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arXiv 2024年

作者： Li, Pochun Xiao, Yuyang Yan, Jinghua Li, Xuan Wang, Xiaoye Northeastern University Boston United States University of Illinois Urbana-Champaign Urbana United States University of Utah Salt Lake City United States Columbia University New York United States Western University London Canada

This study presents a novel computer system performance optimization and adaptive workload management scheduling algorithm based on Q-learning. In modern computing environments, characterized by increasing data volumes, task complexity, and dynamic workloads, traditional static scheduling methods such as Round-Robin and Priority scheduling fail to meet the demands of efficient resource allocation and real-time adaptability. By contrast, Q-learning, a reinforcement learning algorithm, continuously learns from system state changes, enabling dynamic scheduling and resource optimization. Through extensive experiments, the superiority of the proposed approach is demonstrated in both task completion time and resource utilization, outperforming traditional and dynamic resource allocation (DRA) algorithms. These findings are critical as they highlight the potential of intelligent scheduling algorithms based on reinforcement learning to address the growing complexity and unpredictability of computing environments. This research provides a foundation for the integration of AI-driven adaptive scheduling in future large-scale systems, offering a scalable, intelligent solution to enhance system performance, reduce operating costs, and support sustainable energy consumption. The broad applicability of this approach makes it a promising candidate for next-generation computing frameworks, such as edge computing, cloud computing, and the Internet of Things. Copyright © 2024, The Authors. All rights reserved.

关键词： scheduling algorithms

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A probabilistic analysis on general probabilistic scheduling problems

arXiv

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arXiv 2024年

作者： Suruga, Daiki

The scheduling problem is a key class of optimization problems and has various kinds of applications both in practical and theoretical scenarios. In the scheduling problem, probabilistic analysis is a basic tool for investigating performance of scheduling algorithms, and therefore has been carried out by plenty amount of prior works. However, probabilistic analysis has several potential problems. For example, current research interest in the scheduling problem is limited to i.i.d. scenarios, due to its simplicity for analysis. This paper provides a new framework for probabilistic analysis in the scheduling problem and aims to deal with such problems. As a consequence, we obtain several theorems including a theoretical limit of the scheduling problem which can be applied to general, non-i.i.d. probability distributions. Several information theoretic techniques, such as information-spectrum method, turned out to be useful to prove our results. Since the scheduling problem has relations to many other research fields, our framework hopefully yields other interesting applications in the future. © 2024, CC BY.

关键词： scheduling algorithms

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Load Balancing Job Shop scheduling with AGVs: A Multi-Objective Optimization Approach

Load Balancing Job Shop Scheduling with AGVs: A Multi-Object...

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

作者： Zhenhong Ye Mechanical and Electrical Engineering Institute Ningbo Polytechnic Ningbo China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Introducing AGVs in job shops significantly enhances the production efficiency of manufacturing enterprises. However, the majority of existing studies focus on minimizing the makespan, overlooking the load balancing issue during AGV scheduling. Although a few studies attempt to consider electricity and load constraints, yielding unsatisfactory results. To tackle these challenges, this paper proposes a multi-objective optimization approach for load-balancing job shop scheduling with AGVs. Specifically, we begin by formulating the scheduling as a multi-objective optimization problem. Subsequently, utilizing the Non-dominated Sorting Genetic Algorithm (NSGA-II), we solve the multi-objective optimization by designing encoding rules and tailoring mutation and crossover operations. The case study on a real-world scenario demonstrates the feasibility and efficiency of the proposed method in addressing such problems.

关键词： Job shop scheduling scheduling algorithms Production Load management Real-time systems Mathematical models Manufacturing

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Research on Kubernetes-based Preventive Over-commitment scheduling Algorithm

Research on Kubernetes-based Preventive Over-commitment Sche...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Jianyue Diao School of Software Beijing University of Technology Beijing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

With the rapid advancement of cloud computing [1] and container cloud technology [2], Kubernetes (K8S) [3] has become the preferred container orchestration and management platform for many enterprises and individuals to deploy online applications. It provides comprehensive cloud computing solutions that greatly facilitate the rapid deployment and operation of applications. However, despite the many advantages K8S brings to microservices architecture, stability issues during microservices updates have gradually emerged in practical applications. Especially the default node scheduling mechanism allows for the phenomenon of resource overcommitment, which poses a significant risk to certain types of applications such as memory-intensive applications. Particularly during batch updates of microservices, excessive allocation of node memory resources may lead to severe overcommitment, which can cause Out of Memory (OOM) errors later in the application’s operation, affecting the stability and user experience of online applications. In light of this, this paper focuses on the research to enhance the stability of online applications during the update process of microservices, aiming to optimize the scheduling algorithm of K8S to ensure efficient utilization of resources while effectively reducing the probability of resource overcommitment. This ensures that online applications can smoothly transition through the update cycle, avoiding short-term request exceptions or service interruptions caused by resource overcommitment, thus safeguarding business continuity.

关键词： Cloud computing scheduling algorithms Microservice architectures Computer architecture Containers Stability analysis scheduling User experience Stress Testing

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Spatiotemporal Task scheduling for Green Computing in Computing Power Networks

Spatiotemporal Task Scheduling for Green Computing in Comput...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Wen Wen Renchao Xie Qinqin Tang Zehui Xiong Gaochang Xie Tao Huang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Recently, the advancement of information technologies have accelerated the generation of big data, necessitating substantial computing power. This has spurred the development of Computing Power Networks (CPNs), which can overcome the limitations of computing power isolation. However, CPNs consume significant energy and produce large carbon emissions during big data processing. Therefore, an energy-efficient task scheduling scheme, coupled with the utilization of renewable energy, appears to be particularly necessary. Nevertheless, the interplay between computing power and networks, and the spatiotemporal variations in green CPNs pose a challenge to designing the task scheduling scheme. In this paper, we propose a transferable spatiotemporal task scheduling scheme with a triple selection of CPN nodes, routing paths, and forwarding time of tasks. The scheme can overcome the dynamics of green CPNs, and jointly optimize the energy consumption and carbon emissions with ensuring delay constraints and long-term load balancing. Then, we present a task scheduling algorithm based on improved nondominated sorting genetic algorithm-II (NSGA-II) to solve the problem, and numerical results demonstrate that our scheme is effective in reducing the overall energy consumption and carbon emissions of CPNs.

关键词： Energy consumption Renewable energy sources scheduling algorithms Green products Carbon dioxide Big Data Green computing Routing Dynamic scheduling Spatiotemporal phenomena

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Using Hidden Markov Chain for Improving the Dependability of Safety-Critical WSNs 37th

Using Hidden Markov Chain for Improving the Dependability of...

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Alnader, Issam Lasebae, Aboubaker Raheem, R. Middlesex University The Burroughs Hendon LondonNW4 4BT United Kingdom

ISBN: (纸本)9783031290558

Wireless Sensor Networks (WSNs) are distributed network systems used in a wide range of applications, including safety-critical systems. The latter provide critical services, often concerned with human life or assets. Therefore, ensuring the dependability requirements of Safety critical systems is of paramount importance. The purpose of this paper is to utilize the Hidden Markov Model (HMM) to elongate the service availability of WSNs by increasing the time it takes a node to become obsolete, via optimal load balancing. We propose an HMM-algorithm that, given a WSN, analyses and predicts undesirable situations, notably, nodes dying unexpectedly or prematurely. We apply this technique to improve on the Randomized coverage-based scheduling algorithm (RCS) by C. Lius, a scheduling-based algorithm that has served to improve the lifetime of WSNs. Our experiments show that our HMM technique improves the lifetime of the network, achieved by detecting nodes that die early and rebalancing their load. Our technique can also be used for diagnosis and provide maintenance warnings to WSN system administrators. Finally, our technique can be used to improve algorithms other than the RCS. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： scheduling algorithms

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Optimizing Average AoI with Directional Charging for Wireless-Powered Network Edge 31

Optimizing Average AoI with Directional Charging for Wireles...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Chen, Quan Guo, Song Xu, Wenchao Li, Jing Shi, Tuo Cai, Zhipeng Gao, Hong School of Computers Guangdong University of Technology Guangzhou China Hong Kong Polytechnic University Department of Computing Hong Kong College of Intelligence and Computing Tianjin University Tianjin China Georgia State University Department of Computer Science Atlanta United States School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (纸本)9798350399738

Age of Information, which emerged as a new performance metric to quantify the data freshness, has drawn increasing interests recently. At wireless-powered network edge, the source nodes can only transmit their updates after being charged ready, which means the system AoI is not only determined by the update transmission strategies, but also the charging strategies. However, the existing works either only focused on the point of scheduling of update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average $A$ oI optimization problem at wireless-powered network edge without such limitations in this paper. Firstly, the lower bound of the weighted sum of average $A$ oI of the whole network with a directional charger is analyzed, which is proved to be related to nodes' maximum transmitting interval and the charging strategy. An optimal charging time allocation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed, which can obtain a periodical charging schedule including a charging period $T$ and the charging orientation assigned to each time slot within $T$, while the average AoI is bounded. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI. © 2023 IEEE.

关键词： scheduling algorithms

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Time Quantum Optimization in Round Robin Algorithm

Time Quantum Optimization in Round Robin Algorithm

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Chitaliya, Paarshva Kulkarni, Samruddhi Telang, Arya Zaveri, Dishant Shah, Aditya Deulkar, Khushali Djsce Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350300826

The round robin algorithm for process scheduling is a widely used algorithm to ensure that processes in Operating Systems get equal and ample opportunities for their execution. However, the effectiveness of this goal being achieved depends on the value of the time quantum. This value is often chosen arbitrarily by the user or the system, which leads to the dilution of the salient features of this algorithm. To counteract this, the research paper pursues using various machine learning techniques of both regression and classification to predict the optimum time quantum. This is done to ensure minimum average waiting time for a given set of processes, each identified by a n arrival time and a burst time. This predicted optimum time quantum value is much more beneficial to the working of the algorithm than an arbitrarily chosen value. The paper describes the methodology for data engineering of schedules via randomization, and proposes the techniques used for the prediction of the optimum time quantum on this dataset. The main focus of the paper is on the techniques used and the metrics associated with them. These are theoretically and mathematically examined and explored. The experimental testing of various regression and classification methods have been discussed in the paper. Out of these, the method achieving the best result is found to be Random Forest Classifier with training accuracy of 90.5 percent and test accuracy of 89.2 percent. The projected approach indicates the potential to resolve the issue of high average waiting times in round robin algorithm, which facilitates the enhancement of performance of operating systems. © 2023 IEEE.

关键词： scheduling algorithms

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Research on Distributed Computing Power scheduling Based on Ant Path Optimization

Research on Distributed Computing Power Scheduling Based on ...

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Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), International Conference on

作者： Shijie Gao Wende Wang Cong Hu Chao Chen Xiaoqiang Liu Zihan Jin Guang'ao Li Beijing China-Power Information Technology Co. LTD Beijing China State Grid Liaoning Electric Power CO.LTD Shenyang China information and telecommunication branch State Grid Anhui Electric Power CO.LTD Hefei China North China Electric Power University Beijing China information and telecommunication branch State Grid Liaoning Electric Power CO.LTD Shenyang China

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

This study solves the problem of data fusion caused by multi-source and cross-domain power business data, proposes a cross-domain computing capability model for the entire network based on the data middle platform, realizes the sharing and computing of multi-source data, and provides a new solution for the challenges of data centers. It also designs a cross-domain computing power scheduling algorithm based on ant path optimization, significantly improves the accuracy and stability of computing power scheduling through heuristic information design, etc. Experimental verification shows that the algorithm is superior to existing distributed scheduling algorithms in terms of scheduling overhead, etc., and is suitable for the multi-source cross-domain data fusion computing scenarios in data centers. The innovation lies in the introduction of the path optimization idea of the ant algorithm, overcomes the problems of traditional scheduling algorithms, and provides examples and method support for the optimization of power data management and distributed data processing technology in other fields.

关键词： Data centers Technological innovation scheduling algorithms Heuristic algorithms Distributed databases Data integration scheduling Stability analysis Internet of Things Optimization

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Transmit Power Aware Proportional Fair scheduling Algorithm in LEO Satellite Communication System

Transmit Power Aware Proportional Fair Scheduling Algorithm ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Chun-Tai Liu Jen-Yi Pan Department of Communications Engineering Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng University Chia-Yi Taiwan R.O.C. Department of Communications Engineering National Chung Cheng University Chia-Yi Taiwan R.O.C.

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

The integration of satellite communication systems is gaining prominence in the 5G New Radio (NR) system, particularly for achieving widespread wireless coverage in fifth or next-generation wireless networks. The 3rd Generation Partnership Project initiated the study of satellite communication in Release 15, established the initial standard for satellite communication in Release 17, and has continually enhanced the standard. With the commencement of Release 19 in 2024, one of the focal points is uplink throughput enhancements in the NR Non-Terrestrial Network (NR NTN). Uplink signals in satellite communication encounter significant path loss. They are constrained by limited transmission power, affecting the quality and performance of these signals and preventing the provision of high-data-rate services. An effective scheduling algorithm is imperative to address this challenge and meet the data rate requirements outlined by the International Telecommunication Union Radiocommunication (ITU-R). This paper introduces the Transmit Power Aware Proportional Fair (TPA-PF) scheduling algorithm for low earth orbit satellite communication systems. The proposed algorithm incorporates considerations of uplink transmit power and power headroom reports to determine the optimal number of resource allocations. Simulation results demonstrate the substantial improvement in user throughput and spectral efficiency achieved by the proposed scheduling algorithm, meeting the ITU- R requirements.

关键词： Vehicular and wireless technologies scheduling algorithms Spectral efficiency Wireless networks Satellite broadcasting Low earth orbit satellites Throughput

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