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Spatial-Temporal Resource Utilization for Partially Connected Multi-AUV Broadcast scheduling

IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第5期14卷 1321-1325页

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Niyato, Dusit Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing Peoples R China Tsinghua Univ State Key Lab Space Network & Commun Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore

The long propagation delays in underwater acoustic channels can be leveraged to improve communication efficiency using spatial-temporal reuse techniques. This letter focuses on a system of partially connected AUVs that continuously broadcast packets. A central controller gathers the location information of the AUVs and designs a broadcast sequence exploiting propagation delays to minimize the broadcast cycle. Since finding optimal solutions is time-consuming, we propose a scheduling algorithm that uses heuristics based on partial connections to reduce computational complexity. Additionally, we allow collision tolerance to shorten the broadcast cycle further and investigate the trade-off of the shorter broadcast cycle and collision probability, resulting in a scheduling algorithm exhibiting receiving-alignment properties. Simulations demonstrate the effectiveness of the proposed algorithm in reducing broadcast cycles while maintaining low computational complexity.

关键词： Bandwidth scheduling algorithms Media Access Control Electronic mail Protocols Propagation delay Underwater acoustics Linear programming Information science Heuristic algorithms Medium access control propagation delay underwater acoustic network broadcast scheduling algorithm

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Latency Bounds for TSN scheduling in the Presence of Clock Synchronization

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 41-45页

作者： Ghosh, Aviroop Yousefi, Saleh Kunz, Thomas Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Urmia University Department of Electrical and Computer Engineering Urmia57561-51818 Iran

The IEEE 802.1Qbv (80.21Qbv) standard is designed for traffic requiring deterministic and bounded latencies through strict periodic time synchronization, as specified by IEEE 802.1AS standard. However, internal clock drift in devices causes timing misalignment, introducing further challenges to 802.1Qbv scheduling. Existing solutions, using either complex optimization approaches or non-trivial scheduling heuristics, address this by scheduling frame transmissions only once they are guaranteed to have been fully received, even in the presence of clock drifts. However, this approach introduces additional delays that can impact deadline requirements. This letter analytically derives tight end-to-end latency bounds, allowing us to determine if stream deadlines for a given network will be violated without the need to solve for any scheduling algorithms. It also proposes an approach that results in tighter bounds based on information collected from the synchronization process. The analytical results are compared with simulation results, confirming their validity. © 2019 IEEE.

关键词： Synchronization Clocks Delays Standards Schedules Logic gates scheduling algorithms Payloads Jitter Data models

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A reinforcement learning based mobile charging sequence scheduling algorithm for optimal sensing coverage in wireless rechargeable sensor networks

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Journal of Ambient Intelligence and Humanized Computing 2024年第6期15卷 2869-2881页

作者： Li, Jinglin Wang, Haoran Xiao, Wendong School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Beijing Engineering Research Center of Industrial Spectrum Imaging Beijing100083 China Shunde Innovation School University of Science and Technology Beijing Guangdong Shunde528399 China

Mobile charging provides a new way for energy replenishment in the Wireless Rechargeable Sensor Network (WRSN), where the Mobile Charger (MC) is employed for charging nodes sequentially via wireless energy transfer according to the mobile charging sequence scheduling result. Mobile Charging Sequence scheduling for Optimal Sensing Coverage (MCSS-OSC) is a critical problem for providing network application performance;it aims to maximize the Quality of Sensing Coverage (QSC) of the network by optimizing the MC’s mobile charging sequence and remains a challenging problem due to its NP-completeness in nature. In this paper, we propose a novel Improved Q-learning Algorithm (IQA) for MCSS-OSC, where MC is taken as an agent to continuously learn the space of mobile charging strategies through approximate estimation and improve the charging strategy by interacting with the network environment. A novel reward function is designed according to the network sensing coverage contribution to evaluate the MC charging action at each charging time step. In addition, an efficient exploration strategy is also designed by introducing an optimal experience-strengthening mechanism to record the current optimal mobile charging sequence regularly. Extensive simulation results via Matlab2021 software show that IQA is superior to existing heuristic algorithms in network QSC, especially for large-scale networks. This paper provides an efficient solution for WRSN energy management and new ideas for performance optimization of reinforcement learning algorithms. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： scheduling algorithms

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Task scheduling in Cloud Computing: A Comparative Study of algorithms

Task Scheduling in Cloud Computing: A Comparative Study of A...

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Information Technology (OCIT), OITS International Conference on

作者： Harish Kumar Patnaik Niranjan Kumar Ray Suchismita Rout Manas Ranjan Patra School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Dept. of Computer Sc. & Engg. NIST University Berhampur India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

Cloud computing is an environment where resources are utilized on a pay-per-use basis. As users request services, resources are allocated to their associated tasks to ensure timely completion. The cloud service provider aims to optimize resource allocation for these tasks in order to maintain proper utilization of cloud resources. So the proper allocation of task i.e, scheduling of tasks plays a significant role in the performance of the cloud services. This paper offers a comprehensive study of various existing task scheduling algorithms in the cloud environment and an analysis of different scheduling attributes considered in these algorithms.

关键词： Measurement Cloud computing scheduling algorithms scheduling Resource management Information technology

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MSU-TSCH: A Mobile scheduling Updated Algorithm for TSCH in the Internet of Things

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第7期19卷 7978-7985页

作者： Jerbi, Wassim Cheikhrouhou, Omar Guermazi, Abderrahmen Trabelsi, Hafedh Univ Sfax Natl Engn Sch Sfax ENIS CES LAB Sfax 3038 Tunisia Higher Inst Technol Studies Sfax 3038 Tunisia

On top of the low-power IEEE 802.15.4 radio, IEEE created IEEE 802.15.4e time slotted channel hopping (TSCH), an exceptionally efficient, reliable, and predictable time-frequency-enabled medium access control protocol for the industrial sector. However, the IEEE 802.15.4e TSCH specification does not address how scheduling would be created, updated, or maintained. Moreover, TSCH does not support mobility. Although several scheduling techniques have been proposed in the literature to fill this gap, these techniques did not consider networking metrics, such as duty cycle, network capacity, end-to-end delay, and packet delivery ratio. In this article, we develop a new dynamic scheduling approach for TSCH that supports mobility and adapts to topology changes, called mobile scheduling updated TSCH (MSU-TSCH), that attempts to schedule time slots by first picking the closest nodes to the sink. We undertake comprehensive simulations to assess the effectiveness of MSU-TSCH with PRuning-based coloring scheduling, an existing scheduling algorithm in a mobile context. On the one hand, this proposal reduces the received energy consumption by up to 40%. On the other hand, it decreases the average end-to-end latency by 30%.

关键词： IEEE 802.15.4e TSCH industrial wireless sensor networks (IWSNs) Internet of Things (IoT) mobile scheduling updated TSCH (MSU-TSCH) scheduling algorithms

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Efficient User scheduling for Uplink Hybrid Satellite-Terrestrial Communication

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第3期22卷 1885-1899页

作者： Zhu, Lina Bai, Lin Zhou, Lin Choi, Jinho Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Zhejiang Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100083 Peoples R China Beihang Univ Beijing Sch Cyber Sci & Technol Beijing 100083 Peoples R China Beihang Univ Beijing Beijing Lab Gen Aviat Technol Beijing 100083 Peoples R China Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia

Due to increasing demands of seamless connection and massive information exchange across the world, the integrated satellite-terrestrial communication systems develop rapidly. To shed lights on the design of this system, we consider an uplink communication model consisting of a single satellite, a single terrestrial station and multiple ground users. The terrestrial station uses decode-and-forward (DF) to facilitate the communication between ground users and the satellite. The channel between the satellite and the terrestrial station is assumed to be a quasi-static shadowed Rician fading channel, while the channels between the terrestrial station and ground users are assumed to experience independent quasi-static Rayleigh fading. We consider two cases of channel state information (CSI) availability. When perfect CSI is available, we derive the instantaneous achievable sum rate of all ground users and formulate an optimization problem to maximize the sum rate. When only channel distribution information (CDI) is available, we derive a closed-form expression for the outage probability and formulate another optimization problem to minimize the outage probability. Both optimization problems correspond to scheduling algorithms for ground users. For both cases, we propose low-complexity user scheduling algorithms and demonstrate the efficiency of our scheduling algorithms via numerical simulations.

关键词： Satellites Relays scheduling algorithms Uplink scheduling Optimization Wireless communication Hybrid satellite-terrestrial communication systems decode-and-forward user scheduling outage probability space-air-ground integrated networks

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Reliability-Aware and Energy-Efficient Workflow scheduling in IaaS Clouds

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年第3期20卷 2156-2169页

作者： Ye, Lingjuan Xia, Yuanqing Tao, Siyuan Yan, Ce Gao, Runze Zhan, Yufeng Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol Jiaxing Yangtze Delta Reg Acad Jiaxing 314019 Peoples R China

Nowadays, more and more workflow applications with different computing requirements are migrated to clouds and executed with cloud resources. Workflow scheduling becomes a critical problem in the cloud environment, which focuses on meeting various quality of service (QoS) constraints. Workflow reliability and energy consumption are two essential parts in clouds and minimizing energy consumption for scheduling workflow with the reliability constraint is a challenging issue. In response to the challenge, we propose a workflow scheduling algorithm named REWS to reduce energy consumption and satisfy workflow reliability constraints. In REWS, a new sub-reliability constraint prediction strategy is adopted to break down the workflow reliability constraint to task sub-reliability constraints and the effectiveness of this strategy is proved. Moreover, an update method is adopted to adjust the task sub-reliability constraint for reducing energy consumption. In addition, a brief system framework which consists of five parts: workflow analyzer, reliability decomposer, resource manager, workflow scheduler and feedback processer is built to support the algorithm implementation of REWS. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed REWS approach. The results demonstrate the superiority of REWS as compared with the state-of-the-art algorithms. Note to Practitioners-Workflow scheduling is a challenging issue in emerging trends of the cloud environment that focuses on satisfying various QoS constraints. In this paper, we investigate a reliability-aware and energy-efficient workflow scheduling problem in cloud computing. A novel workflow scheduling algorithm called REWS, is designed to reduce the energy consumption and meet the workfolw reliability constraint. The basic idea of REWS is to divide the workflow reliability constraint into task sub-reliability constraints and schedule tasks with an energy-efficient scheduling strategy.

关键词： Reliability Cloud computing Task analysis Energy consumption scheduling scheduling algorithms Reliability engineering Cloud computing workflow scheduling energy consumption workflow reliability constraint

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Delay-Oriented scheduling in 5G Downlink Wireless Networks Based on Reinforcement Learning With Partial Observations

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IEEE-ACM TRANSACTIONS ON NETWORKING 2023年第1期31卷 380-394页

作者： Hao, Yijun Li, Fang Zhao, Cong Yang, Shusen Xi An Jiao Tong Univ Natl Engn Lab Big Data Analyt NEL BDA Xian 710049 Shaanxi Peoples R China Imperial Coll London Dept Comp London SW7 2AZ England Xi An Jiao Tong Univ Natl Engn Lab Big Data Analyt NEL BDA Minist Educ Key Lab Intelligent Networks & Network Secur MOE Xian 710049 Shaanxi Peoples R China Ind Artificial Intelligence Ctr Pazhou Lab Guangzhou 510335 Guangdong Peoples R China

5G wireless networks are expected to satisfy different delay requirements of various traffics by network resource scheduling. Existing scheduling methods perform poorly in practice due to their unrealistic assumption on the access to the full channel state information (CSI) or the explicit mathematical expression of network delay. In this paper, we consider the delay-oriented packet scheduling problem in multi-cell 5G downlink networks with multiple users and traffic types (e.g., FTP, VoIP and video streaming), and formulate it as a partially observable Markov decision process (POMDP). We design a delay-oriented downlink scheduling framework based on deep reinforcement learning (DRL) to autonomously schedule the active traffic flows without the full channel information. Furthermore, a recurrent proximal policy optimization (RPPO) algorithm is proposed to perceive the underlying state and accelerate learning under different time granularities, with the policy gradient theorem under POMDP strictly proved. By incorporating the future traffic information provided by a proposed spatial-temporal prediction algorithm, RPPO can balance the load and achieve lower delay in real-time multi-cell multi-user scenarios. Results of extensive experiments on a realistic 5G simulator demonstrate that our framework significantly outperforms existing approaches in terms of both tail delay and average delay for up to 48% and 41.7%, respectively.

关键词： Delays 5G mobile communication Downlink Optimization scheduling algorithms Job shop scheduling Wireless networks Delay optimization wireless resource allocation packet scheduling partial channel state information deep reinforcement learning recurrent neural network

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Adaptive Cloud Bundle Provisioning and Multi-Workflow scheduling via Coalition Reinforcement Learning

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IEEE TRANSACTIONS ON COMPUTERS 2023年第4期72卷 1041-1054页

作者： Wang, Xiaogang Cao, Jian Buyya, Rajkumar Univ Melbourne Sch Comp & Informat Syst CLOUDS Lab Melbourne Vic 3010 Australia Shanghai Dianji Univ Sch Elect & Informat Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

The efficient cloud resource provisioning for the execution of complex workflow applications has always been one of the important research issues. Most of the existing approaches focus on the resource provisioning of single-type virtual machine (VM) instances for the single or multiple workflows, while few consider the situation of provisioning multi-type VM instances simultaneously. As a result, the executing performance of complex workflows degrades. Different from the existing work, this paper proposes an adaptive cloud bundle provisioning and multi-workflow scheduling model to dynamically perform both the horizontal and vertical cloud resource scaling on multi-type VM instances for the execution of complex workflows. Among the model, a depth-first-search coalition reinforcement learning (DFSCRL) provisioning policy is presented to realize the resource scaling, which integrates the physical machine (PM) coalition formation with the Q-learning algorithm, then dynamically generates an optimal multi-type VM instance bundle from the PM coalition, and finally provisions these instances to the concurrent execution of multiple workflows. The theoretical proofs and various experiments with the multifaceted metrics demonstrate that the performance of the proposed algorithms is superior to that of the state-of-the-art relevant policies.

关键词： Task analysis Dynamic scheduling Cloud computing Heuristic algorithms Adaptation models scheduling algorithms Costs coalition formation depth-first-search multi -type VM instance bundle provisioning multi-workflow scheduling reinforcement learning

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scheduling Policies for Stability and Optimal Server Running Cost in Cloud Computing Platforms

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第1期20卷 229-245页

作者： Haritha, K. Singh, Chandramani Indian Inst Sci Dept Elect Syst Engn Bengaluru 560012 India

We propose throughput and cost optimal job scheduling algorithms in cloud computing platforms offering Infrastructure as a Service. We first consider online job migration and propose job scheduling algorithms to minimize job migration and server running costs. We consider algorithms that assume knowledge of job-size on the arrival of jobs. We characterize the optimal cost subject to system stability. We develop a drift-plus-penalty framework based algorithm that can achieve optimal cost arbitrarily closely. Specifically, this algorithm yields a tradeoff between delay and costs. We then relax the job-size knowledge assumption and give an algorithm that uses readily offered service to the jobs. We show that this algorithm gives order-wise identical cost as the job size based algorithm. Later, we consider offline job migration that incurs migration delays. We again present throughput optimal algorithms that minimize server running cost. We illustrate the performance of the proposed algorithms and compare these to the existing algorithms via simulation.

关键词： Servers Costs Cloud computing Throughput scheduling algorithms Delays Clustering algorithms Cloud computing platforms drift-plus-penalty framework migration delay reconfiguration scheduling

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