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

作者： Xu, Yanbo Zhao, Jiakun Xi'An Jiaotong University of China Department of Software Engineering Xi'an China

ISBN: (纸本)9781665487115

Cloud computing is widely used in academia and industry. Users can provide jobs to cloud service providers that provide pay-as-you-go methods by cloud for calculation and obtaining results. Cloud service providers make reasonable scheduling for different jobs of users and in the case of successful operation, the cost of energy consumption becomes a key factor. Prior works proposed various algorithms based on reinforcement learning on job scheduling. However, due to the lack of online training mode and refined container decision-making, this paper makes some improvements and extensions. This paper presents the algorithm ACT4JS, an actor-critic with transformer for three stage job scheduling algorithm, that finely manages data centers, server nodes, and application containers. The job scheduling algorithm is designed to select the best long-term decision by learning from cloud computing resource model that accepts single-task multi-instance jobs and directed acyclic graph jobs with dependencies. To learn key features and loss optimization, use the transformer network structure and the proximal policy optimization method. To minimize energy consumption costs for large scale job scheduling and running, designs a reward function to reflect optimized energy consumption targets and independent simulation experiments. To achieve low energy consumption cost and low task reject rate, use training methods such as experience replay and advantage estimate. Compared with the mainstream baseline algorithm, the results show that the ACT4JS algorithm is better than the baseline in the online training model. In addition, the effect of ACT4JS is more effective in the long-term experimental. © 2022 IEEE.

关键词： Training Cloud computing Energy consumption Costs Job shop scheduling scheduling algorithms Computational modeling

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Providing quality of service in omni-path networks

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JOURNAL OF SUPERCOMPUTING 2022年第10期78卷 12310-12343页

作者： Cano-Cano, Javier Andujar, Francisco J. Alfaro-Cortes, Francisco J. Sanchez, Jose L. Mora, Gaspar Univ Castilla La Mancha Comp Syst Dept Campus Univ S-N Albacete 02071 Spain Univ Valladolid Comp Syst Dept Campus Miguel DelibesPaseo Belen 15 Valladolid 47011 Castilla & Leon Spain Intel Corp Comp Syst Dept Santa Clara CA USA

New hierarchical crossbar switch architectures, such as Omni-Path (OPA) and Cray X2, have appeared to improve packet latency, reduce overall cost and increase fault tolerance of the high-performance interconnection networks in supercomputing and data center systems. These and other interconnect technologies (Infiniband or 40/100 Gigabit Ethernet) include support to provide quality of service (QoS) to the applications. In this paper, we show how this QoS support can be enabled to achieve bandwidth and/or latency differentiation in Omni-Path interconnection networks, as a representative case of hierarchical switches. To do that, three different table-based schedulers are used. We include the description of these schedulers and a comparative study by using the results obtained when we evaluate them with Hiperion, a simulation tool that implements an OPA model.

关键词： Quality of Service (QoS) scheduling algorithms Interconnection networks Omni-Path (OPA) Simulation Hierarchical-crossbar-switch architecture

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The HEFT Algorithm Based on Node Merging 8

The HEFT Algorithm Based on Node Merging

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8th International Workshop on Control Engineering and Advanced algorithms, IWCEAA 2024

作者： Huang, Ying Lu, Yuanhong Zhang, Jie Zhang, Jingyue Guo, Haiping National Energy Power Grid Technology R&D Centre Guangzhou510663 China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou Guangzhou510663 China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China

ISBN: (纸本)9798331528881

The task scheduling problem in power systems is complex, especially when communication costs exceed computation costs. This paper proposes an improved HEFT algorithm with a node merging mechanism, merging nodes through hierarchical traversal to assign high-communication-cost tasks to the same processor, reducing communication overhead. Experiments with 499-dimensional and 1614-dimensional matrices from the power system show that the improved algorithm reduces completion time by 5ms and 2ms, respectively, compared to standard HEFT. © 2024 IEEE.

关键词： scheduling algorithms

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Research on a multilevel plan management model based on the pyramid plan management structure 3

Research on a multilevel plan management model based on the ...

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3rd International Symposium on Computer Applications and Information Systems, ISCAIS 2024

作者： Wang, Long Liu, Haibin Xia, Minghao Wang, Yu Li, Mingfei Beijing University of Technology Faculty of Materials and Manufacturing Beijing China Capital Aerospace Machinery Co. Ltd Beijing China

ISBN: (纸本)9781510681316

Planning management is the most important aspect of management activities in large manufacturing enterprises. In addition to their large scale, large manufacturing enterprises are also reflected in the complexity of their manufacturing environment, such as multiple workshops with different specialties, multiple production projects, complex market environments, and so on. How to use a planning management model to effectively undertake project plans upwards, penetrate into end plans downwards, and carry out efficient management has become an important research topic for modern enterprises. This article proposes a new multi-level planning management structure and deploys a new priority algorithm at the workshop planning layer. This algorithm focuses on factors such as device loading and task preemption in the earliest deadline priority scheduling algorithm EDF, and has improvements in reducing task loss and aligning with actual production. In addition, innovation focuses on the study of planning, assessment, and management models in the field of scheduling research. The article proposes a plan performance model that can accurately evaluate project teams of different scales, resulting in a 14.74% increase in the plan execution rate of a certain enterprise. © 2024 SPIE.

关键词： scheduling algorithms

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Adaptive Offloading and Enhancement for Low-Light Video Analytics on Mobile Devices

Adaptive Offloading and Enhancement for Low-Light Video Anal...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： He, Yuanyi Yang, Peng Qin, Tian Hou, Jiawei Zhang, Ning Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China University of Windsor Department of Electrical and Computer Engineering WindsorON Canada

ISBN: (纸本)9798350351255

In this paper, we explore adaptive offloading and enhancement strategies for video analytics tasks on computing-constrained mobile devices in low-light conditions. We observe that the accuracy of low-light video analytics varies from different enhancement algorithms. The root cause could be the disparities in the effectiveness of enhancement algorithms for feature extraction in analytic models. Specifically, the difference in class activation maps (CAMs) between enhanced and low-light frames demonstrates a positive correlation with video analytics accuracy. Motivated by such observations, a novel enhancement quality assessment method is proposed on CAMs to evaluate the effectiveness of different enhancement algorithms for low-light videos. Then, we design a multi-edge system, which adaptively offloads and enhances low-light video analytics tasks from mobile devices. To achieve the trade-off between the enhancement quality and the latency for all system-served mobile devices, we propose a genetic-based scheduling algorithm, which can find a near-optimal solution in a reasonable time to meet the latency requirement. Thereby, the offloading strategies and the enhancement algorithms are properly selected under the condition of limited end-edge bandwidth and edge computation resources. Simulation experiments demonstrate the superiority of the proposed system, improving accuracy up to 20.83% compared to existing benchmarks. © 2024 IEEE.

关键词： scheduling algorithms

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ILP-based multiperiod flow routing and scheduling method in time-sensitive network 4

ILP-based multiperiod flow routing and scheduling method in ...

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2022 4th International Conference on Wireless Communications and Smart Grid, ICWCSG 2022

作者： Wang, Jin Zhou, Liang Tian, Li State Grid Hubei Electric Power Research Institute Wuhan China

Time-sensitive network (TSN) provides end-to-end data transfer with very low latency and high reliability on the basis of Ethernet. It is suitable for time-sensitive applications and will be widely used in scenarios such as autonomous driving and industrial Internet. When the existing time-sensitive flow routing and scheduling methods do not consider the shared bandwidth of the flow triggered by different cycle times, the possibility of conflict is different, which leads to the inaccuracy of the index to measure the scheduling bottleneck. This paper analyses the different cycle time-sensitive flow between the possibility of conflict, puts forward a kind of applicable to passes based on integer linear programming cycle time-sensitive traffic scheduling algorithm, according to the characteristics of the cycle time-sensitive flow carries on the preliminary classification and sorting, and developed a series of scheduling constraints, implementation is sensitive to time flow of packet scheduling. The experiment can be conducted from the three perspectives of average end-to-end delay, maximum load, and flow scheduling. The proposed routing method was compared with the existing benchmark routing algorithm evaluation. The results show that the proposed method can significantly improve the capacity of the network sensitive to time flow, reduce load scheduling by optimizing the network bottleneck at the same time, and improve the schedulability. © Published under licence by IOP Publishing Ltd.

关键词： scheduling algorithms

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Cardinality Constrained scheduling in Online Models 39

Cardinality Constrained Scheduling in Online Models

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39th International Symposium on Theoretical Aspects of Computer Science, STACS 2022

作者： Epstein, Leah Lassota, Alexandra Levin, Asaf Maack, Marten Rohwedder, Lars Department of Mathematics University of Haifa Israel EPFL Lausanne Switzerland Faculty of Industrial Engineering and Management Technion Haifa Israel Heinz Nixdorf Institute Department of Computer Science Paderborn University Germany School of Business and Economics Maastricht University Netherlands

ISBN: (纸本)9783959772228

Makespan minimization on parallel identical machines is a classical and intensively studied problem in scheduling, and a classic example for online algorithm analysis with Graham’s famous list scheduling algorithm dating back to the 1960s. In this problem, jobs arrive over a list and upon an arrival, the algorithm needs to assign the job to a machine. The goal is to minimize the makespan, that is, the maximum machine load. In this paper, we consider the variant with an additional cardinality constraint: The algorithm may assign at most k jobs to each machine where k is part of the input. While the offline (strongly NP-hard) variant of cardinality constrained scheduling is well understood and an EPTAS exists here, no non-trivial results are known for the online variant. We fill this gap by making a comprehensive study of various different online models. First, we show that there is a constant competitive algorithm for the problem and further, present a lower bound of 2 on the competitive ratio of any online algorithm. Motivated by the lower bound, we consider a semi-online variant where upon arrival of a job of size p, we are allowed to migrate jobs of total size at most a constant times p. This constant is called the migration factor of the algorithm. algorithms with small migration factors are a common approach to bridge the performance of online algorithms and offline algorithms. One can obtain algorithms with a constant migration factor by rounding the size of each incoming job and then applying an ordinal algorithm to the resulting rounded instance. With this in mind, we also consider the framework of ordinal algorithms and characterize the competitive ratio that can be achieved using the aforementioned approaches. More specifically, we show that in both cases, one can get a competitive ratio that is strictly lower than 2, which is the bound from the standard online setting. On the other hand, we prove that no PTAS is possible. © Leah Epstein, Alexandra Lassota,

关键词： scheduling algorithms

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Mobile Charger scheduling Algorithm for Energy Recharging in Wireless Sensor Networks

Mobile Charger Scheduling Algorithm for Energy Recharging in...

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2022 International Conference on Electronic Systems and Intelligent Computing, ICESIC 2022

作者： Dande, Bhargavi Chang, Chih-Yung Fan, Cheng-De Tamkang University Dept. of Computer Science and Information Engineering New Taipei City Taiwan Tamkang University Dept. of Electrical and Computer Engineering New Taipei City Taiwan

ISBN: (纸本)9781665483858

In wireless sensor networks (WSNs), the finite battery capacity of the sensor nodes restricts the lifetime of the network. With the wireless power transfer (WPT) technology, the mobile chargers can transmit energy to the sensor nodes, which provides a solution for recharging the sensors. Therefore, this paper proposed a mobile charger scheduling (MCS) algorithm, which maximizes the coverage quality of the network. The proposed MCS algorithm formulated an adaptive recharging request threshold for the sensor nodes. Then the coverage benefit (CB) of the requested sensors is considered to dynamically adjust the path. Finally, through the experimental analysis, it is shown that the proposed MCS algorithm gains the best performance than the existing works. © 2022 IEEE.

关键词： scheduling algorithms

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A Duty-Cycle Based scheduling Algorithm for Energy Efficient e-Health System 1

A Duty-Cycle Based Scheduling Algorithm for Energy Efficient...

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1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022

作者： Bhowmik, Tanima Mojumder, Rohan Ghosh, Dibyendu Banerjee, Indrajit Iiest Shibpur Department of It Shibpur Howrah India C.I.E.M Department of Ece Kolkata India

ISBN: (数字)9781665443579

ISBN: (纸本)9781665443579

In the last few years, the environment around us has evolved into an almost IoT-based environment. IoT has an effect in each sector, from medical services to industry. IoT devices are sensor-based, the sensor nodes rely vigorously on batteries for power supply. Thus, energy efficiency plays a significant role in such devices. High power-consuming devices reduce battery life. In this paper, we propose a duty-cycle based scheduling algorithm where the sensors are turned ON as per their requirement, and for the rest of the time, they are kept in OFF state. Framework results show that the scheduled algorithm can further improve battery lifetime. © 2022 IEEE.

关键词： scheduling algorithms

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Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey

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IEEE INTERNET OF THINGS JOURNAL 2022年第11期9卷 8364-8386页

作者： Bian, Jiang Al Arafat, Abdullah Xiong, Haoyi Li, Jing Li, Li Chen, Hongyang Wang, Jun Dou, Dejing Guo, Zhishan Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Baidu Inc Big Data Lab Beijing 100193 Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Univ Macau Dept Comp & Informat Sci Macau Peoples R China Zhejiang Lab Res Ctr Intelligent Network Hangzhou 311121 Zhejiang Peoples R China

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computer vision, natural language processing, automated speech recognition, etc. Real-time safety-critical embedded and Internet of Things (IoT) systems, such as autonomous driving systems, UAVs, drones, security robots, etc., heavily rely on ML/DL-based technologies, accelerated with the improvement of hardware technologies. The cost of a deadline (required time constraint) missed by ML/DL algorithms would be catastrophic in these safety-critical systems. However, ML/DL algorithm-based applications have more concerns about accuracy than strict time requirements. Accordingly, researchers from the real-time systems (RTSs) community address the strict timing requirements of ML/DL technologies to include in RTSs. This article will rigorously explore the state-of-the-art results emphasizing the strengths and weaknesses in ML/DL-based scheduling techniques, accuracy versus execution time tradeoff policies of ML algorithms, and security and privacy of learning-based algorithms in real-time IoT systems.

关键词： Task analysis Real-time systems Machine learning algorithms scheduling algorithms Internet of Things Hardware Sensors Deep learning (DL) Internet of Things (IoT) machine learning (ML) real-time systems (RTSs) scheduling

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