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Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Zhongping Lin Wensheng Guo Xia Yang Haiyong Sun School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Lianan Tech Chengdu China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

ARINC653 is an important standard for IMA integrated modular avionics systems. In today's rapidly developing world, single core ARINC653 can no longer meet the increasingly diverse tasks and complex systems. Therefore, research on multi-core ARINC653 systems is crucial. This article abstracts the task scheduling of ARINC653 partition system, analyzes the scheduling of ARINC653 multi-core tasks from two levels: inter partition scheduling and intra partition scheduling, and proposes an improved two-level scheduling method. Compared with the traditional ARINC653 scheduling algorithm, this method has fewer partition splitting times. And use AADL modeling language to simulate the system.

关键词： Analytical models scheduling algorithms Multicore processing Computational modeling Computer architecture scheduling Partitioning algorithms Kernel Complex systems Standards

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Scalable Distributed Least Squares Algorithm for Linear Algebraic Equations via scheduling

arXiv

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arXiv 2024年

作者： Liu, Shenyu The School of Automation Beijing Institute of Technology China

In this work, we propose a novel discrete-time distributed algorithm for finding least squares solutions of linear algebraic equations with a scheduling protocol to further enhance its scalability. Each agent in the network is assumed to know some rows of the coefficient matrix and the corresponding entries in the observation vector. Unlike typical distributed algorithms, our approach considers communication bandwidth limits, allowing agents to transmit only a portion of their "guessed" solution, independent of its dimension. A periodic scheduling protocol determines which portion is transmitted at each iteration. Assuming a small fixed step size and a diagonalizable algorithm matrix, we prove that agents' "guessed" solutions converge exponentially to a least squares solution. For cases where the observation vectors are time-varying, a modified algorithm guarantees practical convergence, with tracking error bounded by the single-step variation in the observation vector. Simulations on both small-scale and medium-scale examples validate the feasibility of our algorithms. © 2024, CC BY.

关键词： scheduling algorithms

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Three-Way Task scheduling Algorithm for Cloud Computing

Three-Way Task Scheduling Algorithm for Cloud Computing

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International Computer Science and Engineering Conference (ICSEC)

作者： Awatef Salem Balobaid Department of Computer Science College of Engineering & Computer Sciences Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

Cloud task scheduling is a crucial aspect of cloud computing, and it directly influences cloud resource usage. This study presents a cloud task scheduling algorithm based on granularity and three-way decision making (CTSA-3WD). This research aims to address the issues of load imbalance, low resource utilization, and job completion time. In the proposed approach, the execution duration of cloud jobs and the actual computing resource situation restrict the task set’s light-load and heavy-load functions. The algorithm is based on the three-way decision-making and separates the work set into three pieces. The system focusses on three distinct task sets and determines an optimal scheduling strategy by employing the Max-Min algorithm for the significant proportion of light-load jobs task set, the Min-Min algorithm for the substantial percentage of heavy-load studies, and the Min-Min and Max-Min algorithms combination for the task set with both light and heavy load tasks. The experimental results demonstrate that the CTSA-3WD algorithm, when compared to Min-Min, Max-Min, and selective scheduling algorithms, effectively enhances overall resource utilization. Additionally, it enables improved load balancing across the entire system.

关键词： Computer science Cloud computing Correlation scheduling algorithms Decision making Optimal scheduling Minimax techniques Load management scheduling Resource management

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Robust purification defense for transfer attacks based on probabilistic scheduling algorithm of pre-trained models: A model difference perspective

Robust purification defense for transfer attacks based on pr...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xinlei Liu Jichao Xie Tao Hu Hailong Ma Baolin Li Peng Yi Zhen Zhang Information Engineering University Zheng Zhou China Key Laboratory of Cyberspace Security Ministry of Education Zheng Zhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Neural networks are vulnerable to meticulously crafted adversarial examples, resulting in high-confidence misclassifications in image classification tasks. Due to their stealthiness and difficulty in detection, black-box transfer attacks have become a significant focus of defense. In this article, we propose a purification defense based on probabilistic scheduling algorithm of pre-trained models (ProbSched-PTM) to counter diverse transfer attacks. We first quantify the differences among various models based on their output scores and verify the linear negative correlation between adversarial transferability and model difference. Subsequently, guided by the model difference probability, we integrate the negative momentum probability as a regularization factor to construct ProbSched-PTM. It selects the most appropriate substitute model from multiple pre-trained models to generate strong-transferability adversarial examples for training the purification model, which enables the purification model to effectively eliminate diverse adversarial perturbations. The ProbSched-PTM-based purification defense provides robust defense against unseen adversarial attacks from different substitute models. In a black-box attack scenario, utilizing ResNet-34 as the target model, our approach achieves average defense rates of over 94.8% on CIFAR-10 and over 71.2% on Mini-ImageNet, demonstrating state-of-the-art performance.

关键词： Training Schedules Purification scheduling algorithms Perturbation methods Computational modeling Closed box Probabilistic logic Security Image classification

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A microservice workflow scheduling algorithm based on dynamic resource selection strategy

A microservice workflow scheduling algorithm based on dynami...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Peng Liu Dazhong Wang Yuxi Zhang Xiaojiang Wu China Electric Power Research Institute Beijing China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

To solve the problem of high overall scheduling cost in existing cloud based workflow scheduling algorithms when facing a large number of microservice tasks, a microservice workflow scheduling algorithm based on dynamic resource selection strategy (DRSS) is proposed - DRSS scheduling algorithm. By utilizing the position of tasks in the workflow to determine their sub deadlines and scheduling priorities, a dynamic resource selection strategy is adopted to schedule tasks and obtain the optimal resources for task execution. Based on this, the task status and resource vector of virtual machine instances are updated. The experimental results show that this algorithm is superior to similar algorithms in terms of scheduling success rate and cost.

关键词： Cloud computing Schedules Costs scheduling algorithms Heuristic algorithms Microservice architectures Dynamic scheduling

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Reinforcement Learning for Adaptive Resource scheduling in Complex System Environments

Reinforcement Learning for Adaptive Resource Scheduling in C...

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Computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Pochun Li Yuyang Xiao Jinghua Yan Xuan Li Xiaoye Wang Northeastern University Boston USA University of Illinois Urbana-Champaign Urbana USA University of Utah Salt Lake City USA Columbia University New York USA Western University London Canada

ISBN: (数字)9798331518677

ISBN: (纸本)9798331518684

This study presents a novel computer system performance optimization and adaptive workload management scheduling algorithm based on Q-learning. In modern computing environments, characterized by increasing data volumes, task complexity, and dynamic workloads, traditional static scheduling methods such as Round-Robin and Priority scheduling fail to meet the demands of efficient resource allocation and real-time adaptability. By contrast, Q-learning, a reinforcement learning algorithm, continuously learns from system state changes, enabling dynamic scheduling and resource optimization. Through extensive experiments, the superiority of the proposed approach is demonstrated in both task completion time and resource utilization, outperforming traditional and dynamic resource allocation (DRA) algorithms. These findings are critical as they highlight the potential of intelligent scheduling algorithms based on reinforcement learning to address the growing complexity and unpredictability of computing environments. This research provides a foundation for the integration of AI-driven adaptive scheduling in future large-scale systems, offering a scalable, intelligent solution to enhance system performance, reduce operating costs, and support sustainable energy consumption. The broad applicability of this approach makes it a promising candidate for next-generation computing frameworks, such as edge computing, cloud computing, and the Internet of Things.

关键词： Adaptive scheduling Q-learning Adaptive systems scheduling algorithms Heuristic algorithms System performance Dynamic scheduling Real-time systems Resource management Optimization

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Deterministic Message Flow scheduling in Industrial Internet: Message Middleware Use Case

Deterministic Message Flow Scheduling in Industrial Internet...

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International Conference on Communication Technology (ICCT)

作者： Xin Wang Chonghan Zhou Jing Jiang Key Laboratory of Information and Communication Systems Ministry of Information Industry and Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University (BISTU) Beijing China State Grid Economic and Technological Research Institute co. LTD Beijing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

This article mainly focuses on message flow scheduling for industrial message middleware in the integration of deterministic network with industrial internet, which is to find the optimal matching between telecom operator and message requests from industrial users. However, current deterministic network is limited to two or three layers, and the whole transmission reliability and latency involving industrial networking and service layers cannot be guaranteed, resulting in uncontrollable message scheduling reliability, deterministic latency, low channel resource utilization etc. To this end, an integer linear programming (ILP) model is formulated for message flow scheduling issue, with multi-objectives optimization of resource utilization and profit under customized message sending latencies, and deterministic reliability. A heuristic algorithm is also proposed. Furthermore, we present a Deep Reinforcement Learning based Message Flow scheduling algorithm (DRL-MFS), which not only solves complex message flow scheduling problems, but also achieves the self-optimization automatically. Proposed message flow scheduling approaches are simulated based on data from real cloud messaging service platforms. Simulations results prove that proposed DRL-MFS is superior to heuristic, and is close to ILP model in multi-objective optimization under three urgency levels of message sending. The reliability of three methods is proven to be deterministic.

关键词： Job shop scheduling scheduling algorithms Simulation Heuristic algorithms Deep reinforcement learning Telecommunications Telecommunication network reliability Resource management Middleware Optimization

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BLOA: Optimizing Resource Utilization using Multi-Objective Task scheduling in Cloud Environment

BLOA: Optimizing Resource Utilization using Multi-Objective ...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Santhosh Kumar Medishetti Charan Reddy Tamma Rakesh Venigalla Pranideep Reddy Meka Phani Vishnu Addepalli Shafreen Shamsheer VIT-AP University Amaravathi India Nalla Narasimha Reddy Education Society’s Group of Institution India George Mason University Virginia United States of America George Washington University Virginia United States of America

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The Butterfly Lion Optimization Algorithm (BLOA) is proposed as an innovative hybrid metaheuristic approach for Task scheduling (TS) in Cloud Computing (CC) environments. Proposed algorithm is combination of mating and foraging behaviour of Butterfly Optimization Algorithm (BOA) and hunting strategy of Lion Optimization Algorithm (LOA). TS is a critical challenge in CC, where the goal is to efficiently allocate resources while optimizing multiple objectives such as minimizing makespan, reducing execution time and response time while maximizing the throughput. The BLOA combines the dynamic exploration capabilities of butterfly behavior with the strategic hunting techniques of lions, creating a robust algorithm that balances global exploration and local exploitation. This hybrid algorithm leverages the strengths of both butterflies and lions to navigate complex, high-dimensional search spaces typical of CC environments. By integrating adaptive mechanisms, the BLOA is capable of dynamically adjusting to varying workload demands and resource availability. Comparative evaluations against other leading scheduling algorithms demonstrate that BLOA achieves superior performance in terms of reducing makespan time by 18.15%, execution time by 11.23%, and response time by 14.78%, while maximizing the throughput by 21.11%. The algorithm is especially useful for CC environments that are heterogeneous and distributed, which makes it ideal for cloud service providers who intend to improve resource utilization as well as decrease energy consumption and operational costs.

关键词： Cloud computing Energy consumption scheduling algorithms Navigation Heuristic algorithms Metaheuristics Throughput scheduling Time factors Resource management

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B-UFS: Uniform Resource Metric-Based Periodic Flow scheduling in 5G-TSN Integrated Network

B-UFS: Uniform Resource Metric-Based Periodic Flow Schedulin...

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International Symposium on Industrial Embedded Systems

作者： Dandan Wang Xi Jin Zhiwei Feng Qingxu Deng Northeastern University Shenyang China Shenyang Institute of Automation CAS Shenyang China

ISBN: (数字)9798350388633

ISBN: (纸本)9798350388640

In modern intelligent factories, numerous applications require deterministic end-to-end latency for flow transmission. The 5G-TSN integrated network is aiming to meet the flow transmission requirements of time-critical applications. However, the heterogeneity between 5G and TSN, along with the non-deterministic nature of 5G, pose challenges in guaran-teeing bounded end-to-end latency. Therefore, an efficient flow scheduling algorithm becomes more crucial for deterministic communication in the 5G-TSN network. The majority of existing research focuses solely on allocating resources for either 5G or TSN, neglecting the investigation of joint resource allocation for both. This paper investigates the periodic flow scheduling problem, focusing on guaranteeing deterministic communication and the joint allocation of heterogeneous resources. We propose a novel heuristic algorithm that considers the urgency of flows, and quantifies heterogeneous resources uniformly to achieve a spatiotemporal load balancing across heterogeneous networks. Evaluation results demonstrate that our algorithm can achieve deterministic end-to-end latency for periodic flows and significantly enhance the schedulability of periodic flow sets.

关键词： Schedules Job shop scheduling 5G mobile communication scheduling algorithms Load management Heterogeneous networks Production facilities Spatiotemporal phenomena Time factors Resource management

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Content-Aware Opportunistic Packet scheduling with Dynamic Routing Algorithm for Multi-path Video Streaming over Mesh Networks*

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Chinese Journal of Electronics 2023年第2期21卷 270-276页

作者： Yongfer Zhang Yunsheng Zhang Chun Yuan Shiyin Qin Zhihai He Digital Media Laboratory School of Computer Science and Engineering Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China The Graduate School at Shenzhen Tsinghua University Shenzhen China Department of Electrical and Computer Engineering University of Missouri Columbia MO U.S.A

Multi-path video streaming over networks emerges as an important application in multimedia communication. Since the efficiency of multi-path video streaming is closely tied to the packet scheduling strategies, in this paper, we develop a content-aware opportunistic packet scheduling scheme with dynamic routing algorithm, which aims to maximize the reconstructed video quality by considering the content diversity of each video packet. To handle the problem of increased reordering delay, a sliding-window-based policy is then developed to find a balance between the reconstructed video quality and the reordering delay, throughout and packet loss. Our extensive experimental results demonstrate that the proposed packet scheduling scheme significantly outperforms EDPF and achieves a good tradeoff between the reconstructed video quality and the reordering time at the receiver side.

关键词： scheduling algorithms Heuristic algorithms Packet loss Receivers Streaming media Routing Dynamic scheduling

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