories for data writing. In this system, these pre-generated DNA fragments, referred to herein as "DNA movable types," are used as basic writing units in a repetitive way. The process of data writing is achieved by the rapid assembly of these DNA movable types, thereby avoiding the costly and environmentally hazardous process of de novo DNA synthesis. With this system, we successfully encoded 24 bytes of digital information in DNA and read it back accurately by means of high-throughput sequencing and decoding, thereby demonstrating the feasibility of this system. Through its repetitive usage and biological assembly of DNA movable-type fragments, this system exhibits excellent potential for writing cost reduction, opening up a novel route toward an economical and sustainable digital data-storage technology.

Keywords： 合成生物学     DNA信息存储     DNA活字存储系统     经济性DNA数据存储    

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A home energy management approach using decoupling value and policy in reinforcement learning

熊珞琳,唐漾,刘臣胜,毛帅,孟科,董朝阳,钱锋

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1261-1272 doi: 10.1631/FITEE.2200667

Abstract: a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcement learning

Keywords： Home energy system     Electric vehicle     Reinforcement learning     Generalization    

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Decentralized multi-agent reinforcement learning with networked agents: recent advances Review Article

Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 802-814 doi: 10.1631/FITEE.1900661

Abstract: Multi-agent (MARL) has long been a significant research topic in both machine learning and control systems

Keywords： 强化学习；多智能体系统；网络系统；一致性优化；分布式优化；博弈论    

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Cooperative channel assignment for VANETs based on multiagent reinforcement learning Research Articles

Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 1047-1058 doi: 10.1631/FITEE.1900308

Abstract: In addition, neural networks are constructed as nonlinear Q-function approximators, which facilitates

Keywords： Vehicular ad-hoc networks     Reinforcement learning     Dynamic channel assignment     Multichannel    

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ONFS: a hierarchical hybrid file system based on memory, SSD, andHDDfor high performance computers Article

Xin LIU, Yu-tong LU, Jie YU, Peng-fei WANG, Jie-ting WU, Ying LU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 1940-1971 doi: 10.1631/FITEE.1700626

Abstract: With supercomputers developing towards exascale, the number of compute cores increases dramatically, making more complex and larger-scale applications possible. The input/output (I/O) requirements of large-scale applications, workflow applications, and their checkpointing include substantial bandwidth and an extremely low latency, posing a serious challenge to high performance computing (HPC) storage systems. Current hard disk drive (HDD) based underlying storage systems are becoming more and more incompetent to meet the requirements of next-generation exascale supercomputers. To rise to the challenge, we propose a hierarchical hybrid storage system, on-line and near-line file system (ONFS). It leverages dynamic random access memory (DRAM) and solid state drive (SSD) in compute nodes, and HDD in storage servers to build a three-level storage system in a unified namespace. It supports portable operating system interface (POSIX) semantics, and provides high bandwidth, low latency, and huge storage capacity. In this paper, we present the technical details on distributed metadata management, the strategy of memory borrow and return, data consistency, parallel access control, and mechanisms guiding downward and upward migration in ONFS. We implement an ONFS prototype on the TH-1A supercomputer, and conduct experiments to test its I/O performance and scalability. The results show that the bandwidths of single-thread and multi-thread ‘read’/‘write’ are 6-fold and 5-fold better than HDD-based Lustre, respectively. The I/O bandwidth of data-intensive applications in ONFS can be 6.35 times that in Lustre.

Keywords： High performance computing     Hierarchical hybrid storage system     Distributed metadata management     Data migration    

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Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents Research Article

Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1032-1042 doi: 10.1631/FITEE.2100594

Abstract: We have designed three coaching strategies (fixed crash rate, curriculum learning, and adaptive crashdemonstrate the efficacy of the adaptive strategy compared with the fixed crash rate strategy and curriculum learning

Keywords： Multi-agent system     Reinforcement learning     Unexpected crashed agents    

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Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT Article

Qian Wang, Siguang Chen, Meng Wu

Engineering 2023, Volume 31, Issue 12,   Pages 127-138 doi: 10.1016/j.eng.2022.10.014

Abstract: Furthermore, a blockchain incentive and contribution co-aware federated deep reinforcement learning algorithm

Keywords： Computation offloading     Caching     Incentive     Blockchain     Federated deep reinforcement learning    

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Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous Article

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Engineering 2023, Volume 21, Issue 2,   Pages 75-91 doi: 10.1016/j.eng.2022.05.017

Abstract:

Due to its limited intelligence and abilities, machine learning is currentlytraining loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learningIn this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developedvalidated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning

Keywords： Human-in-the-loop AI     Deep reinforcement learning     Human guidance     Autonomous driving    

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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning Research Article

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1541-1556 doi: 10.1631/FITEE.2300084

Abstract: As one of the most fundamental topics in (RL), is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions, we focus on the policy and propose an efficient selective sampling approach to improve by modeling the internal hierarchy of the environment. Specifically, we first employ in the policy to generate an action candidate set. Then we introduce a clustering buffer for modeling the internal hierarchy, which consists of on-policy data, off-policy data, and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage. In this way, our approach is able to take advantage of the supervision information in the expert demonstration data. Experiments on six different continuous locomotion environments demonstrate superior performance and faster convergence of selective sampling. In particular, on the LGSVL task, our method can reduce the number of convergence steps by 46.7% and the convergence time by 28.5%. Furthermore, our code is open-source for reproducibility. The code is available at https://github.com/Shihwin/SelectiveSampling.

Keywords： Reinforcement learning     Sample efficiency     Sampling process     Clustering methods     Autonomous driving    

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Multi-agent differential game based cooperative synchronization control using a data-driven method Research Article

Yu SHI, Yongzhao HUA, Jianglong YU, Xiwang DONG, Zhang REN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1043-1056 doi: 10.1631/FITEE.2200001

Abstract: Finally, the effectiveness of the learning process and the stability of are illustrated in simulation

Keywords： Multi-agent system     Differential game     Synchronization control     Data-driven     Reinforcement learning    

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Title Author Date Type Operation

MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning

Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com

Journal Article

Minimax Q-learning design for H∞ control of linear discrete-time systems

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Journal Article

Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Journal Article

Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving

Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Journal Article

Behavioral control task supervisor with memory based on reinforcement learning for human–multi-robot

Jie HUANG, Zhibin MO, Zhenyi ZHANG, Yutao CHEN,yutao.chen@fzu.edu.cn

Journal Article

Engineering DNA Materials for Sustainable Data Storage Using a DNA Movable-Type System

Zi-Yi Gong, Li-Fu Song, Guang-Sheng Pei, Yu-Fei Dong, Bing-Zhi Li, Ying-Jin Yuan

Journal Article

A home energy management approach using decoupling value and policy in reinforcement learning

熊珞琳,唐漾,刘臣胜,毛帅,孟科,董朝阳,钱锋

Journal Article

Decentralized multi-agent reinforcement learning with networked agents: recent advances

Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu

Journal Article

Cooperative channel assignment for VANETs based on multiagent reinforcement learning

Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Journal Article

ONFS: a hierarchical hybrid file system based on memory, SSD, andHDDfor high performance computers

Xin LIU, Yu-tong LU, Jie YU, Peng-fei WANG, Jie-ting WU, Ying LU

Journal Article

Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents

Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI

Journal Article

Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT

Qian Wang, Siguang Chen, Meng Wu

Journal Article

Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Journal Article

Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Journal Article

Multi-agent differential game based cooperative synchronization control using a data-driven method

Yu SHI, Yongzhao HUA, Jianglong YU, Xiwang DONG, Zhang REN

Journal Article