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4th International Conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Geng, Yudong Chen, Jing Yan, Honghua Hao, Qingshui Zhang, Aimin Zhang, Tianxiang Ji, Mingchao Fu, Tonglin Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Shandong Greenthink Electric Power Technology Co. Ltd Jinan China

ISBN: (纸本)9798331534622

Nowadays, computing resources are widely distributed and diverse, and the demand for efficient execution of various workflow tasks on wide-area resources has been raised. multi-objection scheduling method based on space-time attribute of tasks. A scheduling scheme is generated through the three stages of workflow scheduling queue generation, resource selection combined with task spatiotemporal attributes and task and resource matching, and the selected resources of workflow are run according to this scheduling scheme. This method constructs a scheduling problem model to minimize workflow completion time, cost and resource matching degree, and finds the optimal scheduling scheme to improve task execution efficiency. In the process of the experiment, the workflow is actually run in the multi-cluster environment, and the experiment proves that it has a good effect in reducing the completion time and cost of workflow. © 2024 IEEE.

关键词： scheduling algorithms

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A Deep Reinforcement Learning scheduling Algorithm for Heterogeneous Tasks on Heterogeneous Multi-Core Processors 6

A Deep Reinforcement Learning Scheduling Algorithm for Heter...

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6th International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2024

作者： Tan, Qiguang Chen, Wei Liu, Dake School of information and communication engineering Hainan University Haikou China

ISBN: (纸本)9798350366143

Heterogeneous multi-core processor systems are common complex processing environments for scheduling DAG application tasks. Deep reinforcement learning has become a popular solution for scheduling in these systems due to its superior direct perception decision-making and handling of high-dimensional state-action spaces. This paper investigates a scheduling problem in a heterogeneous multi-core processor environment. First, a transformer-based graph convolutional neural network extracts and encodes system environment information. The decision space is then reduced by splitting task selection and processor allocation, where the former is managed by a deep neural network learning to select nodes, and the latter uses a heuristic scheduling algorithm. The entire scheduling process is framed as a Markov decision problem. Therefore, the PPO algorithm with dynamic adjustment of the clipping factor, combined with the advantage actor-critic network, is used to train, optimize, and evaluate the algorithm, seeking the optimal scheduling strategy. The training process used a reward function for the time required to complete task scheduling, and experiments were conducted in different environments. The results showed that compared with other algorithms, the algorithm proposed in this paper reduced the task scheduling time overhead of heterogeneous multi-core processor systems by 10.89%. © 2024 IEEE.

关键词： scheduling algorithms

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Privacy-Preserving Data scheduling in Incentive-Driven Vehicular Network

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IEEE INTERNET OF THINGS JOURNAL 2022年第22期9卷 22669-22681页

作者： Xia, Youhua Zhan, Tiehua Wu, Libing Zheng, Xi Jin, Jiong Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia Macquarie Univ Dept Comp Sydney NSW 2109 Australia

The lightweight privacy-preserving algorithm in the vehicular networks (VNs) improves the reliability of data transmission for the vehicles. However, it is challenging for vehicles to execute resource-consuming algorithms while driving. In addition, the high-speed mobility of vehicles also brings data scheduling problems for vehicles and other equipment. To tackle the problems mentioned above, this article proposes a privacy-preserving data scheduling in an incentive-driven VN, which achieves efficient and secure data transmission based on an incentive mechanism among vehicles. The algorithm first balances the benefits between the source, forwarding, and destination nodes through a multidimensional incentive mechanism to ensure positive benefits. After the vehicles participate in the task under the action of the incentive mechanism, the key task will then be identified and completed. Finally, the data interference mechanism guarantees the security of data transmission between the vehicle and the edge server. The simulation experiment results show that the proposed algorithm is superior to other algorithms in revenue, satisfaction, and reliability.

关键词： Task analysis Privacy Crowdsensing Data communication Reliability Data privacy scheduling algorithms Incentive mechanism key task privacy-preserving vehicular network (VN)

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Klotski v2: Improved DNN Model Orchestration Framework for Dataflow Architecture Accelerators

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1045-1058页

作者： Bai, Chen Wei, Xuechao Zhuo, Youwei Cai, Yi Zheng, Hongzhong Yu, Bei Xie, Yuan The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Alibaba DAMO Academy Computing Technology Laboratory Hangzhou311121 China Peking University School of Integrated Circuits Beijing100871 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Dataflow architecture accelerators are a new kind of scalable DNN accelerators. For an instruction, the availability of input operands solely determines the beginning of executions. DNN model orchestration determines how to partition, schedule, and map the computation to the underlying hardware. In this article, we propose the Klotski v2 framework to solve DNN model orchestration for dataflow architecture accelerators. First, a Bayesian optimization-based entropy-directed partition algorithm is proposed to transform a DNN model into μ ops. Second, a unified formal formulation for μ ops scheduling and mapping is presented. Third, a two-stage methodology is proposed to decouple the scheduling and mapping. Fourth, a Hilbert curve-based mapping heuristic is proposed to enhance problem-solving efficiency, improving the tradeoff between solution quality and algorithm runtime. Extensive results show that Klotski v2 can achieve an average of 21.57% higher execution performance improvement than the previous methodologies. With the Hilbert curve-based mapping heuristic, we improve the algorithm efficiency by an average of 63.50% across different DNN workloads. © 1982-2012 IEEE.

关键词： scheduling algorithms

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Demo Abstract: Collaborative Real-Time scheduling (CRTS) Algorithm for AGV Transportation System within a CPS Architecture 23

Demo Abstract: Collaborative Real-Time Scheduling (CRTS) Alg...

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8th ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI)

作者： Shaw, Himansu Cheng, Albert M. K. Univ Houston Houston TX 77004 USA

ISBN: (纸本)9798400700378

The use of Autonomous Guided Vehicles (AGVs) is important in smart factories to enhance manufacturing operations. However, current AGV scheduling algorithms are inadequate and not connected to real-time data on manufacturing needs, leading to underutilized AGVs and missed delivery deadlines. The lack of algorithms that ensure timely delivery of materials results in idle time of AGVs and material shortages. We present a collaborative real-time scheduling (CRTS) algorithm for AGVs in smart factories. The algorithm not only ensures timely delivery of materials to processing units but also predicts the minimum number of AGVs required. The algorithm is designed to operate within a cyber-physical system architecture, where AGVs and processing units exchange data via a wireless network. The simulation results on the Node-Red platform show that the algorithm is efficient and adequate to meet real-time delivery requirements, with an average AGV utilization of over 92%.

关键词： scheduling algorithms

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Sensor device scheduling-based cuckoo algorithm for enhancing lifetime of cluster-based wireless sensor networks

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2022年第1期68卷 58-69页

作者： Hameed, Mazin Kadhum Idrees, Ali Kadhum Univ Babylon Dept Software Babylon Iraq Univ Babylon Dept Comp Sci Babylon Iraq

This study proposes the Sensor Device scheduling-based Cuckoo Algorithm (SeDeSCA) for enhancing the lifespan of cluster-based WSNs. SeDeSCA consists of two phases: clustering and scheduling. The WSN is clustered into clusters using the DBSCAN algorithm in the first phase. The scheduling phase is periodic and consists of three steps: cluster head polling, scheduling decision-based optimisation, and covering. The sensors in each cluster choose their cluster head, which executes the Cuckoo Algorithm (CA) to select a suitable schedule of sensors that will achieve sensing during the current period. The aim of scheduling is to minimise energy consumption and ensure sufficient coverage for the monitored area while maximising network lifespan. The third step is to cover the area of interest with the sensors that are scheduled to be active during this period. The simulation results show that SeDeSCA improves the network lifespan and coverage ratio, as well as the lifespan of WSNs.

关键词： wireless sensor networks cuckoo algorithm DBSCAN lifetime enhancement scheduling algorithms

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A Secure Key Expansion Algorithm for Present

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IEEE SENSORS JOURNAL 2023年第20期23卷 25367-25376页

作者： Sultan, Ishfaq Lone, Mudassir Yasin Nazish, Mir Banday, M. Tariq Univ Kashmir Dept Elect & Instrumentat Technol Srinagar 190006 India

The efficiency of a cryptographic algorithm in terms of security depends on the resistance against cryptanalytic attacks. Besides the complexity of the encryption algorithm, the key plays an essential role in the security against cryptanalytic attacks. The strength and complexity of the encryption algorithm do not suffice and serve the fundamental purpose of security if the key is compromised at any stage. Thus, apart from the cryptanalytically robust encryption algorithm, a strong key schedule is also essential to thwart possible attacks against a particular algorithm. PRESENT, a lightweight encryption technique with a simple design, is resistant to linear and differential attacks but has a weak key schedule and is susceptible to cryptanalytic attacks. This article proposes an efficient key scheduling algorithm for PRESENT lightweight encryption technique resistant against cryptanalytic attacks. Statistical tests examine the proposed key schedule's cryptographic strength on the properties of subkeys produced in the key generation mechanism. The tests prove the efficiency of the proposed key schedule in terms of cryptanalytic attacks. Implementation and comparison of the efficacy of the proposed key schedule in terms of security and implementation costs are illustrated with the PRESENT-80 key schedule and the PRESENT-128 key schedule. The key scheduling algorithm is implemented on the advanced RISC machine (ARM) Cortex M3-based NXP LPC 1857 and 1768 hardware development platforms. The results indicate the efficiency of the proposed algorithm in terms of security, performance, and power consumption across the two hardware platforms.

关键词： Schedules Encryption scheduling algorithms Resistance Complexity theory Standards Sensors Cryptanalytic attacks diffusion key schedule lightweight cryptography related-key attacks round counter slide attacks

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6G Network AI Architecture for Everyone-Centric Customized Services

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IEEE NETWORK 2023年第5期37卷 71-80页

作者： Yang, Yang Ma, Mulei Wu, Hequan Yu, Quan You, Xiaohu Wu, Jianjun Peng, Chenghui Yum, Tak-Shing Peter Aghvami, A. Hamid Li, Geoffrey Y. Wang, Jiangzhou Liu, Guangyi Gao, Peng Tang, Xiongyan Cao, Chang Thompson, John Wong, Kat-Kit Chen, Shanzhi Wang, Zhiqin Debbah, Merouane Dustdar, Schahram Eliassen, Frank Chen, Tao Duan, Xiangyang Sun, Shaohui Tao, Xiaofeng Zhang, Qinyu Huang, Jianwei Zhang, Wenjun Li, Jie Gao, Yue Zhang, Honggang Chen, Xu Ge, Xiaohu Xiao, Yong Wang, Cheng-Xiang Zhang, Zaichen Ci, Song Mao, Guoqiang Li, Changle Shao, Ziyu Zhou, Yong Liang, Junrui Li, Kai Wu, Liantao Sun, Fanglei Wang, Kunlun Liu, Zening Yang, Kun Wang, Jun Gao, Teng Shu, Hongfeng Hong Kong Univ Sci & Technol Guangzhou IoT Thrust & Res Ctr Digital World Intelligent Th Guangzhou 511453 Peoples R China Terminus Grp Beijing 100027 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Chinese Acad Engn Beijing 100088 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Huawei Technol Shanghai 201206 Peoples R China Qingdao City Univ Dept Comp Sci & Technol Qingdao 266106 Peoples R China Kings Coll London London WC2R 2LS England Imperial Coll London London SW7 2AZ England Univ Kent Sch Engn Canterbury CT2 7NT England China Mobile Beijing 100053 Peoples R China China Unicom Beijing 100048 Peoples R China Univ Edinburgh Sch Engn Edinburgh EH9 3FG Scotland UCL Dept Elect & Elect Engn London WC1E 6BT England China Acad Telecommun Technol Beijing 100191 Peoples R China China Acad Informat & Commun Technol Beijing 100191 Peoples R China Technol Innovat Inst Abu Dhabi U Arab Emirates TU Wien A-1040 Vienna Austria Univ Oslo Dept Informat N-0373 Oslo Norway VTT Tech Res Ctr Finland Espoo 02150 Finland ZTE Corp Shenzhen 518057 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Zhejiang Lab Hangzhou Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Purple Mt Labs Nanjing Peoples R China Xidian Univ Sch Telecommun Engn Xidian Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China East China Normal Univ Software Engn Inst Shanghai Peoples R China Univ Shanghai Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai Peoples R China Univ Elect Sci & Technol

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions.

关键词： Artificial intelligence Task analysis 6G mobile communication Quality of experience Computer architecture 5G mobile communication Cloud computing Mobile communication Performance evaluation Energy efficiency Service computing scheduling algorithms

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TopoOrderPart: a Multi-level scheduling-Driven Partitioning Framework for Processor-Based Emulation 24

TopoOrderPart: a Multi-level Scheduling-Driven Partitioning ...

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43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Bi, Shunyang Tang, Jing You, Hailong Wu, Haonan Li, Cong Sun, Richard Faculty of Integrated Circuit Xidian University Xi’an China S2C Inc. Shenzhen China

ISBN: (纸本)9798400710773

In a compilation flow of processor-based emulation (PBE), partitioning involves dividing a large netlist into smaller pieces and assigning them to different processors. Furthermore, the scheduling process must adhere to the levels of the netlist, which are determined by topological ordering, and the logic gates in the same level can be emulated in parallel. However, during the netlist partitioning stage, assigning most gates at the same level to one processor would undermine the benefits of parallelization in scheduling, leading to overall performance degradation. This paper proposes the TopoOrderPart, the first scheduling-driven partitioning framework for simultaneous balancing topological order and minimizing the cut size, which holds significant value in reducing time steps of scheduling. In particular, the topological order balancing and cut size are considered throughout the multilevel paradigm, and balance-aware coarsening achieves balancing between clusters in the early stage, with super-far root growing initial partitioning obtaining the better partition by selecting those root nodes in distant relationship within the connection space and two novel TopoRefine algorithms further enhancing the solution. Experimental results show TopoOrderPart can improve 69% topological order balancing and 0.53× run time while maintaining comparable cut size, compared to the state-of-the-art partitioner. © 2024 Copyright is held by the owner/author(s).

关键词： scheduling algorithms

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Electric Vehicle Charge Cycle Management System for ComEd Real-Time Pricing Scheme: An IoT Solution

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IEEE INTERNET OF THINGS JOURNAL 2023年第9期10卷 7681-7690页

作者： Davila Rojas, Miguel Angel Trujillo Rodriguez, Cesar Leonardo Univ Dist Francisco Jose de Caldas Alternat Energy Sources Res Lab LIFAE Bogota 110231 Colombia Univ Dist Francisco Jose de Caldas Dept Elect Engn Bogota 110231 Colombia

In this article, an electric vehicle (EV) charge cycle management system (CCMS) for ComEd real-time pricing (RTP) scheme is presented. It is an embedded system for an EV smart charger. As an energy management system, implemented system seeks to reduce charge cycle cost per kWh. Charge cycle management is performed in three steps: first, prices are forecasted along the available time horizon through a trend and seasonality model. Thereafter, with the aim of scheduling the charge cycle, the lowest pricing intervals combination is found using a direct insertion sort algorithm. Finally, the scheduled combination is executed with possible adjustments on execution time in function of the current price. Algorithm performance is evaluated on a simulation study where user daily routine and user driving behavior are modeled stochastically and ComEd RTP historical data are used.

关键词： Pricing Predictive models Real-time systems Computational modeling Market research Internet of Things Correlation coefficient Demand-side management (DSM) electric vehicle (EV) charging energy management systems Internet of Things (IoT) real-time systems scheduling algorithms smart devices

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