Resource Scheduling Research Articles

Trustworthy and efficient project scheduling in IIoT based on smart contracts and edge computing

To facilitate flexible manufacturing, modern industries have incorporated numerous modular operations such as multi-robot services which can be expediently arranged or offloaded to other production resources. However, complex manufacturing projects often consist of multiple tasks with fixed sequences, posing a significant challenge for smart factories in efficiently scheduling limited robot resources to complete specific tasks. Additionally, when projects span across factories, ensuring faithful execution of contracts becomes another challenge. In this paper, we propose a modified combinatorial auction method combined with blockchain and edge computing technologies to organize project scheduling. Firstly, we transform efficient resource scheduling into a resource-constrained multi-project scheduling problem (RCPSP). Subsequently, the solution integrates combinatorial auction with random sampling (CA-RS) into smart contracts. Alongside security analysis, simulations are conducted using real data sets. The results indicate that the suggested CA-RS approach significantly enhances efficiency and security in resource arrangement within the industrial Internet of Things compared to baseline algorithms.

Online Physical Education Teaching Resources Integration Method Based on Multi-Objective Optimization Algorithm

In order to improve the sharing level of online physical education teaching resources, the integration design of online physical education teaching resources is carried out by using MOOC resource information fusion technology and closed frequent item fusion calculation method, and the intelligent scheduling and management ability of online physical education teaching resources is improved. An online physical education teaching resource integration model based on big data information fusion and cluster scheduling is proposed. The factors of online physical education teaching demand scale are initialized, the structure of common factors of online physical education teaching resources is determined by factor analysis method, the orthogonal rotation of indicators by maximum variance method is adopted, the cloud computing model is adopted to analyze the integration structure of online physical education teaching resources, and the feature quantity of cross frequent items rules of online physical education teaching resources is extracted. The parallel K-means clustering model of online physical education resources integration is constructed, the fuzzy correlation degree features of online physical education resources are calculated, the extracted correlation features of online physical education resources are processed by C-means clustering method for big data fusion, the multi-objective optimization technology is adopted for online physical education resources integration and adaptive scheduling, and the adaptive scheduling ability of online physical education resources is improved. The SPSS statistical software is used for empirical analysis, and the results show that the integration degree of online physical education teaching resources by this method is high, which promotes the development of online physical education teaching.

Distributionally Robust Optimization of On-Orbit Resource Scheduling for Remote Sensing in Space-Air-Ground Integrated 6G Networks

Distributionally Robust Optimization of On-Orbit Resource Scheduling for Remote Sensing in Space-Air-Ground Integrated 6G Networks

Mission-Driven Resource Scheduling in Satellite-Terrestrial Networks: From Perspective of Collaboration and Reconfiguration

Mission-Driven Resource Scheduling in Satellite-Terrestrial Networks: From Perspective of Collaboration and Reconfiguration

Optimizing Resource Scheduling for Multi-Scenario Mixed Service Groups under Edge Cloud-Native Environments Using Simulation Learning

The evolution of cloud and edge computing technologies has brought about resource management challenges. Traditional resource scheduling strategies fall short in dynamic cloud-edge environments, one of the challenges is identifying system state changes in multi-scenario edge cloud-native environments. The dynamic orchestration and deployment of container resources are crucial. To address this issue, we introduce a virtual environment, which generates interactions of multi-scenario mixed service groups. Furthermore, we proposed a multi-agent adversarial imitation learning approach, which is trained in the virtual environment. Experiments reveal that our approach, which is fully trained in the virtual mixed-service environment, results in no physical sampling costs and significantly outperforms traditional supervised approaches.

Dynamic mathematical model for resource management and scheduling in cloud computing environments

The object of the research is resource management and scheduling in Kubernetes clusters, in particular, data centers. It was determined that in many publications dedicated to optimization models of scheduling for Kubernetes, mathematical models either do not include constraints at all, or only have the constraints determined on the high level only. The purpose of the research is the creation of a dynamic low-level mathematical optimization model for resource management and scheduling in cloud computing environments that utilize Kubernetes. Examples of such environments include the data centers where the customers can rent both dedicated servers and resources of shared hosting servers that are allocated on demand. The suggested model was created using the principles of creation of mathematical models of discrete (combinatorial) optimization, and was given the name “dynamic” because it takes the time parameter into account. The model receives data about individual servers in the cluster and individual pods that should be launched as an input. The model aims to regulate not only individual assignments of pods to nodes, but also turning on and off the servers. The model has objectives of: minimization of the average number of shared hosting servers running; maximization of the average resource utilization coefficient on such servers; minimization of the number of occasions when the servers are turned on and off; minimization of resource utilization by the pods that are running on shared hosting servers but created by the customers renting the dedicated servers. The model considers resource constraints, among other limitations.

Characterizing Droughts During the Rice Growth Period in Northeast China Based on Daily SPEI Under Climate Change.

In agricultural production, droughts occurring during the crucial growth periods of crops hinder crop development, while the daily-scale standardized precipitation evapotranspiration index (SPEI) can be applied to accurately identify the drought characteristics. In this study, we used the statistical downscaling method to obtain the daily precipitation (Pr), maximum air temperature (Tmax) and minimum air temperature (Tmin) during the rice growing season in Heilongjiang Province from 2015 to 2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 in CMIP6, to study the spatial and temporal characteristics of drought during the rice growing season in cold region and the effect of climate change on drought characteristics. The potential evapotranspiration (PET0) was calculated using the regression correction method of the Hargreaves formula recommended by the FAO, and the daily SPEI was calculated to quantitatively identify the drought classification. The Pearson correlation coefficient was used to analyze the correlation between the meteorological factors (Pr, Tmax, Tmin), PET0 and SPEI. The results showed that: (1) Under 3 SSP scenarios, Pr showed an increasing trend from the northwest to the southeast, Tmax showed an increasing trend from the northeast to the southwest, and higher Tmin was mainly distributed in the east and west regions. (2) PET0 indicated an overall interannual rise in the three future SSP scenarios, with higher values mainly distributed in the central and western regions. The mean daily PET0 values ranged from 4.8 to 6.0 mm/d. (3) Under SSP1-2.6, rice mainly experienced mild drought and moderate drought (-0.5 ≥ SPEI > -1.5). The predominant drought classifications experienced were mild, moderate, and severe drought under SSP2-4.5 and SSP8.5 (-0.5 ≥ SPEI > -2.0). (4) The tillering stage experienced the highest drought frequency and drought intensity, with the longest drought lasting 24 days. However, the heading flower stage had the lowest drought frequency and drought intensity. The drought barycenter was mainly in Tieli and Suihua. (5) The PET0 was most affected by the Tmax, while the SPEI was most affected by the Pr. This study offers a scientific and rational foundation for understanding the drought sensitivity of rice in Northeast China, as well as a rationale for the optimal scheduling of water resources in agriculture in the future.

A Smart Contract-Based Algorithm for Offline UAV Task Collaboration: A New Solution for Managing Communication Interruptions

Environmental factors and electronic interference often disrupt communication between UAV swarms and ground control centers, requiring UAVs to complete missions autonomously in offline conditions. However, current coordination schemes for UAV swarms heavily depend on ground control, lacking robust mechanisms for offline task allocation and coordination, which compromises efficiency and security in disconnected settings. This limitation is especially critical for complex missions, such as rescue or attack operations, underscoring the need for a solution that ensures both mission continuity and communication security. To address these challenges, this paper proposes an offline task-coordination algorithm based on blockchain smart contracts. This algorithm integrates task allocation, resource scheduling, and coordination strategies directly into smart contracts, allowing UAV swarms to autonomously make decisions and coordinate tasks while offline. Experimental simulations confirm that the proposed algorithm effectively coordinates tasks and maintains communication security in offline states, significantly enhancing the swarm’s autonomous performance in complex, dynamic scenarios.

Spatial extension of soil water regime variables derived from soil moisture values using geomorphological variables in Hungary

Accurate measurement and spatial extension of soil properties are essential in geoinformatics and precision agriculture for effective resource management, particularly irrigation planning. This study addresses the challenge of extending soil moisture data and related soil water regime variables in heterogeneous agricultural landscapes by integrating geomorphological variables (GVs) derived from high-resolution digital elevation models (DEM). In digital soil mapping, machine learning and geostatistical models often struggle with validation due to data scarcity and variability across space through many geographical regions that come from the point readings of soil properties. A different approach was developed in the form of a new methodology combining two hourly Sentek soil moisture measurements from the topsoil with DEM-derived GVs to model and extend soil water regime variables. The research was conducted on an agricultural field in a hilly area with diverse geomorphological variability. The model’s performance was validated using cross-validation techniques. The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. We observed that certain GVs, such as the slope, both type of curvatures and the convergence, were strong predictors of soil moisture variation, enabling the model to produce more accurate irrigation recommendations for agricultural areas with similar geomorphological areas. One of the soil water regime variables was validated during the preliminary validation with mixed results. The main issue was coming from the field use and spatial scarcity of the measurements. Our approach not only provides a different method for spatially extending the current soil water regime data but also offers a framework for improving irrigation decision-making with the help of other value rates and limit related soil regime variables derived from the time series readings from the soil moisture sensors. With its variables, the model allows for forecasts of soil moisture changes, which can inform better irrigation scheduling and water resource management, all based on data from the soil monitoring sensor system.

Robust Task Offloading and Trajectory Optimization for UAV-Mounted Mobile Edge Computing

Mobile edge computing (MEC) deployed in unmanned aerial vehicles (UAVs) has shown special strength by enhancing computational capacity and prolonging the battery lives of terrestrial user equipment (UE). Nevertheless, current research lacks studies of robust offloading scheme scheduling and trajectory planning using terrestrial random channels. The state-of-the-art joint task-offloading and trajectory-planning optimization techniques for UAV-mounted MEC are focused on scenarios where only air–ground channels exist rather than time-varying terrestrial channels. By contrast, this paper considers the scenario where both the time-varying/random terrestrial channels and the line-of-sight air–ground channels occur. Aiming at robust resource scheduling for energy-efficient UAV-assisted MEC, we formulate a novel joint optimization of UAV trajectory planning and task offloading, which, however, is highly nonconvex. As a countermeasure, the original optimization is recast as subproblems related to task offloading and trajectory planning and solved by a novel robust iterative optimization algorithm that combines the methods of weighted minimum mean square error, S-procedure, successive convex approximation, etc. Numerical results indicate that, compared to various baselines, the proposed algorithm can effectively reduce energy consumption and optimize the trajectory in the presence of a large number of input tasks. In addition, in terms of stability and effectiveness, the proposed robust iterative optimization algorithm can reduce energy consumption more stably in time-varying/random channels compared to non-robust schemes.

Research on the Model of High-Speed Railway Station Security Resource Scheduling Based on Dynamic Passenger Flow Prediction

Research on the Model of High-Speed Railway Station Security Resource Scheduling Based on Dynamic Passenger Flow Prediction

Application Strategies and Fields of Digital Technology in Urban Public Service Resource Allocation

This article discusses the application of digital technology in the allocation of urban public service resources, emphasizing the importance of big data, the Internet of Things, and artificial intelligence technologies in improving the efficiency of resource matching. In the field of transportation, by setting up sensors and monitoring equipment, establishing regional traffic information exchange platforms, intelligent traffic light systems, and real-time dispatch systems have effectively alleviated traffic congestion and optimized the allocation of public transportation resources. In terms of public health, digital technology has improved the efficiency of medical resource scheduling, especially in responding to sudden public health events, by real-time monitoring and dynamic assessment of medical needs, ensuring public health safety. Public safety management has achieved comprehensive urban security through digital monitoring systems, with intelligent monitoring platforms automatically identifying abnormal behaviors in areas with high crime rates, enhancing the accuracy of police force strikes and prevention capabilities. The intelligent supply of public sports resources has improved the health levels of residents through personalized services, using digital technology to collect residents' health data and exercise needs, formulating personalized exercise prescriptions, and achieving scientific management and digital supply. The article concludes that with the continuous advancement of digital technology, the management and optimization of urban public service resources will become more efficient and accurate, promoting a comprehensive upgrade and intelligent development of the urban public service system.

Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels.

In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions. This paper examines the impact of Doppler shifts caused by vehicle speeds on data transmission in error-prone channels. Based on the M/M/1 and D/M/1 queuing theory models, we derive expressions for the age of information and optimize the system's average AoI by adjusting the data extraction rates of vehicles (which affect system utilization). We propose an online optimization algorithm that dynamically adjusts the vehicles' data extraction rates based on environmental changes to ensure optimal AoI. Simulation results have demonstrated that adjusting the data extraction rates of vehicles can significantly reduce the system's AoI. Additionally, in the network scenario of this work, the AoI of the D/M/1 system is lower than that of the M/M/1 system.

Cloud Computing Resource Scheduling Method Based on Optimization Theory

Cloud Computing Resource Scheduling Method Based on Optimization Theory

Optimizing Scheduling Techniques for Enhanced Carrier Aggregation in LTE-Advanced Networks

In this study, we investigate resource scheduling strategies for the downlink in LTE-Advanced networks, focusing specifically on systems that employ Carrier Aggregation (CA) with multiple Component Carriers (CCs). Effective scheduling is crucial in CA-enabled LTE-Advanced to maximize efficiency and support diverse user needs. We examine three conventional scheduling schemes—Joint User Scheduling (JUS), Separated Random User Scheduling (SRUS), and Separated Burst-Level Scheduling (SBLS). Although JUS yields superior performance, it is complex and does not factor in Quality of Experience (QoE) metrics. In contrast, SRUS and SBLS, while less computationally intensive, underutilize CA and fall short in resource allocation fairness. To overcome these limitations, we propose a new scheduling scheme, Quality of Service and Channel Scheduling (QSCS), which dynamically adjusts CC allocations at the burst level based on user service priority and signal quality. Simulation results show that QSCS achieves throughput similar to JUS while selecting optimal CCs based on Quality of Service (QoS) and channel parameters, resulting in enhanced QoE compared to other methods. This proposed approach demonstrates significant potential for improving resource allocation and user satisfaction in LTE-Advanced networks.

MAARS: Multiagent Actor-Critic Approach for Resource Allocation and Network Slicing in Multiaccess Edge Computing.

This paper presents a novel algorithm to address resource allocation and network-slicing challenges in multiaccess edge computing (MEC) networks. Network slicing divides a physical network into virtual slices, each tailored to efficiently allocate resources and meet diverse service requirements. To maximize the completion rate of user-computing tasks within these slices, the problem is decomposed into two subproblems: efficient core-to-edge slicing (ECS) and autonomous resource slicing (ARS). ECS facilitates collaborative resource distribution through cooperation among edge servers, while ARS dynamically manages resources based on real-time network conditions. The proposed solution, a multiagent actor-critic resource scheduling (MAARS) algorithm, employs a reinforcement learning framework. Specifically, MAARS utilizes a multiagent deep deterministic policy gradient (MADDPG) for efficient resource distribution in ECS and a soft actor-critic (SAC) technique for robust real-time resource management in ARS. Simulation results demonstrate that MAARS outperforms benchmark algorithms, including heuristic-based, DQN-based, and A2C-based methods, in terms of task completion rates, resource utilization, and convergence speed. Thus, this study offers a scalable and efficient framework for resource optimization and network slicing in MEC networks, providing practical benefits for real-world deployments and setting a new performance benchmark in dynamic environments.

Enhancing task execution: a dual-layer approach with multi-queue adaptive priority scheduling.

Efficient task execution is critical to optimize the usage of computing resources in process scheduling. Various task scheduling algorithms ensure optimized and efficient use of computing resources. This article introduces an innovative dual-layer scheduling algorithm, Multi-Queue Adaptive Priority Scheduling (MQAPS), for task execution. MQAPS features a dual-layer hierarchy with a ready queue (RQ) and a secondary queue (SQ). New tasks enter the RQ, where they are prioritized, while the SQ contains tasks that have already used computing resources at least once, with priorities below a predefined threshold. The algorithm dynamically calculates the time slice based on process priorities to ensure efficient CPU utilization. In the RQ, the task's priority level defines its prioritization, which ensures that important jobs are completed on time compared to other conventional methods where priority is fixed or no priority parameter is defined, resulting in starvation in low-priority jobs. The simulation results show that MQAPS better utilizes CPU resources and time than traditional round-robin (RR) and multi-level scheduling. The MQAPS showcases a promising scheduling technique ensuring a balanced framework for dynamic adjustment of time quantum and priority. The MQAPS algorithm demonstrated optimization, fairness, and efficiency in job scheduling.

Operation mode and scheduling plan optimization approach for multiple balancing zones in a distribution system

Modern power systems are developing rapidly, with distributed energy, energy storage devices, adjustable loads, and other flexible resources consolidated through microgrids, virtual power plants, and integrated source–network–load–storage systems. This consolidation under various balancing zone models facilitates synergistic operations and has become critical to enhancing distributed power consumption and ensuring the reliability of electricity supply. Therefore, in light of the challenges of inadequate economic efficiency, reduced accommodation of renewable energy, and poorer operational reliability in distribution networks, this study proposes a category selection and flexibility resource scheduling method that considers the differences in multiple balancing zone models and modes. Firstly, the approach establishes a multi-dimensional characteristic evaluation index and multiple balancing zone operation models. The characteristic evaluation indicators are then utilized to assess the unique properties of the balancing zone system and eliminate unreasonable operating modes. Finally, through analyzing the effectiveness and differences of various balancing zone operation modes, an optimal operation mode is selected, and a scheduling plan is formulated. We conclude that the scheduling plan optimization method considering the operation mode can realize a reasonable choice of operation modes and achieve benefit optimization.

A NOVEL OPTIMIZATION APPROACH FOR ENHANCING EFFICIENCY IN AIRCRAFT MAINTENANCE PLANNING AND SCHEDULING

The operational side of aircraft maintenance plays a key role in ensuring smooth workflow and the practiced processes' reliability in aircraft maintenance repair and overhaul (MRO). Correct MRO planning effectively reduces downtime and enhances the overall accuracy and dependability of procedures. An optimal schedule of maintenance resources and capacity, which entails using the least amount of ground time and schedule modifications, can be used to ensure that all activities are completed productively and consistently before their deadlines. This paper delves into the creation of an optimization approach that considers the dynamic environment of aircraft maintenance. The approach deals with a constraint programming (CP) model for heavy maintenance resources and capacity scheduling, in which variables such as scheduled maintenance tasks, ground time, workload distribution, and maintenance due dates are bounded by resources and technicians’ availability. The proposed CP model showed a strong ability to satisfy different constraints and conditions that were introduced in the artificial industrial environment within a short solving time of 3.12 seconds. Thus, it showed promising results in the case of further developments.

Logistics Optimization for Resource Allocation and Scheduling Using Time Slots

Abstract In the field of logistics, efficient scheduling and resource allocation are essential for ensuring the seamless flow of goods through transportation networks. This paper addresses the Interval Scheduling Problem, a combinatorial optimization challenge, in the context of logistics planning for goods transportation. The study examines how optimized appointment scheduling and resource allocation can enhance the performance of transportation networks. By combining theoretical insights, algorithmic solutions, and practical applications, this work proposes a comprehensive approach grounded in mathematical models that account for time, resource, and capacity constraints, alongside a computational implementation. Utilizing advanced computational techniques and real-time data integration, the proposed solutions aim to increase operational effectiveness and competitiveness while reducing costs in transportation logistics.