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Online Menu-Based scheduling of Electric Vehicle Charging

IEEE TRANSACTIONS ON SMART GRID 2025年第3期16卷 2478-2491页

作者： Mathioudaki, Angeliki Tsaousoglou, Georgios Varvarigos, Emmanouel Fotakis, Dimitris Natl Tech Univ Athens Sch Elect & Comp Engn Zografos 15772 Greece Tech Univ Denmark Dept Appl Math & Comp Sci Sect Cognit Syst DK-2800 Lyngby Denmark Athena Res Ctr Archimedes Res Unit Maroussi 15125 Greece

This paper addresses the problem of efficiently scheduling of EV charging requests in a single Charging Station (CS), also taking into consideration the inability of EV users to express their preferences in closed-form utility functions. We consider a paradigm where a menu of possible charging intervals, each associated with an urgency price, is generated online. By letting the EV users pick their most preferable menu option, the proposed algorithm commits on each EV's charging completion time upon its arrival, achieves a near optimal total weighted charging completion time, and prevents the users from strategically misreporting their preferences, while offering a practical and implementable solution to the problem of EVs - charging station interaction. Our experimental evaluation demonstrates that our algorithmic approach performs nearly optimal in a variety of practically relevant scenarios.

关键词： Schedules Electric vehicle charging Electricity Heuristic algorithms scheduling algorithms Minimization Partitioning algorithms Optimization Complexity theory Charging stations Electric vehicles charging scheduling prompt scheduling online algorithms incentive compatibility

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Critical-Task-Driven Multi-Objective Evolutionary Algorithm for scheduling Large-Scale Workflows in Cloud Computing

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Liu, Xiaolu Yao, Feng Xing, Lining Chen, Huangke Zhao, Wei Zheng, Long Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Xidian Univ Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China

Simultaneously optimizing energy consumption and makespan is essential for deploying workflows on cloud platforms. Meanwhile, it poses various challenges, such as multiple types of decision variables and large-scale decision variables. Until now, some studies have adopted multi-objective evolutionary optimization techniques to handle these challenges. But, most relevant studies regard the optimization problem as a black box and do not fully explore the domain knowledge to strengthen the search efficiency. To further answer the above challenges, this study tailors a critical-task-driven evolutionary algorithm, namely CTMOEA, to optimize both energy consumption and makespan by evolving two types of decision variables: 1) mappings from tasks to resources;and 2) task runtime. In particular, an adaptive incentive mechanism is designed to distinguish decision variables with higher criticality and allocate more computing resources to evolve them, thus handling large-scale decision variables in a targeted manner. Moreover, a critical task remapping mechanism is designed to adaptively remap critical tasks onto the same resources as their successor tasks to eliminate data transfer time between them, thus pursuing a simultaneous reduction in energy consumption and makespan. At last, based on 16 real-world workflow traces, we validate the superior overall performance of the proposed CTMOEA by significantly outperforming five baselines on 13 test instances. Also, we conduct an ablation analysis to demonstrate the contributions of the three problem-specific mechanisms to overall performance.

关键词： Cloud computing Energy consumption Optimization Costs scheduling algorithms Data centers Runtime Data transfer Data models Computational modeling workflow scheduling evolutionary computation multi-objective optimization large-scale energy efficient

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Packet scheduling in Multi-Flow Carrier Aggregation With QoS Provisioning: Cross-Layer Design and Performance Analysis

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第2期24卷 507-524页

作者： Alorainy, Abdulaziz King Abdulaziz City Sci & Technol KACST Future Generat Connect & Wireless Sensors Res Ins Riyadh 12354 Saudi Arabia

Multi-flow carrier aggregation (CA) is a key solution for improving data rates in heterogeneous networks (HetNets). Efficient data splitting is important to enhance the performance of multi-flow CA. In this paper, packet scheduling in multi-flow CA is investigated via a cross-layer design approach with comprehensive quality of service (QoS) provisioning. In particular, an approximation of the average queuing delay (AQD) in terms of the average buffers' length and the average packet arrivals is presented. Then, using the presented AQD approximation, various packet scheduling schemes are developed in this paper. First, a low-complexity buffer and link aware heuristic packet scheduling scheme is proposed. Next, in order to reduce backhaul signalling overhead, a hybrid packet scheduling scheme that combines heuristic packet scheduling and random packet scheduling is considered. Then, an optimal packet scheduling scheme, albeit of high computational complexity, is proposed as a benchmark for other packet scheduling schemes. Also, the performances of the proposed packet scheduling schemes are analyzed and various data link layer performance parameters such as AQD, packet loss probability (PLP) and statistical queuing delay bound (SDB) are taken into consideration. Moreover, the complexity and the signalling overhead of the proposed packet scheduling schemes are investigated. Presented numerical results demonstrate that the heuristic packet scheduling scheme can achieve a performance that is similar to the performance of the optimal packet scheduling scheme while overcoming complexity and feasibility issues of the optimal packet scheduling scheme. Also, the hybrid packet scheduling scheme reduces signalling overhead while reaping some of the benefits of using buffer and link aware packet scheduling.

关键词： scheduling algorithms Quality of service Delays Complexity theory Macrocell networks Mobile computing Cross layer design Dual connectivity multi-flow carrier aggregation packet scheduling queuing delay

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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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scheduling algorithms for Tree-based Convergecast Networks with a Random Number of Messages 14

Scheduling Algorithms for Tree-based Convergecast Networks w...

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XIV Int Symposium Problems Redundancy Inform Control Syst

作者： Bakin, Evgeny Evseev, Grigory St Petersburg State Univ Aerosp Instrumentat St Petersburg Russia

ISBN: (纸本)9781479940509

In the paper we consider tree-based L-level convergecast networks, in which leaf nodes correspond to data sources (DS), a root node - to a base station (BS), and other nodes - to intermediate repeaters (IR). At a startup phase each DS generates a random number of messages for transmission to a BS. For such a model a lower bound for an average duration of collision-free convergecast is given. Also we propose a collision-free messages transmission scheduling algorithms for which the lower bound is achieved at L -> infinity.

关键词： trees (mathematics) wireless sensor networks BS DS IR base station collision-free messages transmission scheduling algorithms data sources intermediate repeaters tree-based convergecast networks Control systems Decision support systems Redundancy Repeaters scheduling algorithms Sensors Wireless sensor networks

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ATPSO: Adaptive Task Priority scheduling and Offloading Optimization Scheme for vehicles in harsh environments

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AD HOC NETWORKS 2025年 175卷

作者： Li, Xiguang Zhao, Yuchen Sun, Yunhe Muthanna, Ammar Hawbani, Ammar Zhao, Liang Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Liaoning Peoples R China Peoples Friendship Univ Russia RUDN Univ Moscow 117198 Russia

In harsh environments, vehicle driving risks significantly increase. Safety enhancing applications can mitigate these risks, but can also introduce delay-sensitive and computationally intensive tasks that challenge vehicle data processing and communication. This paper proposes an Adaptive Task Priority scheduling and Offloading Optimization Scheme (ATPSO) based on the Vehicular Edge Computing (VEC) paradigm to improve Quality of Service (QoS) for safety tasks. Firstly, a multifactor prioritization mechanism using environment awareness is developed, leveraging SHapley Additive ExPlanations (SHAP) and XGBoost models to quantify safety priorities from real-time data and assess vehicle risks. A Transformer model dynamically adjusts priority weights to minimize risk. Secondly, the local queue stability problem is addressed as a Lyapunov optimization problem, proposing a multi-queue priority scheduling and distributed resource allocation scheme. Lastly, a Markov Decision Process (MDP) model is constructed to handle dynamic computational offloading, and the Entropy-Enhanced Multi-Agent Soft Actor-Critic (EE-MASAC) algorithm is introduced to optimize offloading strategies and resource allocation. Simulation results demonstrate that ATPSO effectively reduces safety task delays, improves task completion rates, and lowers vehicle risk scores, outperforming existing methods in adaptability and performance, offering a solid foundation for practical deployment of vehicle safety applications.

关键词： Vehicular edge computing Task offloading resource allocation scheduling algorithms Deep reinforcement learning

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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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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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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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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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