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Response Time Analysis and Optimal Priority Assignment for Global Non-Preemptive Fixed-Priority Rigid Gang scheduling

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第3期36卷 455-470页

作者： Sun, Binqi Kloda, Tomasz Chen, Jiyang Lu, Cen Caccamo, Marco Tech Univ Munich D-85748 Munich Germany Univ Toulouse AAS CNRS INSA F-31000 Toulouse France

Non-preemptive rigid gang scheduling combines the efficiency of parallel execution with the reduced overhead of non-preemptive scheduling. This approach is particularly advantageous for parallel hardware accelerators, such as Google's Edge Tensor Processing Unit (TPU), which is widely used for deep neural network (DNN) inference on embedded systems. This paper studies sporadic global non-preemptive fixed-priority (NP-FP) rigid gang scheduling, which is well-suited for DNN applications in Edge TPU pipelines. Each gang task spawns a fixed number of threads that must execute concurrently across distinct processing units. We introduce the first carry-in limitation technique specifically designed for gang task response time analysis, addressing the unique challenges posed by intra-task parallelism. This technique is formulated as a generalized knapsack problem, and we develop both a linear programming relaxation and a dynamic programming approach to solve it under different time complexities. Additionally, we propose the first optimal priority assignment policy for NP-FP gang schedulability tests. Our proposed schedulability analysis and optimal priority assignment policy are evaluated through extensive experiments, including both synthetic task sets and a case study using DNN benchmarks on commercial off-the-shelf Edge TPU accelerators. The results demonstrate that the proposed approaches effectively enhance the state-of-the-art global NP-FP gang schedulability tests, achieving improvements of up to 57.9% for synthetic task sets and 76.7% for Edge TPU benchmarks. Furthermore, we conduct an ablations study to examine the impact of different algorithmic components in the proposed technique, providing valuable insights for future research.

关键词： Optimal scheduling Artificial neural networks scheduling Time factors Pipeline processing scheduling algorithms Real-time systems Interference Benchmark testing Sun Gang scheduling response time analysis optimal priority assignment Edge TPU real-time system

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Earth Observation Satellite Downlink scheduling With Satellite-Ground Optical Communication Links

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 2281-2294页

作者： Li, Hai Li, Yongjun Liu, Yuanhao Deng, Boyu Li, Yu Li, Xin Zhao, Shanghong Air Force Engn Univ Coll Informat & Nav Xian 710077 Peoples R China Chinese Acad Mil Sci Beijing 100071 Peoples R China Natl Univ Def Technol Coll Informat & Commun Wuhan 430000 Peoples R China

Consistent growth in the number of high-resolution Earth observation satellites (EOSs) poses a significant challenge to the downlink scheduling problem for observation data. Satellite-to-ground optical communication is considered promising for downloading massive observation data due to its large communication capacity. To improve the downlink transmission capacity of the EOS observation system, this article focuses on the EOS downlink scheduling problem with satellite-ground optical communication links (EDSPO). First, we establish a mathematical model considering many practical constraints for EDSPO to maximize the profit of downloaded observation data. To tackle this complex scheduling problem, a downlink scheduling algorithm combining with the adaptive large neighborhood search algorithm and the tabu search algorithm (ALNS_TS) is then proposed. In ALNS_TS, a heuristic solution initialization is developed, five removal operators and five repair operators are designed based on the heuristic characteristics of EDSPO, and a task insert algorithm is proposed for repair operators. Moreover, ALNS_TS introduces the tabu value for each downlink transmission task to avoid repeated searches in the neighborhood of the same solution. Finally, extensive experiments are carried out to verify the effectiveness of the ALNS_TS and the superiority of the satellite-ground optical communication link. Simulation results show that the ALNS_TS performs better in profits and the number of completed tasks compared to the existing downlink scheduling algorithms. Besides, the satellite-ground optical communication link has been proven effective in improving the downlink transmission capacity of the EOS observation system.

关键词： Downlink Earth Observing System Optical fiber communication Microwave antennas Satellites Transmitting antennas scheduling algorithms Adaptive large neighborhood search (ALNS) Earth observation satellites (EOSs) downlink scheduling problem satellite-ground optical communication link tabu search (TS)

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Towards Secure and Efficient Data scheduling for Vehicular Social Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 1551-1569页

作者： Xia, Youhua Zhang, Tiehua Jin, Jiong He, Ying Yu, F. Richard Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 200070 Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Carleton Univ Ottawa ON K1S 5B6 Canada

Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks. Contemporary research predominantly centers on crafting cooperative scheduling algorithms tailored for vehicular networks. Notwithstanding, the intricacies of orchestrating scheduling in vehicular social networks both effectively and efficiently remain a formidable task. This paper introduces an innovative learning-based algorithm for scheduling data transmission that prioritizes efficiency and security within vehicular social networks. The algorithm first uses a specifically constructed neural network to enhance data processing capabilities. After this, it incorporates a Q-learning paradigm during the data transmission phase to optimize the information exchange, the privacy of which is safeguarded by differential privacy through the communication process. Comparative experiments demonstrate the superior performance of the proposed Q-learning enhanced scheduling algorithm relative to existing state-of-the-art scheduling algorithms in the context of vehicular social networks.

关键词： Social networking (online) scheduling algorithms Optimal scheduling Data communication Collaboration Computer architecture Real-time systems Data scheduling multi-layer perceptron privacy-preserving Q-learning vehicular social networks

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Optimization-based resource scheduling techniques in cloud computing environment: A review of scientific workflows and future directions

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Kathole, Atul B. Vhatkar, Kapil Lonare, Savita Kshirsagar, Aniruddha P. Dr DY Patil Inst Technol Dept Comp Engn Pune 411018 Maharashtra India Karmaveer Bhaurao Patil Coll Engn Dept Comp Sci & Engn Satara 415001 Maharashtra India

Due to the rapid growth of heterogeneous nature and demand requests over cloud resources, resource scheduling has become the most prominent issue in cloud computing. Thus, the cloud has offered elasticity, uncertainty, and dynamism depending on the services to the users on the internet. Here, the inefficient scheduling approaches have faced difficulty over resources that are being underutilized and over-utilized and it led to the degradation of service performance. The major intention of scheduling is to distribute the tasks between the cloud, where the scheduling algorithm has neglected the issues of imbalance. The scheduling algorithm has optimized the key performance indicators parameters such as resource utilization, cost, energy consumption, availability, reliability, makespan time, response time, and so on. Concerning this, many conventional scheduling algorithms are proposed. This research has depicted a broad literature analysis of resource scheduling in a cloud environment to help researchers detect adequate scheduling algorithms. The current status of the cloud resource allocation strategies is split into several categories. The methodological analysis of resource scheduling is presented in this research work. The optimization and meta-heuristic algorithms used in this work are also discussed. The parameters utilized for estimating the performance of the system are also analyzed. In addition, the benefits and challenges of the resource scheduling algorithms are also discussed. Finally, the research gap analysis is given, which is helpful for researchers in the field of cloud computing to make further improvements in scheduling techniques.

关键词： Resource scheduling Cloud computing environment scheduling algorithms Parameters for optimization Performance measures Research gaps and future challenges

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Qin: Unified Hierarchical Cluster-Node scheduling for Heterogeneous Datacenters

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2025年第1期10卷 39-56页

作者： Guan, Wenkai Ababei, Cristinel Univ Minnesota Div Sci & Math Morris MN 56267 USA Marquette Univ Dept Elect & Comp Engn Milwaukee WI 53233 USA

Energy efficiency is among the most important challenges for computing. There has been an increasing gap between the rate at which the performance of processors has been improving and the lower rate of improvement in energy efficiency. This paper answers the question of how to reduce energy usage in heterogeneous datacenters. It proposes a unified hierarchical scheduling using a D-Choices technique, which considers interference and heterogeneity. Heterogeneity comes from servers' continuous upgrades and the integrated high-performance "big" and energy-efficient "little" cores. This results in datacenters becoming more heterogeneous and traditional job scheduling algorithms become suboptimal. To this end, we present a two-level hierarchical scheduler for datacenters that exploits increased server heterogeneity. It combines in a unified approach cluster and node level scheduling algorithms, and it can consider specific optimization objectives including job completion time, energy usage, and energy-delay-product (EDP). Its novelty lies in the unified approach and in modeling interference and heterogeneity. Experiments on a research cluster found that the proposed approach outperforms state-of-the-art schedulers by around 10% in job completion time, 39% in energy usage, and 42% in EDP. This paper demonstrated a unified approach as a promising direction in optimizing energy and performance for heterogeneous datacenters.

关键词： Servers Optimization Collaboration Interference scheduling Motion pictures scheduling algorithms Collaborative filtering datacenters heterogeneity Kalman filtering scheduling

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End-Edge-Cloud Heterogeneous Resources scheduling Method Based on RNN and Particle Swarm Optimization

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1664-1676页

作者： Wu, Haijie Shen, Wangbo Lin, Weiwei Li, Wei Li, Keqin South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Dept New Networks Shenzhen 518055 Peoples R China Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia State Univ New York New Paltz Dept Comp Sci New Paltz NY 12561 USA

Task scheduling in cloud computing is a challenging but crucial task for ensuring service quality and load balance. Mainstream scheduling algorithms, such as heuristic algorithms and reinforcement learning, have made progress in this area. However, online task scheduling algorithms, such as reinforcement learning, can pose computational challenges in scenarios with limited computational power and heterogeneous resources. Heuristic algorithms, which are more suitable for offline scheduling where the types and quantities of tasks are known in advance, also require substantial computational resources for online scheduling. In this work, we propose the end-edge-cloud (EEC) heterogeneous resources scheduling method (EHRSM) based on a recurrent neural network (RNN) model and particle swarm optimization (PSO). EHRSM uses an RNN model trained on a dataset generated by dynamic programming to recognize and cache online tasks, efficiently transforming online task scheduling into offline scheduling. Additionally, a PSO algorithm with Cantor expansion (CE) for coding optimization is used to complete the offline scheduling. Experimental results show that the method is effective in converting online scheduling to offline scheduling, reducing the average task completion time and waiting time. Compared with existing online scheduling methods, EHRSM reduces task completion time by up to 48.24%.

关键词： Processor scheduling scheduling Job shop scheduling Cloud computing Collaboration Servers Training scheduling algorithms Recurrent neural networks Performance evaluation Cantor expansion end-edge-cloud particle swarm optimization recurrent neural network

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Efficient Prefetch and Issue scheduling Approaches for Simultaneous Multithreading Applied to Superscalar RISC-V Processor

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IEEE ACCESS 2025年 13卷 29177-29189页

作者： Ribo, Hananya Greenberg, Shlomo Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel Sami Shamoon Coll Engn Comp Sci Dept IL-8410802 Beer Sheva Israel

Thread level parallelism (TLP) is a common approach to achieve parallelism where Instruction level parallelism (ILP) is insufficient. Hardware multithreading is a prevalent approach in the micro-architecture layer for tolerating long events such as memory access, mispredictions, and accelerators latencies by utilizing idle cycles and avoiding CPU stalling. Multithreading architectures are commonly used across many processors and various embedded edge devices to improve performance. This work suggests a new 2-way superscalar in-order Multithreading (MT) micro-architecture applied to a superscalar RISC-V processor. This work also proposes efficient prefetch-scheduling and Issue-scheduling algorithms for Simultaneous Multithreading (SMT) processors. The suggested micro-architecture was implemented on an FPGA chip. The results show that the proposed prefetch-scheduling and issue-scheduling algorithms outperform the 2-way superscalar Fine-Grained Multi-Threaded (FGMT) and the Coarse-Grained Multi-Threaded (CGMT) approaches. The proposed architecture is evaluated using the standard MiBench and RISCV benchmarks, demonstrating an average processor core utilization improvement of up to 52% in terms of IPC using four threads.

关键词： Instruction sets Pipelines Multithreading Hardware Prefetching Computer architecture scheduling scheduling algorithms Multicore processing Registers Hardware multithreading prefetch scheduling issue scheduling superscalar RISC-V in-order pipeline

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CECF: A DNN-Based Energy-Efficient Cloud-Edge Collaboration Framework for Intelligent Workload scheduling in 6G-Enabled Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Lu, Yao Liu, Lu Panneerselvam, John Gu, Jiayan Garraghan, Peter Min, Geyong Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF England Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230000 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England

The rapid growth of Internet of Vehicle (IoV) devices and Artificial Intelligence (AI) applications has accelerated the adoption of Cloud and Edge Computing. The advent of sixth-generation mobile communication technology (6G) further facilitates the deployment of Cloud-Edge collaborative computing in large-scale Intelligent Transportation Systems (ITS). Effective ITS must efficiently handle both latency-sensitive tasks (e.g., obstacle detection, traffic signal recognition) and computationally intensive tasks (e.g., path optimization, traffic flow prediction). However, existing Cloud-Edge collaborative frameworks struggle to accurately classify diverse workloads and provide efficient low-latency processing, leading to energy inefficiencies and task failures. To address these challenges, this paper introduces a Deep Learning-based Cloud-Edge Collaboration Framework (CECF) designed to optimize energy conservation in Cloud and Edge environments. CECF employs a DNN-based classifier to categorize workloads for processing in the Cloud or Edge. The classified tasks are managed by a dedicated Cloud scheduler (DSGA) and an Edge scheduler (EA-DFPSO), respectively. To enhance scheduling efficiency for highly variable Cloud tasks, DSGA incorporates a novel self-adaptive mutation algorithm and a random point fixed distance crossover method. Extensive evaluations using real-world workload traces demonstrate that CECF achieves up to a 8.5% improvement in system reliability and reduces energy consumption by 35.88% compared to baseline approaches.

关键词： Cloud computing Servers Energy consumption Collaboration Energy efficiency Image edge detection Heuristic algorithms scheduling algorithms Real-time systems Virtual machines Cloud-edge collaboration computing Internet of Vehicles (IoV) deep learning intelligent transportation systems

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Partitioned scheduling With Shared Resources on Imprecise Mixed-Criticality Multiprocessor Systems

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第1期44卷 65-76页

作者： Zhang, Yi-Wen Ma, Jin-Peng Gu, Zonghua Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Hofstra Univ Dept Comp Sci Hempstead NY 11549 USA Umea Univ Dept Appl Phys & Elect S-90187 Umea Sweden

Both resource access protocols and real-time scheduling algorithms have been extensively studied in classic embedded real-time systems. However, there has been relatively little attention given to the resource access protocol and real-time scheduling algorithms in mixed-criticality systems. In this article, we pay attention to the problem of scheduling an imprecise mixed-criticality (IMC) taskset on a multiprocessor platform with shared resources. First, we propose an IMC with MSRP (IMC-MSRP) resource access protocol, which ensures mutually exclusive access to the shared resources for the tasks. Second, we propose the schedulability test based on the IMC-multiprocessor stack resource policy (MSRP) for a given task-to-processor mapping method. Third, we propose a feasible task-to-processor mapping algorithm called resource-aware criticality-unaware worst-fit decreasing (RA-CU-WFD), which first assigns tasks sharing the same resources to the same processor to reduce the global waiting time of the tasks and thus improve the schedulability ratio of the system. And then assigns tasks based on the criticality-unaware worst-fit decreasing (CU-WFD) algorithm. Finally, we conduct experiments using the synthetic tasksets, and the experimental results show that the RA-CU-WFD outperforms the other approaches in terms of the schedulability ratio.

关键词： Task analysis Program processors Job shop scheduling Access protocols scheduling algorithms Quality of service Partitioning algorithms Imprecise mixed-criticality (IMC) multicore partitioned scheduling shared resource

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Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems

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Discover Internet of Things 2025年第1期5卷 1-13页

作者： Zhou, Fei Zhao, Lihong Ding, Xiaomei Wang, Shuqin Anhui Wenda University of Information Engineering Anhui Hefei231201 China

This paper presents an improved Deep Deterministic Policy Gradient (DDPG) algorithm for task scheduling in Mobile Edge Computing (MEC) systems, focusing on reducing latency and energy consumption. The enhanced DDPG incorporates a soft update mechanism for target network parameters and an experience replay buffer to improve stability and training efficiency. A composite reward function, considering both latency and energy consumption, is designed and compared in linear and nonlinear forms. Experimental results demonstrate that the nonlinear reward function achieves faster convergence and better system performance. The improved DDPG outperforms traditional algorithms like Shortest Job First (SJF), Round Robin (RR), and Earliest Deadline First (EDF) in terms of latency and energy consumption across different task loads. At a task load of 10, the improved DDPG achieved a latency of 250 ms and energy consumption of 965 J, significantly lower than other algorithms. Ablation experiments further confirm the positive impact of each enhancement, showing that the combination of soft updates and experience replay buffers significantly reduces both latency and energy consumption. The study concludes that the improved DDPG algorithm provides an effective solution for task scheduling in MEC systems, offering robustness and adaptability in complex network environments. Future research will focus on real-world deployment and further optimization of the algorithm for diverse MEC scenarios. © The Author(s) 2025.

关键词： scheduling algorithms

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