Tootfinder

Collaborative Charging Scheduling via Balanced Bounding Box Methods
Fangting Zhou, Bal\'azs Kulcs\'ar, Jiaming Wu
https://arxiv.org/abs/2506.14461 …

Collaborative Charging Scheduling via Balanced Bounding Box Methods
Electric mobility faces several challenges, most notably the high cost of infrastructure development and the underutilization of charging stations. The concept of shared charging offers a promising solution. The paper explores sustainable urban logistics through horizontal collaboration between two fleet operators and addresses a scheduling problem for the shared use of charging stations. To tackle this, the study formulates a collaborative scheduling problem as a bi-objective nonlinear integer…

Asymptotically Smaller Encodings for Graph Problems and Scheduling
Bernardo Subercaseaux
https://arxiv.org/abs/2506.14042 https://arx…

Asymptotically Smaller Encodings for Graph Problems and Scheduling
We show how several graph problems (e.g., vertex-cover, independent-set, $k$-coloring) can be encoded into CNF using only $O(|V|^2 / \lg |V|)$ many clauses, as opposed to the $Ω(|V|^2)$ constraints used by standard encodings. This somewhat surprising result is a simple consequence of a result of Erdős, Chung, and Spencer (1983) about biclique coverings of graphs, and opens theoretical avenues to understand the success of "Bounded Variable Addition'' (Manthey, Heu…

Considering the multi-time scale rolling optimization scheduling method of micro-energy network connected to electric vehicles
Hengyu Liu, Yanhong Luo, Congcong Wu, Yin Guan, Ahmed Lotfy Elrefai, Andreas Elombo, Si Li, Sahban Wael Saeed Alnaser, Mingyu Yan
https://arxiv.org/abs/2506.14112

Considering the multi-time scale rolling optimization scheduling method of micro-energy network connected to electric vehicles
The large-scale access of electric vehicles to the power grid not only provides flexible adjustment resources for the power system, but the temporal uncertainty and distribution complexity of their energy interaction pose significant challenges to the economy and robustness of the micro-energy network. In this paper, we propose a multi-time scale rolling optimization scheduling method for micro-energy networks considering the access of electric vehicles. In order to solve the problem of evaluat…

QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs
Ashutosh Shankar (Indian Institute Of Technology Kharagpur), Astha Kumari (Indian Institute Of Technology Kharagpur)
https://arxiv.org/abs/2506.12415

QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs
In latency-sensitive applications, efficient task scheduling is crucial for maintaining Quality of Service (QoS) while meeting strict timing constraints. This paper addresses the challenge of scheduling periodic tasks structured as directed acyclic graphs (DAGs) within heterogeneous, pre-occupied Mobile Edge Computing (MEC) networks. We propose a modified version of the Heterogeneous Earliest Finish Time (HEFT) algorithm designed to exploit residual processing capacity in preoccupied MEC enviro…

Dynamic Layered Decoding Scheduling for LDPC Codes Aided by Check Node Error Probabilities
Chenyuan Jia, Dongxu Chang, Ruiyuan Wang, Guanghui Wang, Guiying Yan, Cunquan Qu
https://arxiv.org/abs/2506.13507

Dynamic Layered Decoding Scheduling for LDPC Codes Aided by Check Node Error Probabilities
In this study, a new scheduling strategies for low-density parity-check (LDPC) codes under layered belief propagation (LBP) is designed. Based on the criteria of prioritizing the update of check nodes with lower error probabilities, we propose two dynamic scheduling methods: dynamic error belief propagation (Dyn-EBP) and dynamic penalty error belief propagation (Dyn-PEBP). In Dyn-EBP, each check node is restricted from being updated the same number of times, whereas Dyn-PEBP removes this restri…

Dynamic Scheduling for Enhanced Performance in RIS-assisted Cooperative Network with Interference
Yomali Lokugama, Saman Atapattu, Nathan Ross, Sithamparanathan Kandeepan, Chintha Tellambura
https://arxiv.org/abs/2506.12778

Dynamic Scheduling for Enhanced Performance in RIS-assisted Cooperative Network with Interference
Reconfigurable Intelligent Surfaces (RIS) have emerged as transformative technologies, enhancing spectral efficiency and improving interference management in multi-user cooperative communications. This paper investigates the integration of RIS with Flexible-Duplex (FlexD) communication, featuring dynamic scheduling capabilities, to mitigate unintended external interference in multi-user wireless networks. By leveraging the reconfigurability of RIS and dynamic scheduling, we propose a user-pair …

CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC
Eman Alqudah, Ashfaq Khokhar
https://arxiv.org/abs/2506.14987 https://

CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC
Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a CNN-based dynamic priority prediction mechanism for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach uses a Convolutional Neural Network and graph coloring to adaptively assign link priorities based on r…

Less Conservative Adaptive Gain-scheduling Control for Continuous-time Systems with Polytopic Uncertainties
Ariany C. Oliveira, Victor C. S. Campos, Leonardo. A. Mozelli
https://arxiv.org/abs/2506.12476

Less Conservative Adaptive Gain-scheduling Control for Continuous-time Systems with Polytopic Uncertainties
The synthesis of adaptive gain-scheduling controller is discussed for continuous-time linear models characterized by polytopic uncertainties. The proposed approach computes the control law assuming the parameters as uncertain and adaptively provides an estimate for the gain-scheduling implementation. Conservativeness is reduced using our recent results on describing uncertainty: i) a structural relaxation that casts the parameters as outer terms and introduces slack variables; and ii) a precise…

Adaptive determinantal scheduling with fairness in wireless networks
H. P. Keeler, B. B{\l}aszczyszyn
https://arxiv.org/abs/2506.11738 https://

Adaptive determinantal scheduling with fairness in wireless networks
We propose a novel framework for wireless network scheduling with fairness using determinantal (point) processes. Our approach incorporates the repulsive nature of determinantal processes, generalizing traditional Aloha protocols that schedule transmissions independently. We formulate the scheduling problem with an utility function representing fairness. We then recast this formulation as a convex optimization problem over a certain class of determinantal point processes called $L$-ensembles, w…

Work-in-Progress: Multi-Deadline DAG Scheduling Model for Autonomous Driving Systems
Atsushi Yano, Takuya Azumi
https://arxiv.org/abs/2505.06780 https://…

Work-in-Progress: Multi-Deadline DAG Scheduling Model for Autonomous Driving Systems
Autoware is an autonomous driving system implemented on Robot Operation System (ROS) 2, where an end-to-end timing guarantee is crucial to ensure safety. However, existing ROS 2 cause-effect chain models for analyzing end-to-end latency struggle to accurately represent the complexities of Autoware, particularly regarding sync callbacks, queue consumption patterns, and feedback loops. To address these problems, we propose a new scheduling model that decomposes the end-to-end timing constraints o…

AstroQ: Automated Scheduling of Cadenced Astronomical Observations
Jack Lubin, Erik A. Petigura, Velibor V. Mi\v{s}i\'c, Judah Van Zandt, Luke B. Handley
https://arxiv.org/abs/2506.08195

AstroQ: Automated Scheduling of Cadenced Astronomical Observations
Astronomy relies heavily on time domain observations. To maximize the scientific yield of such observations, astronomers must carefully match the observational cadence to the phenomena of interest. This presents significant scheduling challenges for observatories with multiple large programs, each with different cadence needs. To address this challenge, we developed AstroQ, an automated framework for scheduling cadenced observations. We tested this on a suite of Doppler exoplanet programs at Ke…

Green Scheduling with Time-of-Use Tariffs and Machine States: Optimizing Energy Cost via Branch-and-Bound and Bin Packing Strategies
Ond\v{r}ej Benedikt, Istv\'an M\'odos, Antonin Novak, Zden\v{e}k Hanz\'alek
https://arxiv.org/abs/2506.10405

Green Scheduling with Time-of-Use Tariffs and Machine States: Optimizing Energy Cost via Branch-and-Bound and Bin Packing Strategies
This paper presents a branch-and-bound algorithm, enhanced with bin packing strategies, for scheduling under variable energy pricing and power-saving states. The proposed algorithm addresses the 1,TOU|states|TEC problem, which involves scheduling jobs to minimize total energy cost (TEC) while considering time-of-use (TOU) electricity prices and different machine states (e.g., processing, idle, off). Key innovations include instance pre-processing for rapid lower bound calculations, a …

My new strategy when people try to force me to fill out a #Google form or use Google scheduling or #Meet is I'll tell them I keep getting an error message, even if I'm not.

Scheduling Agile Earth Observation Satellites with Onboard Processing and Real-Time Monitoring
Antonio M. Mercado-Mart\'inez, Beatriz Soret, Antonio Jurado-Navas
https://arxiv.org/abs/2506.11556

Scheduling Agile Earth Observation Satellites with Onboard Processing and Real-Time Monitoring
The emergence of Agile Earth Observation Satellites (AEOSs) has marked a significant turning point in the field of Earth Observation (EO), offering enhanced flexibility in data acquisition. Concurrently, advancements in onboard satellite computing and communication technologies have greatly enhanced data compression efficiency, reducing network latency and congestion while supporting near real-time information delivery. In this paper, we address the Agile Earth Observation Satellite Scheduling …

Replaced article(s) found for cs.DM. https://arxiv.org/list/cs.DM/new
[1/1]:
The Days On Days Off Scheduling Problem
https://ar…

Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming
Pouya Agheli, Tugce Kobal, Fran\c{c}ois Durand, Matthew Andrews
https://arxiv.org/abs/2506.08263

Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming
We investigate the multiuser scheduling problem in multiple-input multiple-output (MIMO) systems using orthogonal frequency division multiplexing (OFDM) and hybrid beamforming in which a base station (BS) communicates with multiple users over millimeter wave (mmWave) channels in the downlink. Improved scheduling is critical for enhancing spectral efficiency and the long-term performance of the system from the perspective of proportional fairness (PF) metric in hybrid beamforming systems due to …

Efficient Task Graph Scheduling for Parallel QR Factorization in SLSQP
Soumyajit Chatterjee, Rahul Utkoor, Uppu Eshwar, Sathya Peri, V. Krishna Nandivada
https://arxiv.org/abs/2506.09463

Efficient Task Graph Scheduling for Parallel QR Factorization in SLSQP
Efficient task scheduling is paramount in parallel programming on multi-core architectures, where tasks are fundamental computational units. QR factorization is a critical sub-routine in Sequential Least Squares Quadratic Programming (SLSQP) for solving non-linear programming (NLP) problems. QR factorization decomposes a matrix into an orthogonal matrix Q and an upper triangular matrix R, which are essential for solving systems of linear equations arising from optimization problems. SLSQP uses …

Directed Acyclic Graph Convolutional Networks
Samuel Rey, Hamed Ajorlou, Gonzalo Mateos
https://arxiv.org/abs/2506.12218 https://arxi…

Directed Acyclic Graph Convolutional Networks
Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and neural architecture search. In this work, we introduce the DAG Convolutional Network (DCN), a novel graph neural network (GNN) architecture designed specifically for convolutional learning from signals supported on DAGs. The DCN leverages causal graph filters to learn nodal representations that account for the partial ordering inherent to DAGs, a strong inductive bias d…

Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology
Akira SaiToh, Arezoo Modiri, Amit Sawant, Robabeh Rahimi
https://arxiv.org/abs/2506.04328

Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology
Among the genetic algorithms generally used for optimization problems in the recent decades, quantum-inspired variants are known for fast and high-fitness convergence and small resource requirement. Here the application to the patient scheduling problem in proton therapy is reported. Quantum chromosomes are tailored to possess the superposed data of patient IDs and gantry statuses. Selection and repair strategies are also elaborated for reliable convergence to a clinically feasible schedule alt…

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Rajpreet Singh, Novak Bo\v{s}kov, Aditya Gudal, Manzoor A. Khan
https://arxiv.org/abs/2506.01571

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Resource allocation and scheduling are a common problem in various distributed systems. Although widely studied, the state-of-the-art solutions either do not scale or lack the expressive power to capture the most complex instances of the problem. To that end, we present a mathematical framework for hypergraph ranking and analysis, unifying graph theory, lattice theory, and semantic analysis. In our fundamental theorem, we prove the existence of partial order on entities of hypergraphs, extendin…

This https://arxiv.org/abs/2505.06780 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csOS_…

Work-in-Progress: Multi-Deadline DAG Scheduling Model for Autonomous Driving Systems
Autoware is an autonomous driving system implemented on Robot Operation System (ROS) 2, where an end-to-end timing guarantee is crucial to ensure safety. However, existing ROS 2 cause-effect chain models for analyzing end-to-end latency struggle to accurately represent the complexities of Autoware, particularly regarding sync callbacks, queue consumption patterns, and feedback loops. To address these problems, we propose a new scheduling model that decomposes the end-to-end timing constraints o…

This https://arxiv.org/abs/2505.10328 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

A Comparative Study of SMT and MILP for the Nurse Rostering Problem
The effects of personnel scheduling on the quality of care and working conditions for healthcare personnel have been thoroughly documented. However, the ever-present demand and large variation of constraints make healthcare scheduling particularly challenging. This problem has been studied for decades, with limited research aimed at applying Satisfiability Modulo Theories (SMT). SMT has gained momentum within the formal verification community in the last decades, leading to the advancement of S…

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Chuanchao Gao, Niraj Kumar, Arvind Easwaran
https://arxiv.org/abs/2506.12686

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies.…

The Secure Overview and Analysis OF 3GPP MAC CE
Jin Cao, Yuanyuan Yang, Ruhui Ma, Sheng Li, Hui Li
https://arxiv.org/abs/2506.09502 https://

The Secure Overview and Analysis OF 3GPP MAC CE
To more effectively control and allocate network resources, MAC CE has been introduced into the network protocol, which is a type of control signaling located in the MAC layer. Since MAC CE lacks encryption and integrity protection mechanisms provided by PDCP, the control signaling carried by MAC CE is vulnerable to interception or tampering by attackers during resource scheduling and allocation. Currently, the 3GPP has analyzed the security risks of Layer 1/Layer 2 Triggered Mobility (LTM), wh…

A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation
Sebasti\'an Rojas-Innocenti, Enrique Baeyens, Alejandro Mart\'in-Crespo, Sergio Saludes-Rodil, Fernando Frechoso Escudero
https://arxiv.org/abs/2506.10824

A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation
This paper presents a scenario based robust optimization framework for short term energy scheduling in electricity intensive industrial plants, explicitly addressing uncertainty in planning decisions. The model is formulated as a two-stage Mixed Integer Linear Program (MILP) and integrates a hybrid scenario generation method capable of representing uncertain inputs such as electricity prices, renewable generation, and internal demand. A convex objective function combining expected and worst cas…

Energy-Efficient QoS-Aware Scheduling for S-NUCA Many-Cores
Sudam M. Wasala, Jurre Wolff, Yixian Shen, Anuj Pathania, Clemens Grelck, Andy D. Pimentel
https://arxiv.org/abs/2505.23351

Energy-Efficient QoS-Aware Scheduling for S-NUCA Many-Cores
Optimizing performance and energy efficiency in many-core processors, especially within Non-Uniform Cache Access (NUCA) architectures, remains a critical challenge. The performance heterogeneity inherent in S-NUCA systems complicates task scheduling due to varying cache access latencies across cores. This paper introduces a novel QoS management policy to maintain application execution within predefined Quality of Service (QoS) targets, measured using the Application Heartbeats framework. QoS me…

Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response
Lei Han, Yitong Guo, Pengfei Yang, Zhiyong Yu, Liang Wang, Quan Wang, Zhiwen Yu
https://arxiv.org/abs/2506.04276

Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response
Natural disasters have caused significant losses to human society, and the timely and efficient acquisition of post-disaster environmental information is crucial for the effective implementation of rescue operations. Due to the complexity of post-disaster environments, existing sensing technologies face challenges such as weak environmental adaptability, insufficient specialized sensing capabilities, and limited practicality of sensing solutions. This paper explores the heterogeneous multi-agen…

Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem
Yang Lv, Rong Hu, Bin Qian, Jian-Bo Yang
https://arxiv.org/abs/2506.08608

Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem
The steelmaking continuous casting scheduling problem (SCCSP) is a critical and complex challenge in modern steel production, requiring the coordinated assignment and sequencing of steel charges across multiple production stages. Efficient scheduling not only enhances productivity but also significantly reduces energy consumption. However, both traditional heuristics (e.g., two-stage local search) and recent metaheuristics often struggle to adapt to the dynamic characteristics of practical SCCS…

This https://arxiv.org/abs/2206.10164 has been replaced.
link: https://scholar.google.com/scholar?q=a

Efficient scheduling in redundancy systems with general service times
We characterize the impact of scheduling policies on the mean response time in nested systems with cancel-on-complete redundancy. We consider not only redundancy-oblivious policies, such as FCFS and ROS, but also redundancy-aware policies of the form $Π$ 1 -- $Π$ 2 , where $Π$ 1 discriminates among job classes (e.g., least-redundant-first (LRF), most-redundantfirst (MRF)) and $Π$ 2 discriminates among jobs of the same class. Assuming that jobs have independent and identically distributed (i…

Students around the world who were gearing up to study in the United States this fall face growing uncertainty
after the Trump administration temporarily halted student visa appointments this week.
On Tuesday, a state department directive ordered US embassies globally to immediately stop scheduling visa interviews for foreign students
while it prepares to implement expanded social media screening for all international visa applicants.
While interview appointments that …

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning
Waylon Luo (Kent State University), Jiapeng Zhao (Cisco), Tong Zhan (Meta), Qiang Guan (Kent State University)
https://arxiv.org/abs/2506.10889

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning
Present-day quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high susceptibility to errors-all of which obstruct the execution of large and complex circuits. The advancement of quantum algorithms has outpaced the capabilities of existing quantum hardware, making it difficult to scale computations effectively. Additionally, inconsistencies in hardware performance and pervasive quantum noise undermine system stability and computational accu…

Not all those who drift are lost: Drift correction and calibration scheduling for the IoT
Aaron Hurst, Andrey V. Kalinichev, Klaus Koren, Daniel E. Lucani
https://arxiv.org/abs/2506.09186

Not all those who drift are lost: Drift correction and calibration scheduling for the IoT
Sensors provide a vital source of data that link digital systems with the physical world. However, as sensors age, the relationship between what they measure and what they output changes. This is known as sensor drift and poses a significant challenge that, combined with limited opportunity for re-calibration, can severely limit data quality over time. Previous approaches to drift correction typically require large volumes of ground truth data and do not consider measurement or prediction uncer…

ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
Yuki Imajuku, Kohki Horie, Yoichi Iwata, Kensho Aoki, Naohiro Takahashi, Takuya Akiba
https://arxiv.org/abs/2506.09050

ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
How well do AI systems perform in algorithm engineering for hard optimization problems in domains such as package-delivery routing, crew scheduling, factory production planning, and power-grid balancing? We introduce ALE-Bench, a new benchmark for evaluating AI systems on score-based algorithmic programming contests. Drawing on real tasks from the AtCoder Heuristic Contests, ALE-Bench presents optimization problems that are computationally hard and admit no known exact solution. Unlike short-du…

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
Chi Liu, Zhezhuang Xu, Jiawei Zhou, Yazhou Yuan, Kai Ma, Meng Yuan
https://arxiv.org/abs/2506.06824

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
Green buildings (GBs) with renewable energy and building energy management systems (BEMS) enable efficient energy use and support sustainable development. Electric vehicles (EVs), as flexible storage resources, enhance system flexibility when integrated with stationary energy storage systems (ESS) for real-time scheduling. However, differing degradation and operational characteristics of ESS and EVs complicate scheduling strategies. This paper proposes a model-free deep reinforcement learning (…

This https://arxiv.org/abs/2505.21323 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

A first look at ROS~2 applications written in asynchronous Rust
The increasing popularity of the Rust programming language in building robotic applications using the Robot Operating System (ROS~2) raises questions about its real-time execution capabilities, particularly when employing asynchronous programming. Existing real-time scheduling and response-time analysis techniques for ROS~2 focus on applications written in C++ and do not address the unique execution models and challenges presented by Rust's asynchronous programming paradigm. In this paper, we a…

This https://arxiv.org/abs/2505.22124 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Nurse Staffing and Scheduling Problem with Bounded Flexibility and Demand Uncertainty
Nurse staffing and scheduling are persistent challenges in healthcare due to demand fluctuations and individual nurse preferences. This study introduces the concept of bounded flexibility, balancing nurse satisfaction with strict rostering rules, particularly a real-world time regularity policy from a major hospital in Singapore. We model the problem as a multi-stage stochastic program to address evolving demand, optimizing both aggregate staffing and detailed scheduling decisions. A reformulat…

Carbon-Aware Temporal Data Transfer Scheduling Across Cloud Datacenters
Elvis Rodrigues, Jacob Goldverg, Tevfik Kosar
https://arxiv.org/abs/2506.04117 http…

Carbon-Aware Temporal Data Transfer Scheduling Across Cloud Datacenters
Inter-datacenter communication is a significant part of cloud operations and produces a substantial amount of carbon emissions for cloud data centers, where the environmental impact has already been a pressing issue. In this paper, we present a novel carbon-aware temporal data transfer scheduling framework, called LinTS, which promises to significantly reduce the carbon emission of data transfers between cloud data centers. LinTS produces a competitive transfer schedule and makes scaling decisi…

This https://arxiv.org/abs/2504.00605 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

A batch production scheduling problem in a reconfigurable hybrid manufacturing-remanufacturing system
In recent years, remanufacturing of End-of-Life (EOL) products has been adopted by manufacturing sectors as a competent practice to enhance their sustainability and market share. Due to the mass customization of products and high volatility of market, processing of new products and remanufacturing of EOLs in the same shared facility, namely Hybrid Manufacturing-Remanufacturing System (HMRS), is a mean to keep such production efficient. Accordingly, customized production capabilities are require…

This https://arxiv.org/abs/2403.16971 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csOS_…

AIOS: LLM Agent Operating System
LLM-based intelligent agents face significant deployment challenges, particularly related to resource management. Allowing unrestricted access to LLM or tool resources can lead to inefficient or even potentially harmful resource allocation and utilization for agents. Furthermore, the absence of proper scheduling and resource management mechanisms in current agent designs hinders concurrent processing and limits overall system efficiency. As the diversity and complexity of agents continue to gro…

This https://arxiv.org/abs/2410.23302 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

A Flexible Job Shop Scheduling Problem Involving Reconfigurable Machine Tools Under Industry 5.0
The rise of Industry 5.0 has introduced new demands for manufacturing companies, requiring a shift in how production schedules are managed to address human centered, environmental, and economic goals comprehensively. The flexible job shop scheduling problem (FJSSP), which involves processing operations on various capable machines, accurately reflects the complexities of modern manufacturing settings. This paper investigates the FJSSP involving reconfigurable machine tools with configuration dep…

This https://arxiv.org/abs/2409.15104 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

PecSched: Preemptive and Efficient Cluster Scheduling for LLM Inference
The scaling of transformer-based Large Language Models (LLMs) has significantly expanded their context lengths, enabling applications where inputs exceed 100K tokens. Our analysis of a recent Azure LLM inference trace reveals a highly skewed long-tail distribution of input lengths, with approximately 80% of inputs shorter than 2K tokens. Long inputs constitute only a small fraction. Existing cluster-level LLM scheduling strategies, including First-In-First-Out (FIFO), reservation-based, and pri…

Optimal BESS Scheduling for Multi-Market Participation in the Nordics
Zeenat Hameed, Chresten Traeholt
https://arxiv.org/abs/2506.02837 https://

Optimal BESS Scheduling for Multi-Market Participation in the Nordics
Battery energy storage systems BESSs can provide fast frequency reserves and energy arbitrage in Nordic electricity markets but their limited energy capacity requires accurate revenue forecasts and coordinated bidding across multiple submarkets. This paper introduces a unified framework that employs generalized additive models GAMs to generate one week ahead forecasts of spot and FCRN revenues in Denmark, Finland, and Norway using three years of hourly price and volume data. Forecast outputs fe…

This https://arxiv.org/abs/2404.19431 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Toward Practical and Theoretical Integration of Sensing and Communication in URA Systems
This work addresses the problem of integrated sensing and communications (ISAC) involving a massive number of unsourced and uncoordinated users. In the proposed model, known as the unsourced ISAC system (UNISAC), all active communication and sensing users simultaneously share a short frame to transmit their signals without requiring scheduling by the base station or the need to announce their identities. Consequently, the received signal from each user is heavily affected by interference from n…

This https://arxiv.org/abs/2506.02576 has been replaced.
link: https://scholar.google.com/scholar?q=a

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively …

Distributedness based scheduling
Paritosh Ranjan, Surajit Majumder, Prodip Roy, Bhuban Padhan
https://arxiv.org/abs/2506.02581 https://

Distributedness based scheduling
Efficient utilization of computing resources in a Kubernetes cluster is often constrained by the uneven distribution of pods with similar usage patterns. This paper presents a novel scheduling strategy designed to optimize the distributedness of Kubernetes resources based on their usage magnitude and patterns across CPU, memory, network, and storage. By categorizing resource usage into labels such as "cpu high spike" or "memory medium always," and applying these to deployed pods, the system cal…

Backpressure-based Mean-field Type Game for Scheduling in Multi-Hop Wireless Sensor Networks
Salah Eddine Choutri, Boualem Djehiche, Prajwal Chauhan, Saif Eddin Jabari
https://arxiv.org/abs/2506.03059

Backpressure-based Mean-field Type Game for Scheduling in Multi-Hop Wireless Sensor Networks
We propose a Mean-Field Type Game (MFTG) framework for effective scheduling in multi-hop wireless sensor networks (WSNs) using backpressure as a performance criterion. Traditional backpressure algorithms leverage queue differentials to regulate data flow and maintain network stability. In this work, we extend the backpressure framework by incorporating a mean-field term into the cost functional, capturing the global behavior of the system alongside local dynamics. The resulting model utilizes t…

Hybrid Learning for Cold-Start-Aware Microservice Scheduling in Dynamic Edge Environments
Jingxi Lu, Wenhao Li, Jianxiong Guo, Xingjian Ding, Zhiqing Tang, Tian Wang, Weijia Jia
https://arxiv.org/abs/2505.22424

Hybrid Learning for Cold-Start-Aware Microservice Scheduling in Dynamic Edge Environments
With the rapid growth of IoT devices and their diverse workloads, container-based microservices deployed at edge nodes have become a lightweight and scalable solution. However, existing microservice scheduling algorithms often assume static resource availability, which is unrealistic when multiple containers are assigned to an edge node. Besides, containers suffer from cold-start inefficiencies during early-stage training in currently popular reinforcement learning (RL) algorithms. In this pape…

This https://arxiv.org/abs/2412.09840 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

LAVA: Lifetime-Aware VM Allocation with Learned Distributions and Adaptation to Mispredictions
Scheduling virtual machines (VMs) on hosts in cloud data centers dictates efficiency and is an NP-hard problem with incomplete information. Prior work improved VM scheduling with predicted VM lifetimes. Our work further improves lifetime-aware scheduling using repredictions with lifetime distributions versus one-shot prediction. Our approach repredicts and adjusts VM and host lifetimes when incorrect predictions emerge. We also present novel approaches for defragmentation and regular system mai…

This https://arxiv.org/abs/2410.23302 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

A Flexible Job Shop Scheduling Problem Involving Reconfigurable Machine Tools Under Industry 5.0
The rise of Industry 5.0 has introduced new demands for manufacturing companies, requiring a shift in how production schedules are managed to address human centered, environmental, and economic goals comprehensively. The flexible job shop scheduling problem (FJSSP), which involves processing operations on various capable machines, accurately reflects the complexities of modern manufacturing settings. This paper investigates the FJSSP involving reconfigurable machine tools with configuration dep…

Prediction of the Conditional Probability Densities of Time Interval Extrema for Risk-Sensitive Scheduling
Buyi Yu, Wenyuan Tang
https://arxiv.org/abs/2506.01358

Prediction of the Conditional Probability Densities of Time Interval Extrema for Risk-Sensitive Scheduling
Planning and scheduling activities in the electrical power system, such as the commitment of reserve generation, often involve statistical characterization of peak demand. Extreme Value Analysis (EVA)-based probabilistic assessments of annual peaks are widely adopted by energy regulatory and oversight agencies to determine the likelihood and severity of potential energy shortfalls. Due to the inability of classical EVA to account for peak distributions that change with annual extreme temperatur…

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
Prachi Jadhav, Hongwei Jin, Ewa Deelman, Prasanna Balaprakash
https://arxiv.org/abs/2506.02025

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
High-Performance Computing (HPC) job scheduling involves balancing conflicting objectives such as minimizing makespan, reducing wait times, optimizing resource use, and ensuring fairness. Traditional methods, including heuristic-based (e.g., First-Come-First-Served) or intensive optimization techniques, often lack adaptability to dynamic workloads and heterogeneous HPC systems. To address this, we propose a novel Large Language Model (LLM)-based scheduler using a ReAct-style framework (Reason +…

This https://arxiv.org/abs/2308.14124 has been replaced.
link: https://scholar.google.com/scholar?q=a

The APX-hardness of the Traveling Tournament Problem
The Traveling Tournament Problem (TTP-$k$) is a well-known benchmark problem in sports scheduling, which asks us to design a double round-robin schedule such that each pair of teams plays one game in each other's home venue, no pair of teams plays each other on two consecutive days, each team plays at most $k$ consecutive home games or away games, and the total traveling distance of all the $n$ teams is minimized. TTP-$k$ allows a polynomial-time approximation scheme when $k=2$ and becomes APX-…

This https://arxiv.org/abs/2502.17826 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Feedback-Free Resource Scheduling for Flexible Multi-BS Cooperation in FD-RAN
Flexible cooperation among base stations (BSs) is critical to improve resource utilization efficiency and meet personalized user demands. However, its practical implementation is hindered by the current radio access network (RAN), which relies on the coupling of uplink and downlink transmissions and channel state information feedback with inherent issues such as overheads and delays. To overcome these limitations, we consider the fully-decoupled RAN (FD-RAN), in which uplink and downlink functi…

Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS
Preethika Pradeep, Eyhab Al-Masri
https://arxiv.org/abs/2506.04902 https://

Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS
AIoT workloads demand energy-efficient orchestration across cloud-edge infrastructures, but Kubernetes' default scheduler lacks multi-criteria optimization for heterogeneous environments. This paper presents GreenPod, a TOPSIS-based scheduler optimizing pod placement based on execution time, energy consumption, processing core, memory availability, and resource balance. Tested on a heterogeneous Google Kubernetes cluster, GreenPod improves energy efficiency by up to 39.1% over the default Kuber…

Optimizing Connectivity and Scheduling of Near/Far Field Users in Massive MIMO NOMA System
Ziad Qais Al-Abbasi
https://arxiv.org/abs/2505.23259 https://

Optimizing Connectivity and Scheduling of Near/Far Field Users in Massive MIMO NOMA System
It is envisioned that the next generations of wireless communication environment will be characterized with dense traffic demand due to the prediction that there will be large numbers of active users. Hence, it is important to find a solution to deal with such dense numbers of users. This paper investigates optimizing the connectivity and users scheduling to improve the performance of near and far field users in a downlink, multiuser, massive MIMO-NOMA system. For the considered system model, c…

This https://arxiv.org/abs/2503.05408 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Efficient Parallel Scheduling for Sparse Triangular Solvers
We develop and analyze new scheduling algorithms for solving sparse triangular linear systems (SpTRSV) in parallel. Our approach produces highly efficient synchronous schedules for the forward- and backward-substitution algorithm. Compared to state-of-the-art baselines HDagg and SpMP, we achieve a $3.32 \times$ and $1.42 \times$ geometric-mean speed-up, respectively. We achieve this by obtaining an up to $12.07 \times$ geometric-mean reduction in the number of synchronization barriers over HDag…

Bilevel Optimization for Improved Flexibility Aggregation Models of Electric Vehicle Fleets
Philipp H\"artel, Michael von Bonin
https://arxiv.org/abs/2506.04843

Bilevel Optimization for Improved Flexibility Aggregation Models of Electric Vehicle Fleets
Electric vehicle (EV) fleets are expected to become an increasingly important source of flexibility for power system operations. However, accurately capturing the flexibility potential of numerous and heterogeneous EVs remains a significant challenge. We propose a bilevel optimization formulation to enhance flexibility aggregations of electric vehicle fleets. The outer level minimizes scheduling deviations between the aggregated and reference EV units, while the inner level maximizes the aggreg…

Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models
Hang Fan, Mingxuan Li, Jingshi Cui, Zuhan Zhang, Wencai Run, Dunnan Liu
https://arxiv.org/abs/2506.03728

Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models
The rapid growth of EVs and the subsequent increase in charging demand pose significant challenges for load grid scheduling and the operation of EV charging stations. Effectively harnessing the spatiotemporal correlations among EV charging stations to improve forecasting accuracy is complex. To tackle these challenges, we propose EV-LLM for EV charging loads based on LLMs in this paper. EV-LLM integrates the strengths of Graph Convolutional Networks (GCNs) in spatiotemporal feature extraction w…

Performance of Confidential Computing GPUs
Antonio Mart\'inez Ibarra, Julian James Stephen, Aurora Gonz\'alez Vidal, K. R. Jayaram, Antonio Fernando Skarmeta G\'omez
https://arxiv.org/abs/2505.16501

Performance of Confidential Computing GPUs
This work examines latency, throughput, and other metrics when performing inference on confidential GPUs. We explore different traffic patterns and scheduling strategies using a single Virtual Machine with one NVIDIA H100 GPU, to perform relaxed batch inferences on multiple Large Language Models (LLMs), operating under the constraint of swapping models in and out of memory, which necessitates efficient control. The experiments simulate diverse real-world scenarios by varying parameters such as …

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Xiyu Zhao, Qimei Cui, Ziqiang Du, Weicai Li, Xi Yu, Wei Ni, Ji Zhang, Xiaofeng Tao, Ping Zhang
https://arxiv.org/abs/2506.02422

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). Little attention has been given to wireless PFL (WPFL), where privacy concerns arise. Performance fairness of PL models is another challenge resulting from communication bottlenecks in WPFL. This paper exploits quantization errors to enhance the privacy of WPFL and proposes a novel quantization-assisted Gaussian differ…

Enabling Scalable Distributed Beamforming via Networked LEO Satellites Towards 6G
Yuchen Zhang Seungnyun Kim, Tareq Y. Al-Naffouri
https://arxiv.org/abs/2506.01382

Enabling Scalable Distributed Beamforming via Networked LEO Satellites Towards 6G
In this paper, we propose scalable distributed beamforming schemes over low Earth orbit (LEO) satellite networks that rely solely on statistical channel state information for downlink orthogonal frequency division multiplexing systems. We begin by introducing the system model and presenting a pragmatic yet effective analog beamformer and user-scheduling design. We then derive a closed-form lower bound on the ergodic sum rate, based on the hardening bound, for the digital beamformer design. Next…

Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenters
Hayden Moore, Sirui Qi, Ninad Hogade, Dejan Milojicic, Cullen Bash, Sudeep Pasricha
https://arxiv.org/abs/2505.23554

Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenters
In recent years, Large Language Models (LLM) such as ChatGPT, CoPilot, and Gemini have been widely adopted in different areas. As the use of LLMs continues to grow, many efforts have focused on reducing the massive training overheads of these models. But it is the environmental impact of handling user requests to LLMs that is increasingly becoming a concern. Recent studies estimate that the costs of operating LLMs in their inference phase can exceed training costs by 25x per year. As LLMs are q…

This https://arxiv.org/abs/2506.04667 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

FlashDMoE: Fast Distributed MoE in a Single Kernel
The computational sparsity of Mixture-of-Experts (MoE) models enables sub-linear growth in compute cost as model size increases, offering a scalable path to training massive neural networks. However, existing implementations suffer from \emph{low GPU utilization}, \emph{significant latency overhead}, and a fundamental \emph{inability to leverage task locality}, primarily due to CPU-managed scheduling, host-initiated communication, and frequent kernel launches. To overcome these limitations, we …

This https://arxiv.org/abs/2305.15549 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Sensitivity-Informed Parameter Selection for Improved Soil Moisture Estimation from Remote Sensing Data
Improving the accuracy of soil moisture estimation is required for advancing irrigation scheduling and water conservation efforts. Central to this task are soil hydraulic parameters, which govern moisture dynamics but are rarely known precisely and must therefore be inferred from observational data. In large-scale agricultural fields, estimating the complete set of these parameters is often impractical due to the sparse and noisy nature of available measurements. To address this challenge, this…

Unit Commitment with Cost-Oriented Temporal Resolution
Junyi Tao, Ran Li, Salvador Pineda
https://arxiv.org/abs/2506.02707 https://ar…

Unit Commitment with Cost-Oriented Temporal Resolution
Time-adaptive unit commitment (UC) has recently been investigated to reduce the scheduling costs by flexibly varying the temporal resolution, which is usually determined by clustering the net load patterns. However, there exists a misalignment between cost and net load patterns due to the discrete start-up costs and out-of-merit-order dispatch triggered by ramping and other constraints. The optimal time-adaptive resolution cannot be completely captured by clustering-based method. This paper pro…

SLURM Heterogeneous Jobs for Hybrid Classical-Quantum Workflows
Aniello Esposito, Utz-Uwe Haus
https://arxiv.org/abs/2506.03846 https://

SLURM Heterogeneous Jobs for Hybrid Classical-Quantum Workflows
A method for efficient scheduling of hybrid classical-quantum workflows is presented, based on standard tools available on common supercomputer systems. Moderate interventions by the user are required, such as splitting a monolithic workflow in to basic building blocks and ensuring the data flow. This bares the potential to significantly reduce idle time of the quantum resource as well as overall wall time of co-scheduled workflows. Relevant pseudo-code samples and scripts are provided to demon…

FlashDMoE: Fast Distributed MoE in a Single Kernel
Osayamen Jonathan Aimuyo, Byungsoo Oh, Rachee Singh
https://arxiv.org/abs/2506.04667 https://

FlashDMoE: Fast Distributed MoE in a Single Kernel
The computational sparsity of Mixture-of-Experts (MoE) models enables sub-linear growth in compute cost as model size increases, offering a scalable path to training massive neural networks. However, existing implementations suffer from \emph{low GPU utilization}, \emph{significant latency overhead}, and a fundamental \emph{inability to leverage task locality}, primarily due to CPU-managed scheduling, host-initiated communication, and frequent kernel launches. To overcome these limitations, we …

LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing
Zhiqing Tang, Wentao Peng, Jianxiong Guo, Jiong Lou, Hanshuai Cui, Tian Wang, Yuan Wu, Weijia Jia
https://arxiv.org/abs/2506.03694

LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing
Lightweight containers provide an efficient approach for deploying computation-intensive applications in network edge. The layered storage structure of container images can further reduce the deployment cost and container startup time. Existing researches discuss layer sharing scheduling theoretically but with little attention paid to the practical implementation. To fill in this gap, we propose and implement a Layer-aware and Resource-adaptive container Scheduler (LRScheduler) in edge computin…

CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection
Woojin Shin, Donghwa Kang, Byeongyun Park, Brent Byunghoon Kang, Jinkyu Lee, Hyeongboo Baek
https://arxiv.org/abs/2505.23317

CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection
Detection Transformers (DETR) are increasingly adopted in autonomous vehicle (AV) perception systems due to their superior accuracy over convolutional networks. However, concurrently executing multiple DETR tasks presents significant challenges in meeting firm real-time deadlines (R1) and high accuracy requirements (R2), particularly for safety-critical objects, while navigating the inherent latency-accuracy trade-off under resource constraints. Existing real-time DNN scheduling approaches ofte…

SealOS : A Sealos-based Approach for Adaptive Resource Optimization Under Dynamic Workloads for Securities Trading System
Haojie Jia, Zhenhao Li, Gen Li, Minxian Xu, Kejiang Ye
https://arxiv.org/abs/2505.23258

SealOS+: A Sealos-based Approach for Adaptive Resource Optimization Under Dynamic Workloads for Securities Trading System
As securities trading systems transition to a microservices architecture, optimizing system performance presents challenges such as inefficient resource scheduling and high service response delays. Existing container orchestration platforms lack tailored performance optimization mechanisms for trading scenarios, making it difficult to meet the stringent 50ms response time requirement imposed by exchanges. This paper introduces SealOS+, a Sealos-based performance optimization approach for securi…