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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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Software-Defined Edge computing: A New Architecture Paradigm to Support IoT Data Analysis

arXiv

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arXiv 2021年

作者： Wu, Di Xie, Xiaofeng Nie, Xiang Fu, Bin Deng, Hanhui Zeng, Haibo Qin, Zhijin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University China Department of Electrical and Computer Engineering Virginia Tech United States EECS Queen Mary University of London United Kingdom

—The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their generated data such as time-dependency. Therefore, it is desirable to reshape the current IoT architectures by exploring their inherent nature of communication and computing to support smart IoT data process and analysis. We introduce in this paper features of IoT data, trends of IoT network architectures, some problems in IoT data analysis, and their solutions. Specifically, we view that software-defined edge computing is a promising architecture to support the unique needs of IoT data analysis. We further present an experiment on data anomaly detection in this architecture, and the comparison between two architectures for ECG diagnosis. Results show that our method is effective and feasible. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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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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A Real-Time CAN-CAN Gateway with Tight Latency Analysis and Targeted Priority Assignment

A Real-Time CAN-CAN Gateway with Tight Latency Analysis and ...

引用

Real-Time Systems Symposium (RTSS)

作者： Guoqi Xie Haijie Gong Yunbo Han Samarjit Chakraborty Wanli Chang College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China Future Network Lab CSIG Tencent China University of North Carolina at Chapel Hill USA University of York UK

ISBN: (数字)9781728183244

ISBN: (纸本)9781728183251

There is a demand in the automotive industry to connect two CAN-based subsystems. The commercial CAN-CAN gateway supports basic message forwarding with no real-time behavior. To address this issue, a new gateway architecture is described, on which we present a novel worst-case latency analysis. Specifically, we bound the arrival of the messages at the gateway, which is then used by the Pointer Reachability Exploration (PRE) to derive the interfering message jobs. Our analysis computes a safe gateway latency tighter than the conventional one applied in CAN. Furthermore, we propose a Targeted Priority Assignment (TPA) algorithm that targets at the priorities assigned at the CAN bus and runs a reordering at the gateway to enhance the schedulability. TPA performs better than DMPO (Deadline Monotonic Priority Ordering), while OPA (Audsley's Optimal Priority Assignment) cannot be applied in this context. Evaluation over real-life and scalable CAN message sets is conducted. The reported analysis and priority assignment algorithm are developed for dynamic use to improve the acceptance ratio and can also be deployed statically to provide timing guarantees. This work can be easily extended to support multiple CAN subsystems.

关键词： Heuristic algorithms Prototypes Computer architecture Logic gates Real-time systems Timing Vehicle dynamics

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9th International Conference on Intelligent computing, ICIC 2013

作者： Wang, Bing Ji, Zhiwei Advanced Research Institute of Intelligent Sensing Network Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China School of Electronics and Information Engineering Tongji University Shanghai 201804 China

ISBN: (纸本)9783642394812

The development of novel high-throughput experimental techniques makes it possible to comprehensively analyze biological data in health and disease. However, a large amount of data generated results in dramatic data-analytic challenges in discovery of 'signature' molecules, which are specific to different biological conditions (e.g. normal vs. disease, treated vs. untreated). Current statistical methods are effective only in the case their hypothesis can be matched. In this paper, we apply an ensemble statistical method to infer significant molecules. In our approach, four well-done and well-understanding statistical techniques had been used for the analysis to the experimental data, and then the results will be collected into an ensemble framework to find the high confident "significant" molecules which can distinguish the different experimental conditions. We evaluate the performance of our approach on a test dataset which deposited on GEO database with an access number of GSE45114. © 2013 Springer-Verlag.

关键词： Molecules

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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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EBCRO: Hybrid chemical reaction with employed bee operator

EBCRO: Hybrid chemical reaction with employed bee operator

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhiyong Li Tingkun Yuan Bo Yang Shilong Jiang Yanmei Xie College of Computer Science and Electronic Engineering of Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China PKU-HKUST Shenzhen-Hongkong Institution Shenzhen China Hunan Vocational College of Modern Logistics Changsha China

His article proposes a new hybrid algorithm EBCRO, which combines the local search operator of chemical reaction optimization algorithm (CRO) with modified employed bee operator (EB) main part of artificial bee colony algorithm (ABC), and the introduction of probability-based selection of molecular method. This algorithm creates new solutions not only by local search operation of CRO but also mechanisms of EB. CRO is a newly, effectively swarm intelligence optimization algorithm. However, owing to random characteristics of CRO and not make use of other solutions information, its search ability and convergence speed are inefficient. EB as a part of ABC has simplicity and efficiency global search ability, but the solution is updated only by other solutions information which leading to lack of local search ability. Therefore, this paper presents EBCRO, which can balance the exploitation and exploration of algorithm of numerical function optimization. In this study, EBCRO is tested on 23 benchmark numerical functions, and the simulation results of EBCRO, CRO, orthogonal chemical reaction optimization (OCRO), hybrid particle swarm and chemical reaction optimization (HP-CRO2), and ABC showed that EBCRO outperforms other algorithms in convergence performance.

关键词： Optimization Chemicals Convergence Particle swarm optimization Linear programming Kinetic energy

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RELIABILITY-DRIVEN REAL-TIME TASK REPLICATION FOR FAULT TOLERANCE IN HIERARCHICAL SCHEDULING MULTIPROCESSOR SYSTEMS

RELIABILITY-DRIVEN REAL-TIME TASK REPLICATION FOR FAULT TOLE...

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2013IET International Conference on Information and Communication Technologies(IETICT2013)2013IET信息与通信技术国际会议

作者： Lin Liu Renfa Li Rui Li Yong Xie Qingguang Zeng Key Laboratory for Embedded and Network Computing of Hunan Province Hunan UniversityChangshaChina

ISBN: (纸本)9781629931357

The increasing complexity of systems poses a big challenge to design systems with fault-tolerant *** aim of this study was to establish fault-tolerant system for multiprocessor platform with the help of hierarchical scheduling framework.A mathematical fault and reliability model for hierarchical scheduling framework was proposed firstly,and then a heuristic algorithm by exploiting the difference of task resource overhead and the probability of task failures was presented and a task replication scheme was given *** experimental results demonstrated the proposed scheme could effectively guarantee reliability goal and reduce resource consumption simultaneously.

关键词： multiprocessor fault tolerance task replication reliability requirements the hierarchical scheduling

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Non-cooperative game approach for task offloading in edge clouds

arXiv

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arXiv 2018年

作者： Yang, Bo. Li, Zhiyong Liu, Wenbin College of Information and Management Hunan University of Finance and Economics Changsha410205 China College of Computer Science and Electronic Engineering of Hunan University National Supercomputing Center in Changsha Key Laboratory for Embedded and Network Computing of Hunan Province Changsha410082 China

—Task offloading provides a promising way to enhance the capability of the mobile terminal (also called terminal user) that is distributed on network edge and communicates edge clouds with wireless. Generally, there are multiple edge cloud nodes with distinct processing capability in a geographic area, which can offer computing service for various terminal users. Furthermore, the terminal users are competitive and selfish, i.e., each user takes into account only maximizing her own profit, while conducting task offloading strategies. In this paper, we focus on the resource management optimization for edge clouds, and formulate the problem of resource competition among terminal users as a non-cooperative game, in which the terminal user who acts as the player always pursues the minimization of the expected response time for her tasks by optimizing allocation strategies. We present the utility function of the user with queuing theory, and then prove the existence of Nash equilibrium for the formulated game. Using the concept of Nash bargaining solution to calculate the optimal task offloading scheme for the user, we propose a distributed task offloading algorithm with low computation complexity. The results of simulated experiments demonstrate that our method can quickly reach the Nash equilibrium point, and deliver satisfying performance at the expected response time of the user’s tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Game theory

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Improving performance for simulating complex fluids on massively parallel computers by component loop-unrolling and communication hiding 22

Improving performance for simulating complex fluids on massi...

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22nd IEEE International Conference on High Performance computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Guo, Xiao-Wei Li, Chao Li, Wei Cao, Yu Liu, Yi Zhao, Ran Zhang, Sen Yang, Canqun Institute for Quantum Information State Key Laboratory of High Performance Computing Colloege of Computer National University of Defense Technology Changsha Hunan China Colloege of Meteorology and Oceanology National University of Defense Technology Changsha Hunan China Press and Communication Center of the Pla Technical Support Office of Network Department Beijing China

ISBN: (纸本)9781728176499

Due to the complex geometry and physical models of real-world engineering applications, the parallel performance of the mainstream computational fluid dynamics(CFD) codes is unsatisfactory. For complex fluids, an extra stress tensor governed by constitutive equations including nine components brings much more amount of computations. This paper focused on optimizing the most compute-intensive part of a simulation for complex fluids: The iterative linear solver for solving multicomponent equations. Based on the most widely used opensource CFD code OpenFOAM, we unrolled the component loops and replaced the blocking collective MPI calls to non-blocking communications. After operation rescheduling between the loops, the collective communications could be partly overlapped by the computations. Taking the preconditioned conjugate gradient (PCG) algorithm for instance, we presented the complete loop unrolled algorithm for solving multi-component equations. The numerical experiments showed 8.0%29.0% simulation time reduction for a demonstrative case with 2 million cells on 642048 cores. It is worth noting that the approach proposed in this paper is a high-level scheduling algorithm and could be used in combination with other intra-component optimization algorithms, *** pipelined CG methods. © 2020 IEEE.

关键词： Constitutive equations

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