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Age of Information in Multi-Relay Networks with Maximum Age scheduling

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arXiv 2025年

作者： de Jesus, Gabriel Martins Pereira, Felippe Moraes Rebelatto, João Luiz Souza, Richard Demo López, Onel Alcaraz University of Oulu Oulu90014 Finland CPGEI Federal University of Technology - Paraná Curitiba80230-901 Brazil CPGEI and DAELN Federal University of Technology - Paraná Curitiba80230-901 Brazil Florianópolis88040-900 Brazil

We propose and evaluate age of information (AoI)aware multiple access mechanisms for the Internet of Things (IoT) in multi-relay two-hop networks. The network considered comprises end devices (EDs) communicating with a set of relays in ALOHA fashion, with new information packets to be potentially transmitted every time slot. The relays, in turn, forward the collected packets to an access point (AP), the final destination of the information generated by the EDs. More specifically, in this work we investigate the performance of four age-aware algorithms that prioritize older packets to be transmitted, namely max-age matching (MAM), iterative max-age scheduling (IMAS), age-based delayed request (ABDR), and buffered ABDR (B-ABDR). The former two algorithms are adapted into the multi-relay setup from previous research, and achieve satisfactory average AoI and average peak AoI performance, at the expense of a significant amount of information exchange between the relays and the AP. The latter two algorithms are newly proposed to let relays decide which one(s) will transmit in a given time slot, requiring less signaling than the former algorithms. We provide an analytical formulation for the AoI lower bound performance, compare the performance of all algorithms in this set-up, and show that they approach the lower bound. The latter holds especially true for B-ABDR, which approaches the lower bound the most closely, tilting the scale in its favor, as it also requires far less signaling than MAM and IMAS. Copyright © 2025, The Authors. All rights reserved.

关键词： scheduling algorithms

A Multi-Objective Capacity-Constrained Optimization of Corn Planting scheduling

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arXiv 2025年

作者： Cui, Mingshi Qi, Kunting Smucker, Byran Sundarmoorthi, Durai Rutgers University Department of Statistics New BrunswickNJ United States University of Illinois Chicago Department of Computer Science ChicagoIL United States Miami University Department of Statistics OxfordOH United States Washington University in St. Louis Olin Business School St. LouisMO United States

This article describes an improved set of solutions to the problems presented in the 2021 Syngenta Crop Challenge in Analytics (Syngenta, 2021). In particular, we use multiobjective optimization and predictive modeling methods to determine a corn planting schedule. The problem involves the following objectives: i. minimize the median and maximum absolute difference between weekly harvest quantity and the storage capacity, the number of nonzero harvest weeks, and the total amount of corn wasted. This is accomplished while respecting planting windows, expected harvest amounts, the growing degree units required to bring seeds to harvest, and historical weather data. We used a Long Short-Term Memory model to predict growing degree units for 2020 and 2021, based on historical data. Then, we used a genetic algorithm, and an extensive search of the tuning parameter space, to produce a Pareto front of solutions for three distinct optimization models related to the Challenge. We evaluate the quality of the Pareto fronts for each model, and use the results to choose a preferred model and final solution. We also provide comparisons between our final solutions, previous solutions submitted to the Challenge, and solutions from other groups. © 2025, CC BY.

关键词： scheduling algorithms

Optimized scheduling Model of Integrated Energy System in Industrial Park Based on Improved MPA 14

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Optimized Scheduling Model of Integrated Energy System in In...

14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Liang, Shuang Jiang, Qingxuan School of Computer Science Northeast Electric Power University Jilin China

ISBN: (纸本)9798331531935

In the context of the global energy shortage and the increasing emphasis on sustainable development goals, optimizing the scheduling of integrated energy systems in industrial parks has become a critical issue. This paper presents an optimal scheduling model and an improved solution method to enhance energy efficiency in industrial parks. Firstly, we develop an energy efficiency optimization model for the integrated energy system in an industrial park. The model aims to maximize system energy efficiency and minimize operational costs while considering load balance constraints, equipment capacity, operational limits, and renewable energy generation constraints. Secondly, we introduce an enhanced Marine Predators Algorithm (MPA) incorporating an adaptive step-size strategy and a multi-subgroup approach. These improvements enable adaptive adjustment of the iteration step size, accelerating convergence and improving solution accuracy, thus facilitating the efficient solving of the model. Then, The effectiveness of the proposed solution method is validated through standard test functions. Finally, we test the model through simulations. The results show that, when considering both energy efficiency and economic factors, our proposed scheduling model effectively improves energy efficiency. It can theoretically reduce energy costs by 3.13% and save 23.683 tons of standard coal annually, all while maintaining the same output level. © 2025 IEEE.

关键词： scheduling algorithms

Research on Data Center Load Balancing Based on Particle Swarm Optimization Fusion Ant Colony Optimization Algorithm 3

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Research on Data Center Load Balancing Based on Particle Swa...

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2023

作者： Song, Shiyu Wang, Jun School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

In recent years, with the continuous development of big data and cloud computing services, the scale of data centers has become larger and larger, and the rapidly increasing network traffic has also put forward higher requirements for the load balancing technology of data centers. Most of the traditional traffic scheduling algorithms are derived from IP networks. These algorithms do not take into account performance parameters such as link bandwidth and delay of real networks, resulting in very unsatisfactory results. In view of the above problems, this paper proposes a traffic scheduling algorithm based on the Particle Swarm Optimization Fusion Ant Colony Optimization (P-ACO) algorithm, which is mainly for dynamic traffic scheduling of elephant flow. First, the Software Defined Networking (SDN) controller obtains the network performance parameters and topology, then uses the PSO algorithm to iteratively search for solutions, integrates the solutions into the initial pheromone distribution of the ACO algorithm, and finally combined with the global network state, the optimal solution is obtained through the improved ACO algorithm. © 2023 SPIE.

关键词： scheduling algorithms

Railway Signalling System using Encoder and Decoder 7

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Railway Signalling System using Encoder and Decoder

7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Ghute, Minal Barhate, Ajinkya Dhengle, Swejal Bakal, Yachana Kawale, Sharwari Rathod, Devichand Yeshwantrao Chavan College of Engineering Dept. of Electronics and Telecommunication Engineering Nagpur India Veermata Jijabai Technological Institute Dept. of Electronics Mumbai India

ISBN: (纸本)9798350397284

One of the way to think of railway signalling systems is as a collection of intricate systems that work together to control, supervise and safeguard railway operations. When there is a problem with the railway signalling system other safety measures are put in a place to keep the train running like slowing down, with the driver being responsible for keeping the train safe. In a nutshell, issues with the railway's capacity and safety result from malfunctions in the signalling system. A railway signalling system can be considered a group of complex systems that work together to provide control, supervision and protection of railway operations. The principles upon which railway signalling systems operate are extremely intricate. A railway scheduling algorithm can be used to optimize the train schedule, to minimize delays and traffic. The performance of the system is improved by optimizing the code running on the Arduino UNO. This minimizes memory usage, simplifying code and optimizing data processing methods. © 2023 IEEE.

关键词： scheduling algorithms

DocQueue: A scheduling Algorithm for Optimizing Healthcare Resources

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DocQueue: A Scheduling Algorithm for Optimizing Healthcare R...

IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Jeff Joji Sobin Johnson V. Angayarkanni P. Rama J. Kalaivani Department of Computing Technologies SRM Institute of Science & Technology Chennai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

DocQueue is a cloud-based system that optimizes patient scheduling in order to maximize the delivery of healthcare by prioritizing cases (dynamically) based on real-time information and availability of resources. It uses a decision-tree algorithm to analyze symptoms in real time, and then flags high-priority cases immediately, but uses a round-robin scheduling mechanism to manage low-priority cases so things are fair. And the AWS based architecture of the system allows for scalable deployment throughout the healthcare continuum. In simulations, these changes have shown to drastically cut down patient waiting time and make better use of resources, and therefore provide better patient care and more efficient operations. DocQueue is different from the usual scheduling systems because it is flexible and data-driven, it can change its schedule based on the real time needs of health care.

关键词： Computer science Schedules scheduling algorithms Heuristic algorithms Medical services Computer architecture Dynamic scheduling Real-time systems Resource management Optimization

Prediction-Assisted Online Distributed Deep Learning Workload scheduling in GPU Clusters

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arXiv 2025年

作者： Luo, Ziyue Liu, Jia Lee, Myungjin Shroff, Ness B. Dept. of ECE The Ohio State University United States Cisco Research United States

The recent explosive growth of deep learning (DL) models has necessitated a compelling need for efficient job scheduling for distributed deep learning training with mixed parallelisms (DDLwMP) in GPU clusters. This paper proposes an adaptive shortest-remaining-processing-time-first (A-SRPT) scheduling algorithm, a novel prediction-assisted online scheduling approach designed to mitigate the challenges associated with DL cluster scheduling. By modeling each job as a graph corresponding to heterogeneous Deep Neural Network (DNN) models and their associated distributed training configurations, A-SRPT strategically assigns jobs to the available GPUs, thereby minimizing inter-server communication overhead. Observing that most DDLwMP jobs recur, A-SRPT incorporates a random forest regression model to predict training iterations. Crucially, A-SRPT maps the complex scheduling problem into a single-machine instance, which is addressed optimally by a preemptive "shortest-remaining-processing-time-first" strategy. This optimized solution serves as a guide for actual job scheduling within the GPU clusters, leading to a theoretically provable competitive scheduling efficiency. We conduct extensive real-world testbed and simulation experiments to verify our proposed algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： scheduling algorithms

Efficient Multi-Processor scheduling in Increasingly Realistic Models

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arXiv 2024年

作者： Papp, Pál András Anegg, Georg Karanasiou, Aikaterini Yzelman, Albert-Jan N. Computing Systems Lab Huawei Zurich Research Center Zurich Switzerland

We study the problem of efficiently scheduling a computational DAG on multiple processors. The majority of previous works have developed and compared algorithms for this problem in relatively simple models;in contrast to this, we analyze this problem in a more realistic model that captures many real-world aspects, such as communication costs, synchronization costs, and the hierarchical structure of modern processing architectures. For this we extend the well-established BSP model of parallel computing with non-uniform memory access (NUMA) effects. We then develop a range of new scheduling algorithms to minimize the scheduling cost in this more complex setting: several initialization heuristics, a hill-climbing local search method, and several approaches that formulate (and solve) the scheduling problem as an Integer Linear Program (ILP). We combine these algorithms into a single framework, and conduct experiments on a diverse set of real-world computational DAGs to show that the resulting scheduler significantly outperforms both academic and practical baselines. In particular, even without NUMA effects, our scheduler finds solutions of 24% − 44% smaller cost on average than the baselines, and in case of NUMA effects, it achieves up to a factor 2.5× improvement compared to the baselines. Finally, we also develop a multilevel scheduling algorithm, which provides up to almost a factor 5× improvement in the special case when the problem is dominated by very high communication *** Codes 68W10, 68M20, 90C10 Copyright © 2024, The Authors. All rights reserved.

关键词： scheduling algorithms

scheduling Coflows for Minimizing the Maximum Completion Time in Heterogeneous Parallel Networks

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arXiv 2025年

作者： Chen, Chi-Yeh Department of Computer Science and Information Engineering National Cheng Kung University Taiwan

Coflow represents a network abstraction that models communication patterns within data centers. The scheduling of coflows is a prevalent issue in large data center environments and is classified as an NP-hard problem. This paper focuses on the scheduling of coflows in heterogeneous parallel networks, defined by architectures featuring multiple network cores operating concurrently. We introduce two pseudo-polynomial-time algorithms and two polynomial-time approximation algorithms to minimize the maximum completion time (makespan) in heterogeneous parallel networks. We propose a randomized algorithm that offers an expected approximation ratio of 1.5. Building upon this foundation, we provide a deterministic algorithm that utilizes derandomization techniques, which offers a performance guarantee of 1.5 + 2·LB1 , where LB is the lower bound of the makespan for each instance. To address time complexity concerns, we implement an exponential partitioning of time intervals and present a randomized algorithm with an expected approximation ratio of 1.5 + ϵ in polynomial time where ϵ > 0. Additionally, we develop a deterministic algorithm with a performance guarantee expressed as max {1.5 + ϵ, 1.5 + 2·LB1 } within polynomial time. These advancements markedly enhance the best-known approximation ratio of 2 + ϵ. Copyright © 2025, The Authors. All rights reserved.

关键词： scheduling algorithms