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arXiv 2024年

作者： Chen, Qingyun Im, Sungjin Petety, Aditya Electrical Engineering and Computer Science University of California 5200 N. Lake Road MercedCA95344 United States

In this paper, we explore how a natural generalization of Shortest Remaining Processing Time (SRPT) can be a powerful meta-algorithm for online scheduling. The meta-algorithm processes jobs to maximally reduce the objective of the corresponding offline scheduling problem of the remaining jobs: minimizing the total weighted completion time of them (the residual optimum). We show that it achieves scalability for minimizing total weighted flow time when the residual optimum exhibits supermodularity. Scalability here means it is O(1)-competitive with an arbitrarily small speed augmentation advantage over the adversary, representing the best possible outcome achievable for various scheduling problems. Thanks to this finding, our approach does not require the residual optimum to have a closed mathematical form. Consequently, we can obtain the schedule by solving a linear program, which makes our approach readily applicable to a rich body of applications. Furthermore, by establishing a novel connection to substitute valuations in Walrasian markets, we show how to achieve supermodularity, thereby obtaining scalable algorithms for various scheduling problems, such as matroid scheduling, generalized network flow, and generalized arbitrary speed-up curves, etc., and this is the first non-trivial or scalable algorithm for many of them. Copyright © 2024, The Authors. All rights reserved.

关键词： scheduling algorithms

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A Dynamic Priority Packet scheduling for UAV Assisted AoI-Aware Network: A Deep Reinforcement Learning Approach

A Dynamic Priority Packet Scheduling for UAV Assisted AoI-Aw...

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IEEE Conference on Vehicular Technology (VTC)

作者： Xiaoying Fu Jiansong Miao Yushun Yao Tao Zhang Shanling Bai Lan Yi School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

When ground base stations are not available in the aftermath of a disaster, unmanned aerial vehicle (UAV) acting as flying relay is a promising option. The UAVs with limited energy as flying relays allow for wider data coverage and more stable data transmission. However, with the changes of ground devices topology and channel, it is challenging to consider quality of service (QoS) and the age of information (AoI) in UAV communication under the energy constraint. In this paper, we propose a dynamic priority packet scheduling for UAV assisted AoI-aware network whose utility is maximized subject to QoS to get the best tradeoff of the energy consumption and the weighted AoI. Specifically, the dynamics of devices are characterized by Gauss-Markov mobility model. Dynamic priority is affected by devices' movement, channel changes and others. We optimize the trajectory of the UAV and the scheduling scheme of the packets by the Dueling Double Deep Q Network (D3QN) algorithm. Simulations show that the scheme significantly improves the utility of the system compared to the benchmarks.

关键词： Energy consumption scheduling algorithms Mobility models Quality of service Benchmark testing Autonomous aerial vehicles Dynamic scheduling

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Timetable generator and optimizer for Hungarian university students 17

Timetable generator and optimizer for Hungarian university s...

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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Sarkany, Marcell Csaba Kovacs, Andras Óbuda University John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350321104

This paper presents the design and development of a web-based system that allows Hungarian university students to create personalized and optimized timetables during the subject-taking period. The system is based on a backtracking algorithm that considers the user's preferences and constraints. The requirements specification, use cases, data structures, and architecture of the system are detailed, as well as the selection of the *** Core and Angular frameworks. The system was tested with real data and results showed that the stricter the user's preferences, the faster the results were generated. The paper concludes with a summary of the system's development and testing documentation. © 2023 IEEE.

关键词： scheduling algorithms

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Energy-Aware DNN Task scheduling with Dynamic Batching and Frequency Adjustment

Energy-Aware DNN Task Scheduling with Dynamic Batching and F...

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IEEE International Symposium on Quality Electronic Design

作者： Vasileios Pentsos Spyros Tragoudas Kiriti Nagesh Gowda Mike Schmit School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale IL USA ML Computer Vision Group Advanced Micro Devices Inc. Santa Clara CA USA

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

This work presents an energy-aware scheduling algorithm for deep neural network (DNN) inference tasks under energy, memory, and deadline constraints. By leveraging a precomputed lookup table (LUT) of performance metrics, the scheduler dynamically optimizes batch sizes, selectively applies concurrency, and adjusts GPU frequency as a last resort. The proposed approach maximizes batch sizes up to an empirically determined threshold and executes tasks concurrently when task deadlines permit and memory constraints allow. GPU frequency scaling is applied only when neither batch size optimization nor concurrency can ensure deadline compliance. Experiments on CIFAR-10 and CIFAR-100 demonstrate the scheduler's ability to meet deadlines while minimizing energy consumption.

关键词： Concurrent computing scheduling algorithms Heuristic algorithms Memory management Graphics processing units Artificial neural networks Dynamic scheduling Energy efficiency Table lookup Optimization

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Implementing Resource-Aware scheduling Algorithm for Improving Cost Optimization in Cloud Computing

Implementing Resource-Aware Scheduling Algorithm for Improvi...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Kiran Jagannadha Reddy Pamula Udayaraju Venkata Pradeep Reddy Pamaiahgari V. Dilip Kumar Walmart Bentonville AR USA Department of CSE SRM University AP India Wendys International LLC OH USA Department of CSE SRKR Engineering College

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

An uncountable number of computational resources are shared for various applications using a transformative technology called Cloud Computing. It is an emerging technology that can offer scalable and on-demand resources. However, cost optimization and resource allocation are critical issues due to the increasing number of cloud user and their request. It can be solved by optimising resource allocation by reducing the task/user queue. It is possible only if the requested resource is free; otherwise, it must schedule the same or relevant resource. This paper demonstrates a Resource-Aware scheduling (RAS) algorithm to increase the speed of resource allocation by mapping the user request with the resource availability. The proposed algorithm examines the log information of the resources, workload behaviour, resource availability, price, task efficiency, and reduced wastage and allocates the resources dynamically. To do that, it maps user information with resource information and schedules based on availability and priority. The simulation-based experiment is carried out in a private cloud space and demonstrates the resource allocation, utilization, response time, and cost. It is also compared with the traditional scheduling algorithm to evaluate the performance of the RAS. The evaluation found that the RAS model is more suitable for cloud resource allocation.

关键词： Cloud computing Adaptation models Schedules Costs scheduling algorithms Computational modeling Heuristic algorithms Resource management Time factors Optimization

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Learning-Based Algorithm Selection for a Multiprocessor scheduling Problem 19thth

Learning-Based Algorithm Selection for a Multiprocessor Sc...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Braune, Roland University of Applied Sciences Upper Austria Operations Management Wels Austria Department of Business Decisions and Analytics University of Vienna Vienna Austria Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Vienna Vienna Austria

ISBN: (纸本)9783031838873

This paper addresses a multiprocessor scheduling problem with real-world-inspired constraints. A learning-based prediction approach is proposed to support an effective algorithm selection during problem-solving. Classifiers are trained to predict whether a particular solver should be employed. The core of the contribution is the selection and engineering of features based on the structural properties of problem instances. These features are used as predictors in several well-known binary classification approaches. Computational experiments based on randomly generated problem instances reveal accuracy scores of up to 90% and indicate the potential time savings when embedding the proposed classifiers into the overall solution procedure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： scheduling algorithms

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A DQN-based resource scheduling optimization algorithm for multi-computing nodes of large AI models

A DQN-based resource scheduling optimization algorithm for m...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： YuBin Gong JingJing Liu Xu Huang XueKe Bi WenBo Cui College of Electronic and Information Engineering Beijing Jiaotong University Beijing China China Mobile Group Liaoning Company Limited China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In the booming era of artificial intelligence, the demand for computing resources in AI large-model training is increasing exponentially. However, the traditional centralized computing resource management model fails to meet this growing demand. This paper focuses on the resource utilization issue of multiple computing center nodes and introduces the Deep Q-Network (DQN) algorithm to optimize the logic and strategy of computing task scheduling. A system model encompassing a specific state space, action space, and reward mechanism is established. A one-dimensional DNN network serves as the DQN training model, which is trained by means of the Mean Squared Error (MSE) loss function and the RMSProp optimizer. In the experiment, the DQN is compared with five traditional algorithm models. The results demonstrate that the DQN exhibits the smallest and most balanced fluctuations in resource utilization, boasting the best performance. Consequently, it offers an effective solution for computing resource management.

关键词： Training Fluctuations Mechatronics scheduling algorithms Computational modeling Reinforcement learning Reliability engineering scheduling Resource management Optimization

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An Energy-Efficient Virtual Machine scheduling Algorithm in Cloud Data Center

An Energy-Efficient Virtual Machine Scheduling Algorithm in ...

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International Conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Md. Adnan Morshed Anabil Sarker Mehzabin Meem Fahad Ahammed K. M. Safin Kamal Ahmed Wasif Reza Dept. of CSE East West University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Power consumption has a significant influence on resource allocation, which has a negative effect on the environment. To lessen the negative effects, an effective resource allocation algorithm is needed. In this paper, we suggested a unique hybrid approach for energy-efficient scheduling of virtual machines (VMs) in cloud data centers called Energy Efficient Particle Swarm Optimization (EE-PSO), which combines Genetic algorithms (GA) and Particle Swarm Optimization (PSO). This integration is special since it makes use of both algorithms’ advantages to improve scheduling effectiveness and energy consumption. The novelty lies in how GA and PSO are combined, which differs from previous attempts where these algorithms were either used separately or in a less integrated manner. EE-PSO represents significant achievement toward achieving the goals of decreasing power usage and promoting green computing practices.

关键词： Data centers Cloud computing scheduling algorithms Scalability Energy efficiency Virtual machines scheduling Resource management Particle swarm optimization Genetic algorithms

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A Robust Multi-market scheduling Strategy for Battery Energy Storage Systems

A Robust Multi-market Scheduling Strategy for Battery Energy...

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IEEE General Meeting Power& Energy Society

作者： Sunho Jang Siddharth Bhela Nan Xue Electrical & Computer Engineering University of Michigan Ann Arbor MI USA Siemens Technology NJ USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

In this paper, we propose a novel robust battery energy storage system (BESS) scheduling algorithm that makes offers to multiple ancillary service markets. The proposed algorithm enables BESS to provide frequency regulation and synchronized reserve services while concurrently fulfilling commitments to a long-term capacity market without violating any operational constraints. In particular, the offers to the ancillary service markets scheduled by the proposed algorithm remain valid under any possible frequency regulation signal and synchronized reserve events within the defined uncertainty set. The case study demonstrates that the proposed algorithm is robust to the given uncertainties and outperforms two selected benchmarks.

关键词： Uncertainty Battery energy storage system scheduling algorithms Benchmark testing Robustness Real-time systems Planning Synchronization Frequency control

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An Efficient Task scheduling for Security and Stability Control System Models on Multi-Core Systems

An Efficient Task Scheduling for Security and Stability Cont...

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International Conference on Dependable Systems and Their Applications (DSA)

作者： Zhi Wang Bo Shen College of Computer Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798331532390

ISBN: (纸本)9798331532406

In recent years, the large-scale integration of new energy sources, such as wind and photovoltaic power into power grid, has necessitated higher standards for the safe, stable, and flexible control of power systems. The Security and Stability Control System (SSCS) is proposed to monitor the operation of the grid. To address the cost and scale limitations, simulation has been emerging as a crucial method for analyzing safety and stability control systems. However, as the grid’s complexity increases, the precision and computation workload of the SSCS associated to the grid simulation model have significantly grown. Real-time task scheduling in multi-core systems is important for detecting abnormal events of complex stability control simulation models in time. This paper introduces a new scheduling algorithm specifically designed for the simulation model in power system. It also proposes an interference analysis method to address delays caused by contention for shared hardware resources. We conduct extensive experiments to analyze the proposed algorithm. Compared with existing scheduling algorithms, the scheduling algorithm of the paper achieves superior performance and effectiveness.

关键词： Upper bound scheduling algorithms Computational modeling Stability criteria Interference Power system stability Control systems Real-time systems Power grids Security

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