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Zram Instance Pool Framework for Adaptive Memory Compression in Resource-Sensitive embedded Operating Systems

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Deng, Yin Xie, Guoqi Xiong, Chenglai Zhao, Sirong Ren, Wei Li, Kenli Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan Changsha410082 China Huawei Technology Co. Ltd. Central Software Institute Guangdong Shenzhen518129 China

Memory compression can reduce the size of the inactive data in the random access memory (RAM), thereby freeing up unused space and allowing more programs to run;however, current mainstream memory compression frameworks (e.g., Zram and Zswap) and algorithms (e.g., Zstd and Lz4) do not effectively solve the problem of increased CPU utilization, causing they cannot be directly applied to the resource-sensitive embedded operating system that is sensitive to both CPU utilization and memory usage. In this study, we develop a Zram instance pool framework called ZramPool for adaptive memory compression. The framework consists of the swap space with multiple Zram instances and the adaptive Zram compression module. Through introducing linear regression analysis, the number of Zram instances can be adaptively adjusted based on the size of the compressed data, allowing Zram instances to work in parallel to match the workload. In ZramPool, we achieve two different requirements of reducing CPU utilization while keeping compression speed and increasing compression speed while keeping CPU utilization. ZramPool is deployed in the embedded Linux OS with a 8GB memory size running on the ARMv8 architecture. For the first requirement, ZramPool can reduce CPU utilization by an average of 11.42% while the compression speed only decreases by an average of 2.4%. For the second requirement, ZramPool can increase compression speed by an average of 11.71% while the CPU utilization only increases by an average of 1.9%. © 1982-2012 IEEE.

关键词： Random access storage

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CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System

arXiv

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arXiv 2024年

作者： Guan, Qinghua Ouyang, Jinhui Wu, Di Yu, Weiren Zhu, Yi Yuan, Xiang The Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China The Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom

The spatiotemporal data generated by massive sensors in the Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, and stability) in real-time analysis and decision making for different IoT applications. The complexity of IoT data prevents the common people from gaining a deeper understanding of it. Agentized systems help address the lack of data insight for the common people. We propose a generic framework, namely CityGPT, to facilitate the learning and analysis of IoT time series with an end-to-end paradigm. CityGPT employs three agents to accomplish the spatiotemporal analysis of IoT data. The requirement agent facilitates user inputs based on natural language. Then, the analysis tasks are decomposed into temporal and spatial analysis processes, completed by corresponding data analysis agents (temporal and spatial agents). Finally, the spatiotemporal fusion agent visualizes the system’s analysis results by receiving analysis results from data analysis agents and invoking sub-visualization agents, and can provide corresponding textual descriptions based on user demands. To increase the insight for common people using our framework, we have agnentized the framework, facilitated by a large language model (LLM), to increase the data comprehensibility. Our evaluation results on real-world data with different time dependencies show that the CityGPT framework can guarantee robust performance in IoT computing. Copyright © 2024, The Authors. All rights reserved.

关键词： Internet of things

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Fault Tolerance in Time-Sensitive networking with Mixed-Critical Traffic

Fault Tolerance in Time-Sensitive Networking with Mixed-Crit...

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Design Automation Conference

作者： Wenhong Ma Xiangzhen Xiao Guoqi Xie Nan Guan Yu Jiang Wanli Chang Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University City University of Hong Kong Tsinghua University China

As an Ethernet-based communication protocol with timing determinism, TSN (time-sensitive networking) has become a well-recognized promising in-vehicle network solution for increasingly automated automobiles. To satisfy the reliability requirement of safety-critical applications, existing works towards fault-tolerant TSN trade too much bandwidth for redundancy, limiting their scope of applicability. Targeting mixed-critical traffic, which is widely found in practice, we define a shared fault-tolerant segment that is compatible with the TSN standard. It serves the critical flows when faults occur, to improve their reliability and serves the non-critical flows when otherwise, to improve their quality of service (QoS). On top of this, we propose a space-time redundancy scheduling algorithm, aiming to make the most efficient use of bandwidth, i.e., to fulfill both the reliability as well as hard real-time requirements, and maximize the QoS with the least bandwidth. In essence, we formulate a bi-objective design space exploration problem with hundreds of thousands of decision variables and solve it with customized heuristics. Experimental results show that compared to the state-of-the-art methods, our reported work increases the number of critical flows that can be accommodated on a resource-constrained network by 3 to 4 times, and achieve the highest QoS with an average reduction of 60.3% in bandwidth. As the first work along sharing of bandwidth between mixed-critical traffic in fault-tolerant TSN, this idea can be further pursued towards higher efficiency and may be applied in general autonomous systems.

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Cyber-Physical Systems Design in An Uncertain Environment with Time Uncertainty Concern

Cyber-Physical Systems Design in An Uncertain Environment wi...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Si Chen Lida Huang Xiongren Xiao Yan Liu Guoqi Xie Renfa Li College of Computer Science and Electronic Engineering Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China Center for Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing China

Multiple processors system on chip (MPSoC) has been the trend in cyber-physical systems (CPSs), and reasonable partitioning for MPSoC resources is a critical step in CPSs design. The uncertainties of environment and time are both important factors that need to be considered in the design, but none of the previous work pays attention to two uncertainties at the same time. The state-of-the-art work presented a detailed process of applying uncertain programming to solve the partitioning problem, which provides a solution for designing in an uncertain environment. However, this work only considers the bipartition scenario which cannot be directly applied to MPSoC, and it does not focus specifically on time uncertainty. In this paper, we propose a method for modeling the MPSoC partitioning problem in an uncertain environment, with the time uncertainty concern. We present the uncertain model that can be applied to the multiple optional resources scenario. We build the optimization model with the objective of minimizing time, analyze two different cases of minimizing the uncertain time, and finally prove a unified deterministic model to solve. We come up with three algorithms, including the heuristic algorithm, the genetic algorithm, and the exact algorithm, and experiments show that the heuristic algorithm and the genetic algorithm can obtain good approximate solutions compared with the exact algorithm.

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RTISM: Real-Time Inter-VM Communication Based on Shared Memory for Mixed-Criticality Flows

RTISM: Real-Time Inter-VM Communication Based on Shared Memo...

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Real-Time Systems Symposium (RTSS)

作者： Zonghong Li Guoqi Xie Wenhong Ma Xiongren Xiao Yong Xie Wei Ren Wanli Chang Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University China Research Institute of Hunan University in Chongqing China Nanjing University of Posts and Telecommunications China Huawei Technologies China

Virtual machines (VMs) have been increasingly deployed in real-time systems to enhance heterogeneous resource sharing. Despite being isolated for security and prevention of failure propagation, VMs need to communicate with each other to complete certain tasks jointly. A real-time inter-VM communication framework has been proposed based on VirtIO, which is in essence a mechanism of message passing with high latency and low scalability on data amount. In contrary, IVSHMEM implements inter-VM communication with shared memory, which is generally fast and large in throughput. Unfortunately, IVSHMEM does not resolve resource contention between flows and hence cannot be applied in real-time scenarios. In this work, we propose a real-time inter-VM communication framework RTISM built upon shared memory and IVSHMEM. RTISM provides priority-based scheduling and supports mixed-criticality flows. Worst-Case Response Time (WCRT) analysis is reported to bound the end-to-end communication delay and a Limited Priority Assignment (LPA) algorithm is developed to enhance schedulability. Experimental evaluation shows that (i) RTISM has about 4 times higher throughput than the VirtIO-based inter-VM communication framework; (ii) LPA improves schedulability by over 25% compared to the state-of-the-art; (iii) WCRT produces a tight bound.

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Personalized News Recommendation Based on the Text and Image Integration

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Computers, Materials & Continua 2020年第7期64卷 557-570页

作者： Kehua Yang Shaosong Long Wei Zhang Jiqing Yao Jing Liu College of Computer Science and Electronic Engineering and Key Laboratory for Embedded and Network Computing of Hunan Province Hunan UniversityChangsha410082China Oath Verizon Company New York10007USA

The personalized news recommendation has been very popular in the news recommendation *** most research,the picture information in the news is ignored,but the information conveyed to the users through pictures is more intuitive and more likely to affect the users’reading interests than the one in the textual ***,in this paper,a model that combines images and texts in the news is *** this model,the new tags are extracted from the images and texts in the news,and based on these new tags,an adaptive tag(AT)algorithm is *** AT algorithm selects the tags the user is interested in based on the user *** particular,the AT algorithm can predict tags that a user may be interested in with the help of the tag correlation graph without any user *** proposed AT algorithm is verified by *** experimental results verified the AT algorithm regarding three evaluation indexes F1-score(F1),area under curve(AUC)and mean reciprocal rank(MRR).The recommended effect of the proposed algorithm is found to be better than those of the various baseline algorithms on real-world datasets.

关键词： News recommendation F1 AUC MRR feedback correlation graph

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AutoToolVCC: Variable consistency check tool for automotive software projects

AutoToolVCC: Variable consistency check tool for automotive ...

引用

作者： Xie, Pingfu Wang, Yunlong He, Bo Xie, Guoqi College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China United Automotive Electronic Systems Co. Ltd. Shanghai China

Automotive software systems have become increasingly complex and huge, leading to long development cycle and increased development difficulty. When a complete automotive software project has reached a scale of several gigabytes, the complicated development process is prone to produce the following two irregular problems: (1) variable inconsistency problem and (2) code coexistence problem. These irregular problems may eventually lead to serious consequences, such as car crashes. In response to the above irregular problems, we develop a variable consistency check tool called AutoToolVCC for automotive software projects. AutoToolVCC divides the variable consistency check process into two stages: (1) extracting all variable information from the automotive software project and (2) checking the consistency of the variables through the variable preprocessing technique and the variable attribute check technique. After an actual check of multiple automotive software projects (approximately 500–1000 MB in size) provided by an automotive manufacturer, the accuracy of variable consistency check is as high as 100%, and the check time only takes about 10–30 s, whereas the pervious plain method needs 100–600 s. © 2020 John Wiley & Sons, Ltd.

关键词： Accidents

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STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking

arXiv

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arXiv 2022年

作者： Wang, Haidong Li, Zhiyong Li, Yaping Nai, Ke Wen, Ming College of Computer Science Electronic Engineering of Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province Hunan Province Changsha410082 China State Grid Hunan Electric Power Company Limited Economical Technical Research Institute Hunan Key Laboratory of Energy Internet Supply-demand and Operation Hunan Province Changsha410000 China

Online multi-object tracking (MOT) is a longstanding task for computer vision and intelligent vehicle platform. At present, the main paradigm is tracking-by-detection, and the main difficulty of this paradigm is how to associate current candidate detections with historical tracklets. However, in the MOT scenarios, each historical tracklet is composed of an object sequence, while each candidate detection is just a flat image, which lacks temporal features of the object sequence. The feature difference between current candidate detections and historical tracklets makes the object association much harder. Therefore, we propose a Spatial-Temporal Mutual Representation Learning (STURE) approach which learns spatial-temporal representations between current candidate detections and historical sequences in a mutual representation space. For historical trackelets, the detection learning network is forced to match the representations of sequence learning network in a mutual representation space. The proposed approach is capable of extracting more distinguishing detection and sequence representations by using various designed losses in object association. As a result, spatial-temporal feature is learned mutually to reinforce the current detection features, and the feature difference can be relieved. To prove the robustness of the STURE, it is applied to the public MOT challenge benchmarks and performs well compared with various state-of-the-art online MOT trackers based on identity-preserving metrics. Copyright © 2022, The Authors. All rights reserved.

关键词： E-learning

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Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

Energy allocation for activity recognition in wearable devic...

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作者： Xiao, Ling Meng, Yu Tian, Xiaobing Luo, Haibo College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer Science Guangdong University of Science And Technology Dongguan China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China College of Computer and Control Engineering Minjiang University Fujian China

Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low-power. Energy allocation is essential for motion-based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra-low-power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real-world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices. © 2021 John Wiley & Sons, Ltd.

关键词： Kinetic energy

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FedDDB: Clustered Federated Learning based on Data Distribution Difference 22

FedDDB: Clustered Federated Learning based on Data Distribut...

引用

Proceedings of the 2022 5th International Conference on Algorithms, computing and Artificial Intelligence

作者： Chengyu You Zihao Lu Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University China

ISBN: (纸本)9781450398336

Clustered federated learning is a federated learning method based on multi-task learning. It groups similar clients into the same clusters and shares model parameters to solve the problem that the joint model is trapped in local optima on Non-IID data. Most of the existing clustered federated learning methods are based on the difference of model parameters for clients clustering. During the client model training process, the model parameters are biased and the clustering result is affected due to insufficient samples and missing eigenvalues in the dataset. In this paper, we develop a clustered federated learning method based on data distribution difference (FedDDB) in the dataset level. The method in this paper focuses on the distribution of label probability and eigenvalues, analyzes the difference of data distribution difference between clients and measures the distance between datasets which is used for client clustering. Every cluster will be trained independently and in parallel on the cluster center model. At the beginning of each round of training, the client clustering process needs to be repeated. We conduct relevant experiments and demonstrate the effectiveness of our method.

关键词： Clustering method

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