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Joint Robust Power Control and Task scheduling for Vehicular Offloading in Cloud-Assisted MEC Networks

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 698-709页

作者： Liu, Zhixin Su, Jiawei Wei, Jianshuai Chen, Wenxuan Chan, Kit Yan Yuan, Yazhou Guan, Xinping Yanshan Univ Sch Elect Engn Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA 6102 Australia Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

Leveraging the abundance of computational resources, the cloud-edge collaborative architecture provide stronger data processing capabilities for vehicular networks, which not only significantly enhances the timeliness of offloading operations for delay-sensitive tasks but also substantially mitigates resource expenditure associated with non-delay-sensitive tasks. Addressing the communication scenarios characterized by diverse task types, this paper introduces cloud-assisted mobile-edge computing (C-MEC) networks, underscored by a novel optimization scheme. The scheme incorporates a utility function that is correlated with offloading delays during the transmission and computation phases, effectively balancing resource allocations and enhancing the operational efficiency of vehicular networks. However, the mobility of vehicles introduces channel uncertainty, adversely affecting the offloading stability of C-MEC networks. To develop a practical channel model, a first-order Markov process is employed, taking into account vehicular mobility. Additionally, probability constraints regarding co-channel interference are imposed on signal links to ensure the offloading quality. The Bernstein approximation method is utilized to transform the original interference constraints into a tractable form, and the Successive Convex Approximation (SCA) technique is meticulously applied to address the non-convex robust optimization problem. Furthermore, this paper proposes a robust iterative algorithm to ascertain optimal power control and task scheduling strategies. Numerical simulations are conducted to assess the effective of the proposed algorithm against benchmark methods, with a particular focus on robustness in task offloading and utility in resource allocation.

关键词： Cloud computing Servers Computer architecture Resource management Optimization Delays Power control Robustness Indexes scheduling algorithms Bernstein method cloud-assisted MEC offloading delay robust resource allocation vehicular networks

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A resource-aware workload scheduling method for unbalanced GEMMs on GPUs

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COMPUTER JOURNAL 2024年第3期68卷 273-282页

作者： Liu, Hangda Diao, Boyu Chen, Wenxin Xu, Yongjun Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China

GEMM (General Matrix Multiplication) serves as a fundamental operator for deep learning computations. Especially in attention-based deep learning models, such as Bert, GPT, and SAM, the sizes of matrices involved in GEMMs exhibit an unbalanced distribution due to the variable input, resulting in the low utilization of hardware resources. To address the issue, this paper proposes inserting a novel GEMM processing layer into the deep learning inference stack and using an adaptive load balancing method to partition and schedule GEMM computation tasks. The method is implemented with hardware runtime resource information, such as the occupancy of computing units, etc. Experiment results show the remarkable performance of our method in unbalanced input GEMM scenarios, achieving an average performance improvement of 2.3x. The method also performs well in attention-based models (GPT-2 and SAM), achieving an average inference speed improvement of 1.1x. These findings highlight the effectiveness of resource-aware algorithm optimization, especially for computation task scheduling.

关键词： scheduling algorithms

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Adaptive Task scheduling in Digital Twin Empowered Cloud-Native Vehicular Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第6期73卷 8973-8987页

作者： Tan, Xiaobin Wang, Mingyang Wang, Tao Zheng, Quan Wu, Jun Yang, Jian Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230094 Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei 230026 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

Intelligent driving has advanced significantly in recent decades, paving the path for the transportation of the future. Digital twin (DT) technology, which can bridge the physical and virtual space gaps in real time, plays an important role in the collaboration of vehicles and roads for intelligent driving. In this paper, we design the Digital Twin empowered Cloud-native Vehicular Networks (DT-CVN) architecture as well as the workflow for task execution aiming at executing intelligent driving tasks efficiently and reliably. In DT-CVN, we propose a design and implementation scheme for digital twins, in which different modules of the same digital twin entity can be deployed in different network locations in a distributed manner by taking advantage of the distributed features of microservices based on cloud-native technology. Furthermore, we design the modules reuse and requests aggregation mechanisms of digital twins invocation for task scheduling in DT-CVN, which can improve its efficiency even further. Then we model the task scheduling in DT-CVN into a combinational optimization problem and propose a deep reinforcement learning (DRL) based adaptive task scheduling algorithm. Simulation results show that the proposed scheme can improve the efficiency of task scheduling while reducing energy consumption.

关键词： Digital twins Task analysis Collaboration Cloud computing Computer architecture Edge computing scheduling algorithms Intelligent driving digital twin vehicular networks cloud-native task scheduling

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Adaptive Framing and Virtual Channel scheduling Algorithm Based on Advanced Orbiting System for Consumer Sustainability in Industry 5.0

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第2期70卷 4882-4893页

作者： Bie, Yuxia Zhang, Zhongyuan Srivastava, Gautam Hu, Zhi Laghari, Asif Ali Sampedro, Gabriel Avelino Abbas, Sidra Shenyang Aerosp Univ Coll Elect Informat Engn Shenyang 110136 Peoples R China Brandon Univ Dept Math & Comp Sci Brandon MB R7A 6A9 Canada China Med Univ Res Ctr Interneural Comp Taichung 404 Taiwan Lebanese Amer Univ Dept Comp Sci & Math Beirut 03797 Lebanon Shenyang Normal Univ Software Coll Shenyang 110134 Peoples R China Univ Philippines Open Univ Fac Informat & Commun Studies Banos 4031 Philippines Salle Univ Ctr Computat Imaging & Visual Innovat Manila 1004 Philippines COMSATS Univ Islamabad Dept Comp Sci Islamabad 56300 Pakistan

Based on the development needs of Industry 5.0, Advanced On-orbit Systems (AOS) can be integrated with terrestrial 5G networks, with large-scale Internet of Things (IoT) links as well as with flexible deployment and resource optimization, enabling efficient transmission of multiple types of industrial data, human-machine collaboration, and improving the flexibility, innovation as well as efficiency of production processes. This paper first proposes an AOS adaptive framing algorithm based on an optimization threshold. The algorithm adaptively adjusts frame waiting time according to packet arrival conditions and optimizes the frame waiting time threshold using a differential evolution algorithm. Furthermore, an AOS virtual channel scheduling algorithm based on a Deep QNetwork (DQN) is proposed. The algorithm considers the service priority, scheduling delay and frame residual to find the optimal virtual channel scheduling order. Through simulation, it can be seen that the adaptive framing algorithm based on optimized threshold values can effectively reduce average framing time and average packet delay while ensuring efficiency of frame reuse. Moreover, the virtual channel scheduling algorithm based on DQN can better meet the needs of the network, effectively reducing average scheduling delay and frame residual. The combination of AOS framing and virtual channel scheduling can improve transmission efficiency and optimize system performance.

关键词： scheduling Delays scheduling algorithms Multiplexing Job shop scheduling Heuristic algorithms Adaptation models AOS adaptive frame generation algorithm differential evolution algorithm virtual channel scheduling DQN

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scheduling Networked Guidance Data Transmissions Based on a New Event-Triggering Approach

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第1期11卷 30-41页

作者： Ristevski, Stefan Yucelen, Tansel Muse, Jonathan A. Combine Control Syst AB S-41755 Lund Sweden Univ S Florida Dept Mech Engn Tampa FL 33620 USA Univ S Florida Lab Auton Control Informat & Syst Tampa FL 33620 USA AF Res Lab Aerosp Syst Directorate Autonomous Control Branch Beavercreek OH 45433 USA

This article focuses on reducing wireless network utilization between an operator (e.g., a ground station) and a controlled dynamical system (e.g., a vehicle equipped with a feedback control algorithm), where the goal of the operator is to provide guidance commands (e.g., trajectory information) to the controlled dynamical system for command following. To this end, we propose a new event-triggering approach for scheduling networked guidance data transmissions between the operator and the controlled dynamical system. The proposed approach utilizes a reference model available to the operator and we explore three event-triggering rules based on an error signal, which is a function of the state of this reference model and the guidance commands. Specifically, the first rule is a standard one in the literature and it is predicated on exchanging sampled guidance commands with the controlled dynamical system. Unlike existing results in the literature, the second and the third rules are, respectively, predicated on exchanging approximated curve-fitted guidance command functions, which are estimated using the current guidance command value and future waypoints, and exact guidance command functions, which are valid over a time-interval. We further generalize our results to address the presence of system uncertainties using adaptive control theory. In addition to providing detailed stability analysis, a numerical example is also given to show that the second and the third rules achieve less guidance data transmissions (i.e., events) between the operator and the controlled dynamical system.

关键词： Adaptive control ground support networked control systems remotely guided vehicles scheduling algorithms uncertain systems

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FlowValve⁺: Multi-Queue Packet scheduling Framework on SoC-Based SmartNICs

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2444-2458页

作者： Xi, Shaoke Li, Fuliang Hu, Lingxiang Wang, Xingwei Ren, Kui Zhejiang Univ Sch Cyber Sci & Technol State Key Lab Blockchain & Data Secur Hangzhou 310058 Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China

Enforcing scheduling policies with software schedulers at end-hosts leads to high CPU consumption, low throughput, and inaccuracies. To address these issues, offloading packet schedulers to the network interface card presents a promising research direction. However, existing attempts suffer from inflexible on-NIC scheduling that cannot execute complex hierarchies of network policies. In this article, we propose FlowValve(+), a general framework for multi-queue packet scheduling on SoC-based SmartNICs. The key insight behind FlowValve(+) is to abstract the inherent queueing system as a single FIFO queue and perform specialized tail drop to mix the FIFO queue with expected flow proportions. FlowValve(+) leverages hardware accelerations to produce high throughput while substantially reducing CPU and memory usage on end-hosts. We prototype FlowValve(+) on Netronome Agilio 40GbE and NVIDIA Bluefield-2 100GbE SmartNICs to demonstrate its ability to accurately enforce network policies while driving TCP traffic at 40 Gbps and 80 Gbps on both platforms, respectively. Moreover, FlowValve(+) can save two CPU cores compared to DPDK packet schedulers.

关键词： scheduling scheduling algorithms Bandwidth Software Random access memory Quality of service Throughput SoC-based SmartNICs hardware acceleration multi-queue packet scheduling

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An Online scheduling Framework for Multiple TBD Flows in Intelligent Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第1期25卷 1054-1068页

作者： Peng, Yuhuai Wang, Chenlu Wen, Wu Wen, Songye Xiong, Xuanrui Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Chongqing Univ Posts & Telecommun Informat & Commun Inst Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

In intelligent transportation systems, efficient traffic management and population monitoring is attributed to the real-time scheduling of multiple Transportation Big Data (TBD) flows, which strongly supported risk assessment and control during the COVID-19 pandemic. However, as a complex and heterogeneous network, it is difficult to meet the priority and real-time requirements of multi-modal TBD flow scheduling. To improve the real-time performance of TBD flow scheduling, a scheme for Time-Triggered (TT) flow scheduling and Audio-Video Bridging (AVB) flow scheduling is proposed. First, an online scheduling method for TT flows (RFSD) is proposed, which uses Lion Swarm Optimization (LSO) algorithm for priority assignment and dynamic queues to adjust the scheduling order in real time. It ensures fairness in scheduling and effectively improves the utilization of time slot resources. Furthermore, an online scheduling method for AVB flows (RFSU) is proposed, which uses the Imperialist Competitive Algorithm (ICA) to construct the utility function for evaluating the scheduling value of AVB flows, effectively increasing the throughput of AVB flows. Finally, extensive experiments show that RFSD increases successful scheduling by 22% over the PAS algorithm. Compared to the TTA algorithm, RFSU achieves a 24% reduction in average delay and a 27% reduction in jitter.

关键词： scheduling Real-time systems Transportation Heuristic algorithms Job shop scheduling Switches scheduling algorithms Intelligent transportation network transportation big data online scheduling framework hybrid TBD flow smart city

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A Survey on scheduling Techniques in Computing and Network Convergence

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2024年第1期26卷 160-195页

作者： Tang, Shujiong Yu, Yue Wang, Hui Wang, Guiliang Chen, Wuhui Xu, Zenglin Guo, Song Gao, Wen Univ Elect Sci & Technol China Dept Comp Sci & Engn Chengdu 611731 Peoples R China Pengcheng Lab Dept Networked Intelligence Shenzhen 518055 Peoples R China Sun Yat sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Sun Yat sen Univ Natl Engn Res Ctr Digital Life Guangzhou 510006 Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Peking Univ Sch Comp Sci Beijing 100084 Peoples R China Peng Cheng Lab Dept Networked Intelligence Shenzhen 518052 Peoples R China

The computing demand for massive applications has led to the ubiquitous deployment of computing power. This trend results in the urgent need for higher-level computing resource scheduling services. The Computing and Network Convergence (CNC), a new type of infrastructure, has become a hot topic. To realize the visions of CNC, such as computing-network integration, ubiquitous collaboration, latency-free, and ready-to-use, an intelligent scheduling strategy for CNC should integrate and collaborate with the network. However, the Computing and Network Convergence is built on the cloud, edge, and endless terminals, making the scheduling problem more difficult due to its wide-area requests, available flexibility arrangements, interconnections, and resource adaptations. In view of this, in this survey, we comprehensively review the literature on scheduling in various scenarios. We cover the scheduling problem of Computing and Network Convergence from heterogeneous resources, multiple-objective optimization, and diverse tasks. Possible explanations and implications are discussed. Finally, we point out important challenges for future work.

关键词： Processor scheduling scheduling Task analysis Surveys Cloud computing scheduling algorithms Convergence Computing and network convergence scheduling resource allocation Internet technology

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scheduling Workflows With Limited Budget to Cloud Server and Serverless Resources

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第4期17卷 1766-1779页

作者： Zhang, Jinquan Li, Xiaoping Chen, Long Ruiz, Ruben Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 211189 Peoples R China Univ Politecn Valencia Ciudad Politecn Innovac Grp Sistemas Optimizac Aplicada Inst Tecnol Informat Valencia 46021 Spain

Serverless functions (SFs) and on-demand virtual machines (VMs) are common cloud resources for scientific workflow applications, which are widespread in many fields. SFs are paid by actual running time with higher unit costs and higher resource utilization than VMs which are paid by billing time units. Generally, each application is executed on a limited budget. In this article, we study the challenging cloud workflow scheduling problem with a limited budget to minimize makespan in a hybridization of SFs and on-demand VMs for which the BCWS (Budget Constrained Workflow scheduling) algorithm is proposed. Methods are developed to determine the task execution order, rent cloud resources and map tasks to resources respectively. Together with initial schedule construction and schedule improvement policies, these procedures are repeatedly applied in BCWS. The proposed algorithm is evaluated by comparing it to existing algorithms for similar problems over a comprehensive set of workflow instances. Experimental results show that the proposed algorithm significantly reduces the makespan with a hybrid configuration of VMs and SFs compared to the server only or the serverless only configurations and outperforms the compared algorithms which are the best existing ones for similar problems.

关键词： Task analysis Costs Cloud computing Servers Schedules scheduling algorithms scheduling Scientific workflow scheduling budget serverless function critical path

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Application scheduling With Multiplexed Sensing of Monitoring Points in Multi-Purpose IoT Wireless Sensor Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第1期21卷 729-744页

作者： Cavdar, Mustafa Can Korpeoglu, Ibrahim Ulusoy, Ozgur Bilkent Univ Dept Comp Engn TR-06800 Ankara Turkiye

Wireless sensor networks (WSNs) play a crucial role in Internet-of-Things (IoT) systems serving a variety of applications. They gather data from specific sensor nodes and transmit it to remote units for processing. When multiple applications share a WSN infrastructure, efficient scheduling becomes vital. In our research, we address the problem of application scheduling in WSNs. Specifically, we focus on scenarios where applications request data from monitoring points within the coverage area of a WSN. We propose a shared-data approach that reduces the network's sensing and communication load by allowing multiple applications to use the same sensing data. To tackle the scheduling challenge, we introduce a genetic algorithm named GABAS and three greedy algorithms: LMPF, LMSF, and LTSF. These algorithms determine the order in which applications are admitted to the WSN infrastructure, considering various criteria. To assess the performance of our algorithms, we conducted extensive simulation experiments and compared them with standard scheduling methods. We also evaluated the performance of GABAS as compared to another genetic scheduling algorithm that has recently appeared in the literature. The overall experimental results show that the methods we propose outperform the compared approaches across various metrics, namely makespan, turnaround time, waiting time, and successful execution rate. In particular, our genetic algorithm proves to be highly effective in scheduling applications and optimizing the mentioned metrics.

关键词： Wireless sensor networks Monitoring Task analysis Sensors Measurement scheduling algorithms Genetic algorithms Internet of Things application scheduling algorithms

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