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International Workshop on Quality of Service

作者： Xiaohang Shi Sheng Zhang Ke Cheng Yu Chen Andong Zhu Sanglu Lu State Key Lab. for Novel Software Technology Nanjing University P.R. China

Interactive mobile augmented reality (MAR) applications such as Connected Lens are becoming popular, which often rely on deep neural network (NN)-based video analytics techniques to understand the real world. However, performing computation-intensive NN inference on resource-constrained mobile devices is impractical. It is thus proposed to offload the workloads to edge servers with the help of mobile edge computing (MEC). Existing works often focus on system-wide offloading solutions, optimizing the personalized user experience for interactive applications in dynamic environments is yet rarely studied, where multiple challenges remain to be solved. First, the user has to decide the configuration for video analytics, where the inherent accuracy-cost trade-off exists. Second, it is intractable to decide the target server for offloading, since each server supports limited configurations, and a user needs to balance the experience of analytics service and the quality of interaction with others at the same time. Third, the fluctuating network information is often undisclosed to the users, and the candidate servers also vary over time. Therefore, in this paper, we propose an online user-managed server selection and configuration adaptation scheme (OSCA). Via Lyapunov optimization, we aim to maximize the long-term service experience, under the interactive quality constraint with other users. Besides, volatile multi-armed bandit (MAB) is utilized to handle the network fluctuation and the variance of the candidate servers. We conduct rigorous theoretical analysis, and the deviations of both the service experience and the interactive quality are bounded. Through extensive trace-driven experiments, we demonstrate the superior performance of OSCA.

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A New Framework for Bounding Reachability Probabilities of Continuous-time Stochastic Systems

arXiv

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arXiv 2023年

作者： Xue, Bai State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

This manuscript presents an innovative framework for constructing barrier functions to bound reachability probabilities for continuous-time stochastic systems described by stochastic differential equations (SDEs). The reachability probabilities considered in this paper encompass two aspects: the probability of reaching a set of specified states within a predefined finite time horizon, and the probability of reaching a set of specified states at a particular time instant. The barrier functions presented in this manuscript are developed either by relaxing a parabolic partial differential equation that characterizes the exact reachability probability or by applying the Grönwall's inequality. In comparison to the prevailing construction method, which relies on Doob's non-negative supermartingale inequality (or Ville's inequality), the proposed barrier functions provide stronger alternatives, complement existing methods, or fill gaps. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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A Stochastic Resource Management Scheme for Multi-Energy Group Control System 3

A Stochastic Resource Management Scheme for Multi-Energy Gro...

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3rd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Ding, Ran Sun, Rongfu Geng, Yan Yao, Yiming Zhao, Ergang Xu, Lina State Grid Jibei Electric Power Company Limited Beijing China Tsinghua University State Key Lab. of Control and Simulation of Power System and Generation Equipment Dept. of Electrical Engineering Beijing China

ISBN: (纸本)9781665472784

The interaction between various energy supply systems (cold, heat, electricity, gas, etc.) is becoming more tightly coupled as social economy and technology advance. Multi-energy systems are ineffective because different types of energy systems run independently. In order to protect the data privacy of each sub-system, this research suggests an ideal resource management framework for a multi-energy group control system. The energy hub (EH) paradigm is first suggested. When deploying EHs, the physical constraints of various energy systems are taken into account. Through scenario-based stochastic programming, the uncertainties of renewable energy are considered. According to the leader-follower relationship between multi-energy group control system operators and energy hub operators (EHOs), Benders decomposition is applied to decentralize the decision-making process. Finally, an example is given to illustrate the effectiveness of the proposed model and algorithm. © 2023 IEEE.

关键词： Data privacy

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An Iterative Method for Computing Controlled Reach-Avoid Sets

An Iterative Method for Computing Controlled Reach-Avoid Set...

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American Control Conference (ACC)

作者： Dejin Ren Taoran Wu Bai Xue State Key Lab. of Computer Science CAS Institute of Software Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper focuses on addressing the problem of computing controlled reach-avoid sets for continuous-time systems modeled by polynomial ordinary differential equations with control inputs in order to ensure the safety and reachability of safety-critical systems. In a controlled reach-avoid set (CRAS), there exists a feedback controller capable of driving the closed-loop system into a target set of desired states while avoiding hazardous regions. The computation of a controlled reach-avoid set can be transformed into a search for a guidance-barrier function in existing literature. However, existing guidance-barrier functions tend to produce conservative CRASs due to certain flaws. To overcome this issue, a new guidance-barrier function is introduced, which generates less conservative CRASs compared to existing ones. Given the inherent nonlinearity associated with searching for guidance-barrier functions in the control setting, an iterative computational approach is proposed. This approach modifies controllers from previous iterations to find these functions and expand CRASs by solving convex optimizations. Finally, numerical results demonstrate the efficiency of our method in generating less conservative CRASs.

关键词： Three-dimensional displays Computational modeling Ordinary differential equations Polynomials Mathematical models Convex functions Safety

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CODESWIFT: Accelerating LLM Inference for Efficient Code Generation

arXiv

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arXiv 2025年

作者： Zhao, Qianhui Zhang, Li Liu, Fang Lian, Xiaoli Meng, Qiaoyuanhe Jiao, Ziqian Zhou, Zetong Zhang, Borui Guo, Runlin Li, Jia School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Beihang University China Key Lab of High Confidence Software Technology Peking University China

Code generation is a latency-sensitive task that demands high timeliness, but the autoregressive decoding mechanism of Large Language Models (LLMs) leads to poor inference efficiency. Existing LLM inference acceleration methods mainly focus on standalone functions using only built-in components. Moreover, they treat code like natural language sequences, ignoring its unique syntax and semantic characteristics. As a result, the effectiveness of these approaches in code generation tasks remains limited and fails to align with real-world programming scenarios. To alleviate this issue, we propose CODESWIFT, a simple yet highly efficient inference acceleration approach specifically designed for code generation, without comprising the quality of the output. CODESWIFT constructs a multi-source datastore, providing access to both general and project-specific knowledge, facilitating the retrieval of high-quality draft sequences. Moreover, CODESWIFT reduces retrieval cost by controlling retrieval timing, and enhances efficiency through parallel retrieval and a context- and LLM preference-aware cache. Experimental results show that CODESWIFT can reach up to 2.53× and 2.54× speedup compared to autoregressive decoding in repository-level and standalone code generation tasks, respectively, outperforming state-of-the-art inference acceleration approaches by up to 88%. Our code and data are availab.e at https://***/r/CodeSwift. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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A Service-Enhanced Task Offloading Method in MEC-Enabled IoV Networks 19

A Service-Enhanced Task Offloading Method in MEC-Enabled IoV...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalab.e Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhao, Bohai Peng, Kai Leung, Victor C.M. Xia, Yunni Huaqiao University College of Engineering Quanzhou China Nanjing University State Key Lab. for Novel Software Technology Nanjing China Shenzhen University School of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada Chongqing University School of Computers Chongqing China

ISBN: (纸本)9798350346558

To facilitate the evolution from independent transportation means to vehicle-to-infrastructure communications, traditional vehicle-to-vehicle (V2V) technologies are growing to a more advanced and generalized paradigm, i.e., the Internet of Vehicles (IoV). This paradigm has stimulated ever-increasing calling for more capabilities and intelligence for vehicles to foster the potential of supporting a plethora of emerging applications and ubiquitous automotive network access. Combining caching-aided communication and edge computing has been expected to be an advantageous framework to cope with the surging requirements for timely data processing and storage from intelligent IoV applications. In light of the delay restriction and the heterogeneity of edge service nodes (ESNs), we study the service-enhanced problem in edge-enabled IoV networks by configuring proactive service caching. Specifically, based on task scale and relational restrictions, we design an adaptive non-linear caching probability mapping function and further propose a proactive service-enhanced task offloading method in edge-enabled IoV networks named SEOEV. Performance evaluation demonstrates that SEOEV efficiently reduces task processing overhead in comparison to other comparative computing migration approaches. © 2022 IEEE.

关键词： Multiobjective optimization

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Evolution of Cooperation in Snowdrift Game with Multi-Memory Mechanism 42

Evolution of Cooperation in Snowdrift Game with Multi-Memory...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Yaojun Liu, Xingwen Tashi, Nyima College of Electrical Engineering Southwest Minzu University Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission Sichuan Chengdu610225 China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa850000 China

ISBN: (纸本)9789887581543

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known 'Stanford marshmallow experiment' (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Behavioral research

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Detecting Exception Handling Bugs in C++ Programs 23

Detecting Exception Handling Bugs in C++ Programs

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45th IEEE/ACM International Conference on software engineering, ICSE 2023

作者： Zhang, Hao Luo, Ji Hu, Mengze Yan, Jun Zhang, Jian Qiu, Zongyan Institute of Software Cas China Univ. of Chinese Academy of Sciences State Key Lab. of Computer Science Beijing China Univ. of Chinese Academy of Sciences Tech. Center of Softw. Eng. Beijing China Hangzhou Institute for Advanced Study Univ. of Chinese Academy of Sciences State Key Lab. of Computer Science Beijing China School of Mathematical Sciences Peking University Beijing China

ISBN: (纸本)9781665457019

Exception handling is a mechanism in modern programming languages. Studies have shown that the exception handling code is error-prone. However, there is still limited research on detecting exception handling bugs, especially for C++ programs. To tackle the issue, we try to precisely represent the exception control flow in C++ programs and propose an analysis method that makes use of the control flow to detect such bugs. More specifically, we first extend control flow graph by introducing the concepts of five different kinds of basic blocks, and then modify the classic symbolic execution framework by extending the program state to a quadruple and properly processing try, throw and catch statements. Based on the above techniques, we develop a static analysis tool on the top of Clang Static Analyzer to detect exception handling bugs. We run our tool on projects with high stars from GitHub and find 36 exception handling bugs in 8 projects, with a precision of 84%. We compare our tool with four state-of-the-art static analysis tools (Cppcheck, Clang Static Analyzer, Facebook Infer and IKOS) on projects from GitHub and handmade benchmarks. On the GitHub projects, other tools are not able to detect any exception handling bugs found by our tool. On the handmade benchmarks, our tool has a significant higher recall. © 2023 IEEE.

关键词： Static analysis

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Concrete Structural Crack Damage Classification Using Nonlinear Dimension Reduction and Broad Learning System

Concrete Structural Crack Damage Classification Using Nonlin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Bingshu Lin, Jia Zhuang, Xiaodong Zhang, Guanghui Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi'an China Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen China School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781665410205

Concrete structural crack damage classification is of importance for road safety. This paper proposes a new method based on broad neural network for crack damage classification in concrete structures. It includes three stages. Firstly, a pre-trained deep neural network is used to extract the features from crack images. Secondly, principal component analysis is used to project the retrieved features from high dimensions to low dimensions. Thirdly, broad learning system is employed to predict the classification using the low-dimensional features. Experimental results demonstrate that this method reduces the model's training time and improves classification accuracy. © 2024 IEEE.

关键词： Deep neural networks

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xian, Peijie He, Ying Yu, F. Richard Du, Jianbo Shenzhen Univ. College of Computer Science and Software Engineering Shenzhen China Xi'an Univ. of Posts & Telecomm. Shaanxi Key Lab. of Information Comm. Network and Security Shaanxi Xi'an China Carleton Univ. School of Information Technology OttawaON Canada

ISBN: (纸本)9798350351255

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation. © 2024 IEEE.

关键词： Active learning

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