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Average AoI Minimization With Directional Charging for Wireless-Powered Network Edge

IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第6期24卷 4889-4906页

作者： Chen, Quan Guo, Song Xu, Wenchao Li, Jing Shi, Tuo Gao, Hong Cai, Zhipeng Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong 999077 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321017 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

Age of Information (AoI) has been proposed as a new performance metric to capture the freshness of data. At wireless-powered network edge, the source nodes first need to be charged ready for update transmissions, which means the system AoI is not only decided by the scheduling of update transmissions but also by the designing of charging plan. However, the existing works either only focused on the point of scheduling 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 AoI optimization problem at wireless-powered network edge with directional charging. First, the theoretical bound of the weighted sum of average AoI of the entire 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 computation 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 to compute a periodical charging schedule while the average AoI is bounded, including a charging period T and the charging orientations assigned to each time slot within T. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Furthermore, several approximate algorithms are also proposed for average AoI optimization with multiple chargers and network bandwidth constraint. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI.

关键词： Optimization Approximation algorithms Bandwidth scheduling scheduling algorithms Schedules Computer science Mobile computing Minimization Lower bound Age of Information (AoI) average AoI directional charging wireless-powered network

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Optimal Latency-Oriented Coding and scheduling in Parallel Queuing Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第10期70卷 6471-6488页

作者： Bedin, Andrea Chiariotti, Federico Kucera, Stepan Zanella, Andrea Univ Padua Dept Informat Engn I-35151 Padua Italy Nokia Bell Labs Espoo 02610 Finland Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark Nokia Bell Labs D-81371 Munich Germany

The evolution of 5G and Beyond networks has enabled new applications with stringent end-to-end latency requirements, but providing reliable low-latency service with high throughput over public wireless networks is still a significant challenge. One of the possible ways to solve this is to exploit path diversity, encoding the information flow over multiple streams across parallel links. The challenge presented by this approach is the design of joint coding and scheduling algorithms that adapt to the state of links to take full advantage of path diversity. In this paper, we address this problem for a synchronous traffic source that generates data blocks at regular time intervals (e.g., a video with constant frame rate) and needs to deliver each block within a predetermined deadline. We first develop a closed-form performance analysis in the simple case of two parallel servers without any buffering and single-packet blocks, and propose a model for the general problem based on a Markov Decision Process (MDP). We apply policy iteration to obtain the coding and scheduling policy that maximizes the fraction of source blocks delivered within the deadline: our simulations show the drawbacks of different commonly applied heuristic solutions, drawing general design insights on the optimal policy.

关键词： Encoding Reliability Optimal scheduling Delays Servers Mathematical models Termination of employment Low-latency communication scheduling algorithms optimal scheduling

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FlexMRS: Multi-Objective Optimization for Diverse Application Requirements in MPTCP scheduling

FlexMRS: Multi-Objective Optimization for Diverse Applicatio...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Zhang, Zekun Zhong, Lujie Ji, Xiang Capital Normal University Information Engineering College Beijing China Beijing University of Posts and Telecommunications School of Computer Science Beijing China

ISBN: (纸本)9798350367942

Multipath TCP (MPTCP) technology efficiently leverages multiple paths for data transmission, achieving commendable throughput performance. However, existing MPTCP scheduling algorithms fail to meet the diverse and evolving demands of modern applications, which often extend beyond throughput to include preferences for low latency or stability. To address this gap, we introduce FlexMRS, an intelligent multi-objective MPTCP scheduling algorithm. Unlike prevailing algorithms that rely on black-box machine learning models, FlexMRS innovatively utilizes the Lagrangian relaxation method to overcome the traditional limitations of balancing multiple objectives in machine ***, we introduce the Krylov subspace iteration to obtain obtain the local Pareto optimal solution set, thus make FlexMRS could effectively address the switching convergence problem of different application requirement models. © 2024 IEEE.

关键词： scheduling algorithms

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Real-time and Energy-aware scheduling for Edge-to-Cloud Continuum based on Reinforcement Learning 28

Real-time and Energy-aware Scheduling for Edge-to-Cloud Cont...

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28th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： Panceri, Andrea Mattia, Gabriele Proietti Beraldi, Roberto Sapienza University of Rome Control and Management Engineering "A. Ruberti" Department of Computer Italy

ISBN: (纸本)9798331527211

By spreading out computing workloads over multiple levels, the Edge-to-Cloud continuum paradigm improves the performance of applications that are sensitive to latency. However, real-time scheduling is difficult on the Edge computing layer since it consists of a variety of nodes with varying uptime. In this paper, we address this issue by proposing an online and adaptive scheduling algorithm based on a continuous learning Reinforcement Learning. Our algorithm determines each work request individually, optimizing scheduling policies to meet real-time application requirements while taking environmental energy and battery limits into account. We validate the efficacy of our approach in dynamically assigning tasks, particularly in scenarios where Edge nodes exhibit variable speeds and unpredictable failures, while efficiently managing energy resources and battery constraints through extensive simulations and comparisons with static scheduling strategies. © 2024 IEEE.

关键词： scheduling algorithms

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Packet scheduling in the Underwater Network Using Active Priority and QLS-Based Energy-Efficient Backpressure Technique 6th

Packet Scheduling in the Underwater Network Using Active Pri...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Caroline Mary, A. Senthil Kumar, A.V. Saleh, Omar S. Research Scholar Department of Computer Science Hindusthan College of Arts and Science Coimbatore India Assistant Professor Department of Applied Mathematics and Computational Sciences PSGCollege of Technology Coimbatore India Professor and Director Department of MCA Hindusthan College of Arts and Science Coimbatore India Ministry of Higher Education and Scientific Research Baghdad Iraq

ISBN: (纸本)9789819940707

Underwater communications are playing a major role in recent times as underwater happenings have gained interest. Especially in scenarios based on natural calamities like tsunamis, pollution detection, and surveillance, underwater communications are indispensable. Parameters like battery life span and capabilities of sensor nodes play a vital role in the underwater environment. QoS has to be taken care of in underwater communication. Congestion avoidance helps in using energy efficiently in underwater communication. Simulation results prove that this algorithm overcomes the drawback of a normal backpressure-based, static priority algorithm where packet loss, delay, the energy of nodes, and routing overhead are reduced overall, and packet delivery ratio and throughput are increased in special cases of real-time important packets. As underwater nodes are battery-operated, it is essential to have energy-efficient scheduling algorithm. The proposed algorithm solves the drawback of the backpressure algorithm by having energy-efficient queue length stabilizer (QLS) technique, where the backlog is adjusted such that no queues are affected and the energy consumption is less. And real-time packets are treated effectively such that real-time applications can be supported using this scheduling algorithm. The novelty of the proposed method is congestion control methodology with active priority and energy efficiency for an underwater network for effective scheduling of packets where queue backlog size is also adjusted virtually based on energy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： scheduling algorithms

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Opposition Strategy with Harris Hawk Algorithm for Task scheduling in Edge Computing

Opposition Strategy with Harris Hawk Algorithm for Task Sche...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Padmakala, S. Khond, Smita Al-Farouni, Mohammed Punitha, S. Kaliappan, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Malla Reddy Engineering College for Women Department of Cse Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Deemed to Be University Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation Department of Mathematics Salem India Lovely Professional University Department of Research and Development Phagwara India

ISBN: (纸本)9798350375442

Edge computing (EC) nodes undertake an increasing number of tasks with rise of business density. However, unreasonable edge computing resource allocation suffers from high energy consumption and resource waste. This paper, Opposition-Based Learning with Harris Hawk Algorithm (OHHO) using task scheduling in EC, distributed involves complex and dynamic environment with changing tasks and resources availability. The HHO has mechanisms to maintain exploration and exploitation, reduce high dimensionality in search space where algorithm avoids to fall in local optima. OBL helps in this dynamic context by ensuring suboptimal solutions are quickly discarded and replaced with their opposites, which allows algorithm to adapt better to recent situation. This is adaptability is crucial in edge computing, where tasks and resources are constantly fluctuating. The resource allocation involved Dynamic Priority Task scheduling Algorithm (DPTSA) helps in balancing system load by distributing tasks based on their priority and availability of resources. The proposed OHHO algorithm achieves high performance such energy 0.1mJ, Makespan of 1s and Delay of 45ms respectively when compared to existing techniques Deep Q Network (DQN) and Particle Swarm Optimization (PSO) - Grey Wolf Optimizer (GWO) approach. © 2024 IEEE.

关键词： scheduling algorithms

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Research on MES Production scheduling Algorithm Based on Simulated Automotive Production Line Final Assembly Process 4

Research on MES Production Scheduling Algorithm Based on Sim...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Li, Jinyi Wang, Jing Zhang, Chunxi Wu, Guochen Zhang, Yunming Tianjin University of Technology and Education China Beijing Polytechnic College of Automotive Engineering Beijing China

ISBN: (纸本)9798350363586

This study delves into the importance of the final assembly process for automotive production lines and the application of MES production scheduling algorithms. Firstly, the process of automotive production line assembly technology and the implementation of flexible production were outlined. Secondly, a detailed study was conducted on the architecture and functions of the MES system, and a production scheduling algorithm was designed for simulating the assembly process of automotive production lines. The algorithm was also optimized and analyzed. Through experimental verification, this study evaluated the performance and optimization effect of the algorithm, and compared and analyzed it with other algorithms, verifying the superiority of this study. Finally, the main achievements of the research were summarized, and the application prospects of MES production scheduling algorithm in the final assembly process of automotive production lines were discussed. © 2024 IEEE.

关键词： scheduling algorithms

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Throughput-Optimal scheduling via Rate Learning 63

Throughput-Optimal Scheduling via Rate Learning

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Promponas, Panagiotis Valls, Victor Nikolakakis, Konstantinos Kalogerias, Dionysis Tassiulas, Leandros Yale University United States Ibm Research Europe Dublin Ireland

ISBN: (纸本)9798350316339

We study the problem of designing scheduling policies for communication networks. This problem is often addressed with max-weight-type approaches since they are throughput-optimal. However, max-weight policies make scheduling decisions based on the network congestion, which can be sometimes unnecessarily restrictive. In this paper, we present a 'schedule as you learn' (SYL) approach, where we learn an average rate, and then select schedules that generate such a rate in expectation. This approach is interesting because scheduling decisions do not depend on the size of the queue backlogs, and so it provides increased flexibility to select schedules based on other criteria or rules, such as serving high-priority queues. We illustrate the results with numerical experiments for a cross-bar switch and show that, compared to max-weight, SYL can achieve lower latency to certain flows without compromising throughput optimality. © 2024 IEEE.

关键词： scheduling algorithms

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Optimization of Work Shift scheduling under Multiple Constraints Using Genetic Algorithm 14

Optimization of Work Shift Scheduling under Multiple Constra...

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14th International Workshop on Computer Science and Engineering, WCSE 2024

作者： Akiyama, Hiroya Kamioka, Eiji Tran, Chanh Minh Tan, Phan Xuan Graduate School of Engineering and Science Shibaura Institute of Technology Japan College of Engineering Shibaura Institute of Technology Japan

ISBN: (纸本)9789819411566

Work shift scheduling is an essential task in businesses that incorporate both full-time and part-time workers. Work shifts are managed by the store staff and are influenced by various factors. Therefore, as the number of employees and factors to consider increases, the burden of management also grows. Additionally, the importance of each factor varies depending on the preferences of the individual managing the shifts. Thus, this study aimed to automate the process of work shift scheduling optimally based on the importance of each factor. In this paper, a new optimization method is proposed that considers the priority of each factor, represented by an objective function, using a set of Pareto-optimal solutions. Evaluation experiments were conducted using a simulated store, comparing the results of work shifts obtained by the proposed method to those obtained by the existing weighted sum method. The experimental results showed that the proposed method is particularly effective in optimizing multiple objective functions with strong trade-off relations by considering the priority of each objective function. © 2024 14th International Workshop on Computer Science and Engineering, WCSE 2024. All rights reserved.

关键词： scheduling algorithms

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Resource scheduling of High-Performance Computing Clusters based on the Combination of Ant Colony Algorithm and Active Caching Strategy 3

Resource Scheduling of High-Performance Computing Clusters b...

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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Bai, Xuemei Zhou, Jingbo Wang, Zhijun School of Electronic Information Engineering Changchun University of Science and Technology Changchun130012 China School of Coumputer Science and Technology Changchun Normal University Changchun130123 China

ISBN: (纸本)9798331506582

In recent years, the HPC cluster has developed rapidly and has become an important tool to solve large-scale and complex computing problems. However, in a cluster environment, how to efficiently schedule HPC cluster resources has become a problem worthy of attention. Based on this, this paper proposes a scheduling strategy that integrates the ant colony algorithm and active cache to minimize resource scheduling time and maximize resource utilization. Simulation results show that compared with the ant colony algorithm, the new strategy proposed in this paper can improve the efficiency of resource scheduling, significantly reduce the scheduling time, greatly improve the resource utilization rate, and significantly reduce the job loss rate. © 2024 IEEE.

关键词： scheduling algorithms

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