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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Xuguang Hou Daliang Sun Shengli Huang Jiabao Fan Guangda Liu Lieyan He GAC Research and Development Center Guangzhou China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

With the development of in-vehicle network (IVN) technology, the real-time performance and reliability of vehicle Ethernet become very important. Time-sensitive network (TSN) has become a new technical solution for vehicle Ethernet because of its high accuracy of data transmission. To solve the problem of low scheduling efficiency and poor real-time transmission in traffic scheduling in TSN, the optimization of traffic scheduling time is studied in the network from the aspect of scheduling algorithm. Firstly, the time slot scheduling model of time-sensitive flows is established. Then, a hybrid scheduling strategy considering traffic routing and traffic scheduling optimization is designed. In the routing strategy, the ant colony algorithm is designed to optimize the traffic transmission path. In the scheduling strategy, a time planning model is established and the global scheduling schedule is optimized by mixed integer programming algorithm. Finally, the traffic scheduling simulation and gated scheduling experiment verify that the proposed scheduling optimization algorithm can significantly improve the efficiency and real-time performance of traffic scheduling in vehicle TSN.

关键词： Integer programming Schedules scheduling algorithms Ethernet Logic gates Routing scheduling

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Partitioned scheduling with Safety-Performance Trade-Offs in Stochastic Conditional Dag Models

SSRN

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SSRN 2024年

作者： Deng, Xuanliang Sifat, Ashrarul H. Huang, Shaoyu Wang, Sen Huang, Jiabin Jung, Changhee Williams, Ryan Zeng, Haibo Purdue University United States University of Maryland United States

This paper is motivated by robotic systems that solve difficult real-world problems such as search and rescue (SAR). These applications require robots to operate in uncertain environments while maintaining safe interactions with human. We study the scheduling of real-time applications on heterogeneous hardware platforms inspired by such contexts. To capture the stochasticity due to unpredictable environments, we propose the stochastic heterogeneous parallel conditional DAG (SHPC-DAG) model, which extends the most recent HPC-DAG model in two regards. First, it uses conditional DAG nodes to model the execution of computational pipelines based on context, while the stochasticity of DAG edges captures the uncertain nature of a system’s environment or the reliability of its hardware. Second, considering the pessimism of deterministic worst-case execution time (WCET), it uses probability distributions to model the execution times of subtasks (DAG nodes). We propose a new partitioning algorithm Least Latency Partitioned (LLP), which considers precedence constraints among nodes during the allocation process. Coupled with a scheduling algorithm that accounts for varying subtask criticality and constraints, the end-to-end latencies of safety-critical paths/nodes are then minimized. We use tasksets inspired by real robotics to demonstrate that our framework allows for more flexible representation of timing constraints, and safety-performance tradeoffs. © 2024, The Authors. All rights reserved.

关键词： scheduling algorithms

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Multi-Core ECU Load-Reducing Method using Time-Division Processing 17

Multi-Core ECU Load-Reducing Method using Time-Division Proc...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Kim, JongHun Do, YoungSoo Kim, MinHo Jeon, JaeWook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Vehicle network systems are becoming increasingly complex by considering all the surrounding environmental factors necessary for driving. As a result, the number of electronic control units and data processing required in the vehicle is increasing, and the risk of falling into an overload state greatly increases. Most scheduling algorithms to process tasks cannot perform well under overload. Even one device can significantly impact the entire system if it causes performance problems. Therefore, providing a load control method for each device is one of the most important tasks. Simply controlling the load by reducing traffic has not been a good solution, and more efficient ways to control the load on the device are required. In this paper, we propose a method for integrating duplicate tasks and reducing the load while complying with thresholds through multi-core-based time-division processing. © 2023 IEEE.

关键词： scheduling algorithms

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Comparative Study on Calculating CPU Burst Time using Different Machine Learning algorithms 14

Comparative Study on Calculating CPU Burst Time using Differ...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Vayadande, Kuldeep Agarwal, Naman Shevate, Srushti Sawakare, Chinmayee Parakh, Piyush Khandare, Snehankit Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

This paper gives a thorough analysis of works on the subject of expert assessment of CPU burst time using various machine learning algorithms. Knowing how long the CPU bursts for the processes will last is necessary for some CPU scheduling algorithms like SJF and SRTF to function. In particular, the non-preemptive SJF scheduling algorithm estimates the process that will be performed by the CPU in the least amount of burst time. One effective way of predicting CPU burst duration is an ML-based algorithm that estimates the burst-time of the processes. Throughout the study, we discovered that the effectiveness of different machine-learning approaches relies on the applications to which they are put. Our examination of the literature not only argues that these methods are competitive with conventional estimators on a single data set, but also demonstrates that they are responsive to the training data. © Grenze Scientific Society, 2023.

关键词： scheduling algorithms

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Introducing Guard Frames to Ensure Schedulability of All TSN Traffic Classes 28

Introducing Guard Frames to Ensure Schedulability of All TSN...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Bujosa, Daniel Proenza, Julian Papadopoulos, Alessandro V. Nolte, Thomas Ashjaei, Mohammad Mälardalen University Västerås Sweden University of the Balearic Islands Palma Spain

ISBN: (纸本)9798350339918

Offline scheduling of Scheduled Traffic (ST) in Time-Sensitive Networks (TSN) without taking into account the quality of service of non-ST traffic, e.g., time-sensitive traffic such as Audio-Video Bridging (AVB) traffic, can potentially cause deadline misses for non-ST traffic. In this paper, we report our ongoing work to propose a solution that, regardless of the ST scheduling algorithm being used, can ensure meeting timing requirements for non-ST traffic. To do this, we define a frame called Guard Frame (GF) that will be scheduled together with all ST frames. We show that a proper design for the GFs will leave necessary porosity in the ST schedules to ensure that all non-ST traffic will meet their timing requirements. © 2023 IEEE.

关键词： scheduling algorithms

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Particle Swarm Optimization for Parallel Machine scheduling Problem with Machine Eligibility Constraints

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Chinese Journal of Electronics 2023年第1期19卷 1-4页

作者： Jinghua Hao Min Liu WU Cheng Department of Automation Tsinghua University and TNLIST Beijing China

Particle swarm optimization is a popular global optimization technology in continuous and discrete optimization fields in recent years. This paper presents a particle swarm optimization-based scheduling algorithm for large-scale parallel machine scheduling problem with machine eligibility constraints and the objective of minimizing the total weighted tardiness. In the proposed method, we first design a Machine assignment heuristic (MAH) which is used to assign a processing machine of each job based on the dynamic load-balancing mechanism, and then we propose a particle swarm optimization algorithm to optimize the sequence of all jobs in which MAH is used in the decoding process of each particle, also, the mechanisms of particle-moving and velocity-updating are devised based on the problem characteristics. Numerical computational results show that the proposed algorithm is effective for large-scale parallel machine scheduling problems with machine eligibility constraints.

关键词： scheduling algorithms Heuristic algorithms Design methodology Production Parallel machines scheduling Decoding

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Partition scheduling Algorithm for Shared Resources in Real-Time Systems

Partition Scheduling Algorithm for Shared Resources in Real-...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ma, Kun Hu, Wei Liu, Jing Gan, Yu Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430065 Hubei Peoples R China

ISBN: (纸本)9781665442077

For a set of periodic real-time tasks running on a multi-processor system, some tasks need access to shared resources, while the remaining tasks do not. This article aims to solve the problem of priority inversion caused by simultaneous access to shared resources by tasks in a multi-processor real-time system. We propose a task allocation model and partition scheduling algorithm based on the MSRP protocol, which is called SASR-MSRP. Firstly, the algorithm divides the task set into two categories based on whether the task accesses shared resources or not. Secondly, calculate the system utilization rate U of the task that accesses the shared resource and determine the execution priority of the task according to its non-increasing order and assign it to the corresponding processor. Finally, we use the EDF scheduling algorithm to sequentially allocate the remaining independent tasks to the idle time period of the application processor. This algorithm not only reduces the problem of priority inversion, but also improves the overall scheduling efficiency of the system.

关键词： scheduling algorithms

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Power Control and Node scheduling in Uplink Cell-free Massive MIMO with Centralized Beamforming

Power Control and Node Scheduling in Uplink Cell-free Massiv...

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IEEE International Conference on Communications (ICC)

作者： Guenach, M. Gorji, A. Bourdoux, A. Imec Leuven Leuven Belgium TELIN Ghent Univ Ghent Belgium

ISBN: (纸本)9781728171227

Fronthauling the traffic from/to large number of distributed access points (AP)s in the cell-free (CF) massive Multiple-Input Multiple-Output (MIMO) is one of the main challenges hindering the deployment of CF massive MIMO systems. As the point-to-point fronthaul architecture is not cost-effective, there is a need to move towards shared fronthaul mediums or a point-to-multipoint architecture that requires the proper user-AP scheduling to improve the overall performance while a constraint on the fronthaul bandwidth must also be respected. In this paper, a low-bandwidth system with centralized digital beamforming is considered and a novel algorithm is proposed for the joint power and AP scheduling problem. The proposed optimization framework provides a scalable solution for CF massive MIMO under stringent fronthaul bandwidth constraints.

关键词： Array signal processing scheduling algorithms Simulation Process control Massive MIMO Bandwidth scheduling

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Motion-Prediction-based Wireless scheduling for Multi-User Panoramic Video Streaming 40

Motion-Prediction-based Wireless Scheduling for Multi-User P...

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40th IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Chen, Jiangong Qin, Xudong Zhu, Guangyu Ji, Bo Li, Bin Univ Rhode Isl Dept ECBE Kingston RI 02881 USA Univ Rhode Isl Dept CSS Kingston RI 02881 USA Virginia Tech Dept CS Blacksburg VA USA

ISBN: (纸本)9780738112817

Multi-user panoramic video streaming demands 4 similar to 6x bandwidth of a regular video with the same resolution, which poses a significant challenge on the wireless scheduling design to achieve desired performance. On the other hand, recent studies reveal that one can effectively predict the user's Field-of-View (FoV) and thus simply deliver the corresponding portion instead of the entire scenes. Motivated by this important fact, we aim to employ autoregressive process for motion prediction and analytically characterize the user's successful viewing probability as a function of the delivered portion. Then, we consider the problem of wireless scheduling design with the goal of maximizing application-level throughput (i.e., average rate for successfully viewing the desired content) and service regularity performance (i.e., how often each user gets successful views) subject to the minimum required service rate and wireless interference constraints. As such, we incorporate users' successful viewing probabilities into our scheduling design and develop a scheduling algorithm that not only asymptotically achieves the optimal application-level throughput but also provides service regularity guarantees. Finally, we perform simulations to demonstrate the efficiency of our proposed algorithm using a real dataset of users' head motion.

关键词： Wireless communication scheduling algorithms Conferences Streaming media Throughput Prediction algorithms scheduling

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A Sum-of-Ratios Multi-Dimensional-Knapsack Decomposition for DNN Resource scheduling 40

A Sum-of-Ratios Multi-Dimensional-Knapsack Decomposition for...

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40th IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Yu, Menglu Wu, Chuan Ji, Bo Liu, Jia Iowa State Univ Dept Comp Sci Ames IA 50011 USA Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Virginia Tech Dept Comp Sci Blacksburg VA USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

ISBN: (纸本)9780738112817

In recent years, to sustain the resource-intensive computational needs for training deep neural networks (DNNs), it is widely accepted that exploiting the parallelism in large-scale computing clusters is critical for the efficient deployments of DNN training jobs. However, existing resource schedulers for traditional computing clusters are not well suited for DNN training, which results in unsatisfactory job completion time performance. The limitations of these resource scheduling schemes motivate us to propose a new computing cluster resource scheduling framework that is able to leverage the special layered structure of DNN jobs and significantly improve their job completion times. Our contributions in this paper are three-fold: i) We develop a new resource scheduling analytical model by considering DNN's layered structure, which enables us to analytically formulate the resource scheduling optimization problem for DNN training in computing clusters;ii) Based on the proposed performance analytical model, we then develop an efficient resource scheduling algorithm based on the widely adopted parameter-server architecture using a sum-of-ratios multi-dimensional-knapsack decomposition (SMD) method to offer strong performance guarantee;iii) We conduct extensive numerical experiments to demonstrate the effectiveness of the proposed schedule algorithm and its superior performance over the state of the art.

关键词： Training Deep learning Analytical models Schedules scheduling algorithms Parallel processing scheduling

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