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Precise control of page cache for containers

Frontiers of computer Science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN national engineering research center for Big Data technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and flexibility.Resource isolation is a fundamental property of containers.Existing works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource isolation.However,current studies have almost not discussed the isolation problems of page cache which is a key resource for containers.Containers leverage memory cgroup to control page cache usage.Unfortunately,existing policy introduces two major problems in a container-based environment.First,containers can utilize more memory than limited by their cgroup,effectively breaking memory isolation.Second,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized applications.This paper performs an empirical study of these problems and demonstrates the performance impacts on containerized applications.Then we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and precisely.To do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory isolation.And EoD reduces unnecessary page cache evictions to avoid the performance impacts.The evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Type Synthesis of Self-Alignment parallel Ankle Rehabilitation Robot with Suitable Passive Degrees of Freedom

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Chinese Journal of Mechanical engineering 2024年第1期37卷 160-175页

作者： Ya Liu Wenjuan Lu Dabao Fan Weijian Tan Bo Hu Daxing Zeng School of Mechanical engineering Yanshan UniversityQinhuangdao 066004China School of Mechanical engineering Dongguan University of TechnologyDongguan 523808China Dongguan engineering technology research center for parallel Robot Dongguan 523808China The DGUT Innovation center of Robotics and Intelligent Equipment Dongguan 523808China

The current parallel ankle rehabilitation robot(ARR)suffers from the problem of difficult real-time alignment of the human-robot joint center of rotation,which may lead to secondary injuries to the patient.This study investigates type synthesis of a parallel self-alignment ankle rehabilitation robot(PSAARR)based on the kinematic characteristics of ankle joint rotation center drift from the perspective of introducing"suitable passive degrees of freedom(DOF)"with a suitable number and form.First,the self-alignment principle of parallel ARR was proposed by deriving conditions for transforming a human-robot closed chain(HRCC)formed by an ARR and human body into a kinematic suitable constrained system and introducing conditions of"decoupled"and"less limb".Second,the relationship between the self-alignment principle and actuation wrenches(twists)of PSAARR was analyzed with the velocity Jacobian matrix as a"bridge".Subsequently,the type synthesis conditions of PSAARR were proposed.Third,a PSAARR synthesis method was proposed based on the screw theory and type of PSAARR synthesis conducted.Finally,an HRCC kinematic model was established to verify the self-alignment capability of the PSAARR.In this study,93 types of PSAARR limb structures were synthesized and the self-alignment capability of a human-robot joint axis was verified through kinematic analysis,which provides a theoretical basis for the design of such an ARR.

关键词： Ankle rehabilitation robot Self-alignment parallel mechanism Type synthesis Screw theory

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of computer Science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN national engineering research center for Big Data technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory media.Most previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM resource.In this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory hierarchy.Since the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of Optane.This design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane DCPMM.We also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache replacement.Moreover,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM cache.We implement Mocha in an architectural simulator.Experimental results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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Minimal Context-Switching Data Race Detection with Dataflow Tracking

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Journal of computer Science & technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 national engineering research center for Big Data technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded programs.Emerging con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race detector.However,this constraint-based approach has serious limitations on helping programmers analyze and understand data races.First,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the program.Second,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving process.This ad hoc recommendation imposes too many context switches,which complicates the data race analysis.To address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context switches.Specifically,we reduce the false positives by ana-lyzing and tracking the dataflow in the program.By this means,DFTracker thus reduces the unnecessary analysis of false race schedules.We further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data race.Our experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race detec-tor.Therefore,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

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national Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko Department of Mechanical engineering Seoul National University IEN center for Human-Centric Interfaces and engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of Electrical and computer engineering Georgia Institute of Technology Department of Chemical engineering Stanford University Department of Mechanical engineering Ajou University George W.Woodruff School of Mechanical engineering Georgia Institute of Technology Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

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Anti-saturation fault-tolerant adaptive torsional vibration control with fixed-time prescribed performance for rolling mill main drive system

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Journal of Iron and Steel research(International) 2024年第3期31卷 660-669页

作者： Shuang Liu Chen Du Cong Zhang Zhen-hua Bai Key Lab of Industrial computer Control engineering of Hebei Province Yanshan UniversityQinhuangdao 066004HebeiChina national engineering research center for Equipment and technology of Cold Strip Rolling Yanshan UniversityQinhuangdao 066004HebeiChina

An anti-saturation fault-tolerant adaptive torsional vibration control method with fixed-time prescribed performance for the rolling mill main drive system(RMMDS)was investigated,which is affected by control input saturation,actuator faults,sensor measurement errors,and parameter perturbations.First,we gave a continuously differentiable saturation function to approximate the control input saturation characteristic of the RMMDS,translating the saturation characteristic into the matched uncertainty and unknown time-varying gain in the system.Then,an RMMDS mathematical model with unmatched uncertainty and unknown time-varying gain was developed,taking into account the presence of control input saturation,actuator faults,sensor measurement errors,and parameter perturbations.Based on the established mathematical model,an error transformation model of the roll speed tracking was constructed by the equivalent error transformation method.According to the error transformation model,a barrier Lyapunov function and a novel adaptive controller were studied to ensure that the roll speed tracking error always evolves inside a fixed-time asymmetric constraint.Finally,numerical simulations were performed in Matlab/Simulink to verify the effectiveness and superiority of the proposed control method in suppressing the RMMDS torsional vibration.

关键词： Fixed-time prescribed performance Control input saturation Actuator fault Unknown time-varying gain Adaptive control Asymmetric constraint

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Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing Networks:An Online Algorithm

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China Communications 2024年第5期21卷 153-165页

作者： Ge Yiyang Xiong Ke Dong Rui Lu Yang Fan Pingyi Qu Gang School of computer and Information technology Beijing Jiaotong UniversityBeijing 100044China engineering research center of Network Management technology for High Speed Railway of Ministry of Education Beijing 100044China Beijing engineering research center of High-speed Railway Broadband Mobile Communications Beijing 100044China Department of Electronic engineering Tsinghua UniversityBeijing 100084China Beijing national research center for Information Science and technology Tsinghua UniversityBeijing 100084China Department of Electrical and computer engineering College ParkMarylandUSA

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.***.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

关键词： age of information(aoi) mobile edge computing(mec) user scheduling

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Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

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Journal of computer Science & technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 national engineering research center for Big Data technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial intelligence.Graph processing exhibits irregular computational parallelism and random memory accesses,unlike traditional workloads.Therefore,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph algorithm.While domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software codesigns.This article presents a graph processing ecosystem from hardware to software.We start by introducing a series of hardware accelerators as the foundation of this ecosystem.Subsequently,the codesigned parallel graph systems and their distributed techniques are presented to support graph applications.Finally,we introduce our efforts on novel graph applications and hardware architectures.Extensive results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of computer technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of national Special computer engineering technology research center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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Transformer in reinforcement learning for decision-making:a survey

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Frontiers of Information technology & Electronic engineering 2024年第6期25卷 763-790页

作者： Weilin YUAN Jiaxing CHEN Shaofei CHEN Dawei FENG Zhenzhen HU Peng LI Weiwei ZHAO College of Information and Communication National University of Defense TechnologyWuhan 430014China College of Intelligence Science and technology National University of Defense TechnologyChangsha 410072China Laboratory for parallel and Distributed Processing National University of Defense TechnologyChangsha 410072China

Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world applications.Notably,deep neural networks play a crucial role in unlocking RL’s potential in large-scale decision-making tasks.Inspired by current major success of Transformer in natural language processing and computer vision,numerous bottlenecks have been overcome by combining Transformer with RL for decision-making.This paper presents a multiangle systematic survey of various Transformer-based RL(TransRL)models applied in decision-making tasks,including basic models,advanced algorithms,representative implementation instances,typical applications,and known challenges.Our work aims to provide insights into problems that inherently arise with the current RL approaches,and examines how we can address them with better TransRL models.To our knowledge,we are the first to present a comprehensive review of the recent Transformer research developments in RL for decision-making.We hope that this survey provides a comprehensive review of TransRL models and inspires the RL community in its pursuit of future directions.To keep track of the rapid TransRL developments in the decision-making domains,we summarize the latest papers and their open-source implementations at https://***/williamyuanv0/Transformer-in-Reinforcement-Learning-for-Decision-Making-A-Survey.

关键词： Transformer Reinforcement learning(RL) Decision-making(DM) Deep neural network(DNN) Multi-agent reinforcement learning(MARL) Meta-reinforcement learning(Meta-RL)

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