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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Wang, Xiaohan Zhang, Lin Liu, Yongkui Yang, Yuan School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Mechano-Electronic Engineering Xidian University Xi’an710071 China

ISBN: (纸本)9789811989148

As a new manufacturing mode, cloud manufacturing integrates distributed manufacturing resources and capabilities into services, providing services to consumers with manufacturing requirements. Assigning consumers’ tasks to services requires many-to-many scheduling. An effective scheduling algorithm can reduce production expenditure and increase processing efficiency. However, the cloud manufacturing environment takes the characteristics of dynamics, complexity, and diversity, making the task scheduling intractable. Deep reinforcement learning has been gradually applied to various scheduling problems and shows the potential of little hand-craft and non-trivial generalizability. This paper proposes a deep recurrent q-network-based scheduling algorithm to address task scheduling problems in cloud manufacturing. The environment is modeled as a partially observable Markov decision process. The long-short-term-based policy trained by reinforcement learning is utilized to choose service providers for each task in step. A case study of the automobile structure part processing indicates that our proposal outperforms deep q-network by 5.7% and proximal policy optimization by 7.7% on scheduling accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： scheduling algorithms

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A Parallelism-Based Earliest Finish Time (PBEFT) Algorithm for Workflow scheduling in Clouds 7

A Parallelism-Based Earliest Finish Time (PBEFT) Algorithm f...

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7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Wu, Xiaojun Cheng, Sijia Yuan, Sheng Wang, Ziyi School of Software Engineering Xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9781665487115

For those public-cloud-based application providers, workflow scheduling in clouds must not only meet traditional performance optimization goals, but also minimize financial costs. This paper focuses on budget-constrained workflow scheduling issue on heterogeneous cloud resources, and tries to minimize the end-to-end delay of the workflow while meeting the given budget. A heuristic algorithm is proposed, which introduces a resource leasing mechanism to maximize the usage of the budget. This paper also defines a scheduling factor parameter consisting of two factors (parallelism and urgency) to determine the scheduled tasks. A scheduling algorithm based on task duplication is designed, which reduces the finish time of workflow tasks by using the idle time slots in existing servers. Finally, compared with contrast algorithms, the experimental results show that the proposed algorithm is superior in both meeting budget constraints and minimizing end-to-end delay. © 2022 IEEE.

关键词： Cloud computing Costs scheduling algorithms Parallel processing scheduling Virtual machining Delays

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Privacy-Preserving Data scheduling in Incentive-Driven Vehicular Network

Privacy-Preserving Data Scheduling in Incentive-Driven Vehic...

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作者： Xia, Youhua Zhan, Tiehua Wu, Libing Zheng, Xi Jin, Jiong Wuhan University School of Computer Science School of Cyber Science and Engineering Wuhan430072 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia Macquarie University Department of Computing SydneyNSW2109 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. © 2014 IEEE.

关键词： scheduling algorithms

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Complete Targets Coverage in Energy Harvesting Internet of Things With Ambient Backscatter

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IEEE SYSTEMS JOURNAL 2022年第4期16卷 5131-5141页

作者： Yang, Rui Yang, Changlin Chin, Kwan-Wu Liu, Ying He, Tengjiao Sun Yat Sen Univ Sch Software Engn Zhuhai 510275 Peoples R China HoHai Univ Coll Internet Things Engn Changzhou 210098 Peoples R China Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

This article considers deriving set covers or the set of active nodes that are responsible for monitoring all targets in an Internet of Things (IoT) network. A key distinction to prior works is that this article considers sensor nodes aided by backscatter communications, which allow them to communicate with negligible energy cost using ambient radio frequency (RF) signals. The article contains three main novelties. First, it presents a mixed integer linear program (MILP) that can be used to compute the global optimal solution. Second, it also outlines a centralized greedy scheduling (CGS) algorithm that selects nodes based on their energy and the number of covered targets. Third, it presents a distributed greedy scheduling (DGS) algorithm that selects nodes according to their energy level. The simulation results show that equipping nodes with ambient backscattering capability increases the ratio of complete targets coverage by more than 100% as compared to existing techniques.

关键词： Backscatter Radio frequency Monitoring Relays Energy states Energy harvesting Internet of Things Duty-cyle energy management optimization scheduling algorithms wireless sensor networks

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MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments

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IEEE ACCESS 2022年 10卷 27096-27112页

作者： Gedawy, Hend Elgazar, Ali Harras, Khaled A. Carnegie Mellon Univ Qatar Comp Sci Dept Doha Qatar

Many novel IoT-based applications demand low latency, large compute resources, and high privacy. These requirements have motivated the emergence of fog and edge computing to complement the low-privacy and high-latency cloud. The intention behind Fog computing is to place computational servers closer to the user, typically within the city's vicinity, to reduce latency. However, because of the high deployment cost of these servers at scale, and unreliable network infrastructures in many countries or areas, edge computing was proposed. Edge computing advocates leveraging compute resources, typically 0-hops away, on distributed ensembles of colocated devices called FemtoClouds. In this paper, we propose MAESTRO, a system that enables users to offload computational jobs to multiple FemtoClouds in their immediate vicinity. For MAESTRO, we build an integrated architecture that includes two new scheduling algorithms for assigning computing workloads to FemtoClouds. Each of our scheduling algorithms is designed to allow the system to operate more efficiently given poor or strong network infrastructures. We implement a full prototype of our system to assess its performance on our experimental testbed. The results indicate that in communication-challenged environments, our specialized scheduler outperforms state-of-the-art schedulers by up to 55%, while in communication-friendly environments our other specialized scheduler outperforms state-of-the-art schedulers by up to 67%.

关键词： Task analysis scheduling Edge computing Cloud computing scheduling algorithms Servers Prototypes Edge computing FemtoClouds Internet of Things IoT cloud mobile computing

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Deadline-Aware and Fair Upstream scheduling in Optical Ring Networks for Sensor Data Acquisition and Control 27

Deadline-Aware and Fair Upstream Scheduling in Optical Ring ...

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27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Nagatsuka, Taiki Ochi, Koji Asano, Sho Kubo, Ryogo Keio University Department of Electronics and Electrical Engineering 3-14-1 Hiyoshi Kohoku-ku Yokohama-shi Kanagawa223-8522 Japan

We propose an upstream scheduling method for optical ring networks based on time- and space-division multiplexing technology. Numerical simulations show that the proposed method achieves deadline-aware and fair sensor... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： scheduling algorithms

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Task Offloading Scheme based on PTSS-DQN Algorithm for Latency Optimization in Multi-User and Multitasking Environments 10

Task Offloading Scheme based on PTSS-DQN Algorithm for Laten...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhou, Panyang Xing, Xiaofei Wang, Xiao Wang, Tian Zhang, Yuheng Wang, Guojun Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China

ISBN: (纸本)9798331520861

Edge computing is gaining widespread attention, particularly in the domain of task offloading. Efficiently and accurately offloading tasks to edge servers presents a significant challenge due to its complexity. In this paper, we depart from the traditional user-to-edge offloading scenario and instead focus on a model where users offload tasks to base stations. These base stations, equipped with servers and possessing an extended communication range, centrally manage all tasks. A base station can either offload tasks to a specific edge server within its communication range or handle them locally. To address the scheduling challenge among multiple tasks, we propose a priority-based Time Slice scheduling (PTSS) algorithm to calculate task processing latency. Additionally, we introduce the PTSS-DQN algorithm by integrating the Deep Q-Network (DQN) to jointly optimize the offloading strategy, resource allocation, time-slice distribution, and task execution order. Experimental results demonstrate the effectiveness of our proposed approach. © 2024 IEEE.

关键词： scheduling algorithms

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Packet scheduling Algorithm Based on Priority Adjustment in Wireless Sensor Networks 10

Packet Scheduling Algorithm Based on Priority Adjustment in ...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Chen, Xi Shi, Xuesen Wang, Yongqing School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

The quality of service in wireless sensor networks is significantly affected by delays. The existing dynamic multilevel priority (DMP) scheduling algorithm suffers from the problem of starvation where low-priority services cannot be fulfilled in time, leading to higher group delay in the case of sudden traffic flow. To address the limitations of the DMP algorithm, this paper proposes a priority dynamic adjustment packet scheduling (PAS) algorithm to effectively reduce the queuing delay of packets in a node, improve the starvation problem associated with low-priority services, and ensure the quality of different services. The algorithm optimizes the queuing method of different service types in the ready queue and is dynamically adjusted according to the service type and backlog of packets. Consequently, queuing delays of non-real-time service packets are reduced, thereby leading to a reduction in end-to-end delays. The simulation results show that, in a scenario with a high arrival rate of service flows, the PAS algorithm can reduce the end-to-end delays by 30% compared with the DMP scheduling algorithm. © 2022 IEEE.

关键词： scheduling algorithms

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Research on Distributed Microgrid Group Optimization for Ships Based on Optimized Particle Swarm Algorithm 2

Research on Distributed Microgrid Group Optimization for Shi...

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2nd IEEE International Conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Xiao, Bowei Ma, Wenbin School of Electrical Engineering Northeast Electric Power University Jilin China

ISBN: (纸本)9798350373646

This paper delves into the optimization of ship microgrid scheduling models based on an optimized particle swarm algorithm, aiming to enhance the electrical efficiency of ships, reduce reliance on conventional power units, and rationalize the operation of power systems. Initially, the paper constructs a ship microgrid model that integrates various distributed generation forms and thoroughly analyzes its operational characteristics and costs. Building on this, an improved particle swarm algorithm is introduced to optimize the scheduling of the microgrid, seeking the best capacity configuration and operation strategy to maximize economic and environmental benefits. Finally, simulation analysis was carried out using relevant parameters from a certain sea area in the North Sea. The results show that the improved Particle Swarm Optimization (PSO) can significantly optimize the system operation. The optimized system can flexibly adjust the output of various distributed generation units based on the actual situation and the load demand of the ship, reducing system costs and ensuring the reasonable operation of the ship's microgrid system, providing theoretical guidance for the future design and operation of ship microgrids. © 2024 IEEE.

关键词： scheduling algorithms

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Vulnerability Testing on the Key scheduling Algorithm of PRESENT Using Deep Learning 4th

Vulnerability Testing on the Key Scheduling Algorithm of PRE...

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4th EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2021

作者： Duan, Ming Zhou, Rui Fu, Chaohui Guo, Sheng Wu, Qianqiong Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Zhengzhou China

ISBN: (纸本)9783030967901

PRESENT is a lightweight block cipher developed for extremely constrained environment such as RFID tags and IoT (Internet of Things). In 2020, Pareek et al. suggested a neural network to retrieving the 80-bit key of block cipher PRESENT from the last round subkeys and they arrived at a conclusion that the key scheduling algorithm is strong enough against its neural aided attack. While in this paper, we get a contradict result. We present two different experiments to test the vulnerability of key scheduling algorithm using deep learning. First, we build a 3-depth Fully Connected Neural Network to retrieve the master key bit by bit. The result is that we can predict about 80% bits of a random key with very high accuracy (above 0.9). Furthermore, we train a Residual Neural Network for classification. Compared with Fully Connected Network, we need less networks and the success rate is 100%. Finally, we combine the two networks to retrieve the whole 80-bit key from the last 64-bit round subkey. We think this method can be applied to the key schedule of other block ciphers or other similar cryptographic processes. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： scheduling algorithms

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