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Advanced Satellite Mobile Systems (ASMS)

作者： Leonardo Badia Andrea Munari Dept. of Information Engineering University of Padova Italy German Aerospace Center (DLR) Inst. of Communication and Navigation Wessling Germany

ISBN: (数字)9798331522353

ISBN: (纸本)9798331522384

Low Earth Orbit (LEO) satellite communications can provide real-time services with reduced latency, which makes them ideal for applications requiring timeliness, such as Internet connectivity, remote monitoring, and disaster response. In this paper, we study a problem of information freshness optimization for such a scenario. Usually, this is quantified through the age of information (AoI) metric, but the typical scenarios involve a homogeneous time horizon, where transmission can take place indifferently at any instant. In reality, LEO communication may be severely influenced by its intermittent connectivity, which is not present for long time windows where line of sight with the satellite system is absent. Thus, we consider a problem of AoI-minimal scheduling for a finite horizon, solved via dynamic programming, where intermittent connectivity is accounted for, and addressed through different approaches. Moreover, we quantitatively evaluate the impact of different parameters on AoI and derive practical considerations for empirical setups.

关键词： Satellites scheduling algorithms Space communications Low earth orbit satellites Optimal scheduling Signal processing Information age Dynamic programming Surges Satellite communications

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Deadline-Aware Task scheduling for Cloud-Fog Systems

Deadline-Aware Task Scheduling for Cloud-Fog Systems

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Gabriela Azucena Ayala Blanco Emiliano Nazareno Rojas Marcelli Alex Rubén Stockel Duarte Hsiang-Ting Huang Keng-Ying Li Tai-Lin Chin Universidad Politécnica Taiwán-Paraguay Paraguay Dept. CSIE National Taiwan University of Science and Technology Taiwan

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

The integration of fog computing with cloud service reduces transmission times to ensure that Internet of Things (IoT) devices satisfy deadline-aware tasks. Existing researches focus on the effective reduction of task transmission delays in hierarchical fog-cloud networks. However, an improper task execution order increases the waiting time and the risk of missing deadlines. To address this problem, the concept of Deadline-Aware Task Offloading and scheduling (DTOS) is introduced. To balance the load of all fog nodes, this research proposes an offloading strategy using a Genetic Algorithm that encodes multiple chromosomes with different offloading decisions for each task. Each evolution round refines the offloading positions, generating a strategy that approaches the optimal solution. Subsequently, the scheduling algorithm finds an effective execution sequence for each fog node based on the generated offloading decision. The simulation results demonstrate that the proposed strategy is more efficient than the baseline algorithms compared in this research.

关键词： Cloud computing Technological innovation scheduling algorithms Simulation scheduling Delays Internet of Things Biological cells Genetic algorithms Edge computing

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DyHSARW: A Dynamic GTS scheduling Mechanism for Large IEEE 802.15.4 DSME-Based IoT Networks

DyHSARW: A Dynamic GTS Scheduling Mechanism for Large IEEE 8...

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IEEE Conference on Wireless Communications and Networking

作者： Kona Sreekar Reddy Nikumani Choudhury Anakhi Hazarika Tamoghna Ojha Birla Institute of Technology & Science Pilani Hyderabad India Indian Institute of Technology Dhanbad India

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The IEEE 802.15.4 standard is one of the widely adopted networking specifications for realizing different applications of the Internet of Things (IoT), One of its Medium Access Control (MAC) protocols, the Deterministic Synchronous Multi-channel Extension (DSME), enhances stringent QoS by allocating DSME-Guaranteed Time Slots (GTSs) between pairs of devices. However, the standard does not specify a mechanism for scheduling these DSME-GTSs, presenting numerous research opportunities in this area. In this paper, we propose a novel Dynamic Hierarchical Slot-Channel Allocation with Recursive Weighting (DyHSARW) scheme aimed at improving the scheduling of DSME-GTS in large-scale IEEE 802.15.4-based IoT networks. The proposed approach dynamically allocates non-overlapping time slots (using the HCF technique) across multiple channels (based on a device's association order), optimizing resource utilization while adapting to the hierarchical structure of the network. Specifically, the HCF condition checks if the transmission weights of a child-parent pair and the previous time slot's value are compatible, i.e., if their HCF is equal to 1. This condition indicates that the parameters are co-prime, which minimizes the likelihood of collision in the time slot allocation process. This method seeks to overcome the limitations of existing GTS scheduling algorithms, which face challenges in efficient resource allocation, increased latency, and higher energy consumption under dynamic traffic conditions.

关键词： IEEE 802.15 Standard Job shop scheduling scheduling algorithms Heuristic algorithms Media Access Control Quality of service Dynamic scheduling Resource management Standards Industrial Internet of Things

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Improving DBMS scheduling Decisions with Fine-grained Performance Prediction on Concurrent Queries – Extended

arXiv

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arXiv 2025年

作者： Wu, Ziniu Markakis, Markos Liu, Chunwei Chen, Peter Baile Narayanaswamy, Balakrishnan Kraska, Tim Madden, Samuel MIT CSAIL CambridgeMA United States Amazon Web Services Santa ClaraCA United States Amazon Web Services MIT CSAIL CambridgeMA United States

Query scheduling is a critical task that directly impacts query performance in database management systems (DBMS). Deeply integrated schedulers, which require changes to DBMS internals, are usually customized for a specific engine and can take months to implement. In contrast, non-intrusive schedulers make coarse-grained decisions, such as controlling query admission and re-ordering query execution, without requiring modifications to DBMS internals. They require much less engineering effort and can be applied across a wide range of DBMS engines, offering immediate benefits to end users. However, most existing non-intrusive scheduling systems rely on simplified cost models and heuristics that cannot accurately model query interactions under concurrency and different system states, possibly leading to suboptimal scheduling decisions. This work introduces IconqSched, a new, principled non-intrusive scheduler that optimizes the execution order and timing of queries to enhance total end-to-end runtime as experienced by the user — query queuing time plus system runtime. Unlike previous approaches, IconqSched features a novel fine-grained predictor, Iconq, which treats the DBMS as a black box and accurately estimates the system runtime of concurrently executed queries under different system states. Using these predictions, IconqSched is able to capture system runtime variations across different query mixes and system loads. It then employs a greedy scheduling algorithm to effectively determine which queries to submit and when to submit them. We compare IconqSched to other schedulers in terms of end-to-end runtime using real workload traces. On Postgres, IconqSched reduces end-to-end runtime by 16.2%-28.2% on average and 33.6%-38.9% in the tail. Similarly, on Redshift, it reduces end-to-end runtime by 10.3%-14.1% on average and 14.9%-22.2% in the tail. © 2025, CC BY.

关键词： scheduling algorithms

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GREEN: Carbon-efficient Resource scheduling for Machine Learning Clusters 22

GREEN: Carbon-efficient Resource Scheduling for Machine Lear...

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22nd USENIX Symposium on Networked Systems Design and Implementation, NSDI 2025

作者： Xu, Kaiqiang Sun, Decang Tian, Han Zhang, Junxue Chen, Kai iSING Lab Hong Kong University of Science and Technology Hong Kong USTC China

ISBN: (纸本)9781939133465

This paper explores the problem of scheduling machine Learning (ML) jobs while also taking into account the reduction of carbon emissions in the cluster. Traditional cluster schedulers for ML jobs mainly focus on optimizing job completion time (JCT), but do not consider the environmental impact of their decisions, resulting in a suboptimal carbon footprint. To address this issue, we propose GREEN, an ML cluster scheduler that is both time-efficient and carbon-efficient. At its core, GREEN uses a unique carbon-aware scheduling algorithm that reduces carbon footprint with minimized impact on JCT. Additionally, it leverages the temporal flexibility of ML jobs to reduce carbon emissions by shifting workloads to less carbon-intensive times, while still maintaining overall daily capacity. Our experiments using real ML jobs workload demonstrate that GREEN can achieve up to 41.2% reduction in cluster-wide carbon footprint and 12% reduction in peak power consumption, while incurring 3.6%-5.9% time efficiency tradeoff compared to existing methods. © 2025 by The USENIX Association All Rights Reserved.

关键词： scheduling algorithms

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Work in Progress: Security-Aware Preemptive scheduling with Partial Trust for Safety-Critical Embedded Systems

Work in Progress: Security-Aware Preemptive Scheduling with ...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Fatima Raadia Nathan Fisher Wayne State University

ISBN: (数字)9798331543402

ISBN: (纸本)9798331543419

Safety-critical embedded systems often have very limited computational capabilities that must be carefully managed to provide the required functionalities. Since such systems are increasingly becoming a target of attacks, there is a need for real-time resource-allocation techniques that are resistant to such attacks. We extend the widely used sporadic task model to additionally enable the representation of certain kinds of security requirements, develop a corresponding scheduling algorithm and associated schedulability test, and experimentally evaluate the effectiveness of our proposed algorithm and test.

关键词： Resistance Embedded systems scheduling algorithms Computational modeling Real-time systems Security

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Extended Version: Non-Preemptive scheduling of Flexible Loads in Smart Grids via Convex Optimization

arXiv

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arXiv 2025年

作者： Davoudi, Mehdi Chen, Mingyu Qin, Junjie Elmore School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Department of Electrical and Computer Engineering Boston University United States

This paper studies the scheduling of a large population of non-preemptive flexible electric loads, each of which has a flexible starting time but once started will follow a fixed load shape until completion. We first formulate the scheduling problem as a mixed-integer convex program (MICP), then propose an efficient polynomial time relaxation-adjustment-rounding algorithm for solving the problem. The key novelty of the proposed method lies in its adjustment step, which uses a graph-based algorithm to navigate within the set of optimal points of the convex relaxation while reducing the number of fractional entries in the solution. We establish mathematically that our algorithm yields solutions that are near optimal for a finite number of loads and with its sub-optimality independent of the number of loads. Consequently, the proposed method is asymptotically optimal in a per-load cost sense when the number of loads increases. Despite the gap between the MICP and its convex relaxation, we establish that the solution of the proposed algorithm can be decentralized by marginal prices of the convex relaxation. We also develop and analyze variants of the proposed algorithm for settings with uncertainty and with time-varying realistic load shapes. Finally, we numerically evaluate the proposed algorithm in a case study for the non-preemptive scheduling of electric vehicles charging loads. © 2025, CC BY-NC-ND.

关键词： scheduling algorithms

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Optimizing Resource Allocation and scheduling towards FRMCS and GSM-R networks coexistence in Railway Systems

Optimizing Resource Allocation and Scheduling towards FRMCS ...

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International Global Information Infrastructure Symposium (GIIS)

作者： Mohamed Aziz Aboud Nawel Zangar Rami Langar Marion Berbineau Jerome Madec Software and IT Engineering Department Ecole de Technologie Supérieure (ÉTS) Montréal QC Canada LIGM-CNRS UMR 8049 University Gustave Eiffel Marne–la-Vallée France SNCF-Réseaux Direction Télécom Unifiée La Plaine-St-Denis France

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

The actual railway communication system used in Europe for high-speed trains (HST) is called the GSM-R system, which is a communication system based on 2G infrastructure. This system is meant to be replaced by a new system based on 5G NR infrastructure called the Future Railway Mobile Communication System (FRMCS) by 2030. For the next years, both systems will probably coexist in the same frequency band since the migration from GSM-R to FRMCS is planned to be done progressively until the GSM-R system is completely shut down, mainly due to safety and budget constraints. In this paper, we study the resource allocation for the FRMCS system sharing the same frequency band as the already deployed GSM-R system. We formulate the resource allocation problem as an integer linear problem (ILP), known to be NP-hard. To solve it in a reasonable time, we propose a scheduling algorithm, called Intelligent Traffic scheduling Preemptor (ITSP), that allocates resources for the different FRMCS traffic types considered (critical traffic and performance traffic) in the same frequency band with the GSM-R system. Our algorithm is channel quality Indicator (CQI) aware and uses the preemption mechanism in 5G NR standards to optimize the resource allocation for the FRMCS system without impacting the actual GSM-R resource allocation in the context of the white space concept.

关键词： Time-frequency analysis 5G mobile communication scheduling algorithms Europe White spaces GSM-R scheduling Safety Resource management Standards

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Task scheduling Algorithm for Maximizing the Benefits of Passenger-Carrying Robots Based on ED-ACO

Task Scheduling Algorithm for Maximizing the Benefits of Pas...

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

作者： Qingji Gao Haoming Li Jiayuan Hu Research Institute of Robotics Civil Aviation University of China Toray Tianjin China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

To address the problem of passenger-carrying services provided by robots in large public scenarios, this paper studies an Elite Dual-Adaptive Ant Colony Optimization (ED-ACO) algorithm for multitask scheduling to maximize benefits. The core idea of this algorithm is based on the ant colony optimization method, enhanced with elite ant sorting and pheromone adjustment mechanisms. It effectively optimizes the selection of task sequences and path planning for robots in dynamic environments. By integrating a dual-adaptive pheromone update matrix, the algorithm dynamically adjusts pheromone concentrations to guide task scheduling and path selection toward global optimization. Considering multi-objective optimization weights, a scheduling model is constructed to maximize benefits based on time, distance, and energy consumption. Simulation results demonstrate that the proposed algorithm features rapid convergence and outperforms traditional scheduling algorithms in multitask scheduling within airport terminals. It significantly enhances task completion efficiency and overall benefits, offering new insights and practical references for optimizing the scheduling of autonomous mobile robots in large-scale public spaces.

关键词： Ant colony optimization scheduling algorithms Robust stability Heuristic algorithms Robot kinematics Simulation scheduling Optimization Convergence Sorting

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LLM Query scheduling with Prefix Reuse and Latency Constraints

arXiv

引用

arXiv 2025年

作者： Dexter, Gregory Tang, Shao Baarzi, Ata Fatahi Song, Qingquan Dharamsi, Tejas Gupta, Aman LinkedIn Corporation United States

The efficient deployment of large language models (LLMs) in online settings requires optimizing inference performance under stringent latency constraints, particularly the time-to-first-token (TTFT) and time-per-output-token (TPOT). This paper focuses on the query scheduling problem for LLM inference with prefix reuse, a technique that leverages shared prefixes across queries to reduce computational overhead. Our work reveals previously unknown limitations of the existing first-come-first-serve (FCFS) and longest-prefix-match (LPM) scheduling strategies with respect to satisfying latency constraints. We present a formal theoretical framework for LLM query scheduling under RadixAttention, a prefix reuse mechanism that stores and reuses intermediate representations in a radix tree structure. Our analysis establishes the NP-hardness of the scheduling problem with prefix reuse under TTFT constraints and proposes a novel scheduling algorithm, k-LPM, which generalizes existing methods by balancing prefix reuse and fairness in query processing. Theoretical guarantees demonstrate that k-LPM achieves improved TTFT performance under realistic traffic patterns captured by a data generative model. Empirical evaluations in a realistic serving setting validates our findings, showing significant reductions in P99 TTFT compared to baseline methods. Copyright © 2025, The Authors. All rights reserved.

关键词： scheduling algorithms

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