Resource Scheduling Method Research Articles

Cloud Network Collaborative SFC Deployment Under Heterogeneous Resource Of Power White Box Network

The construction of new electric power systems has led to a multiplication in the number of access nodes for electric power IoT information communication, accompanied by a deepening trend in business cloudification. However, due to the insufficient resource linkage and regulation capabilities of the electric power communication network on the cloud side, network side, and edge side, coupled with the lack of a convenient and unified cloud-network resource convergence and control mechanism, the existing electric power communication architecture is gradually finding it difficult to fully meet the demand for deterministic multi-service bearer. Therefore, it is necessary to evolve the original network equipment within the network toward a white box model, opening up interfaces for fine-grained sensing and full-area control, in order to provide more accurate and controllable network transmission services. In this paper, we first propose a cloud-network cooperative resource scheduling architecture for the electric power white box network. This architecture establishes a bearer network to achieve high consistency and cooperativeness of cloud-side end computing power through white box switches, uniformly schedules the global resources of the cloud-network convergence network, and improves the network service quality between cloud-side and side-side. Furthermore, we design a multi-dimensional re-source scheduling method for cloud-network synergy in the electric power white box network. This involves constructing a white box network virtualized heterogeneous resource (VHR) model and a heterogeneous resource control flow (HRCF) model. We transform the problem of merged deployment of service function chains (SFCs) into an SFC merger deployment problem by jointly controlling heterogeneous information, communication, storage, and arithmetic resources, along with the white box switch-specific pipelining resources in the white box network. We then carry out merger optimization of the same kind of function chains. Additionally, we propose a G-GS algorithm based on heuristic methods. Simulation results demonstrate that the method presented in this paper can significantly reduce the cost consumption of white box pipeline resources in the electric power white box network, thereby reducing the processing delay of SFC and improving the quality of electric power service bearing.

A resource scheduling method for cloud manufacturing services based on game theory and supported by blockchain technology

A resource scheduling method for cloud manufacturing services based on game theory and supported by blockchain technology

A Novel Joint Optimization Method of Multi-Agent Task Offloading and Resource Scheduling for Mobile Inspection Service in Smart Factory

A Novel Joint Optimization Method of Multi-Agent Task Offloading and Resource Scheduling for Mobile Inspection Service in Smart Factory

Adaptive Resource Scheduling Algorithm for Multi-Target ISAR Imaging in Radar Systems

Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target’s motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target’s effective rotation angle and the equivalent rotation angular velocity and then define the target’s optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm.

Multi-objective planning for time-cost trade-offs in multi-project parallel environment

PurposeProject managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study focuses on resource-constrained project scheduling in multi-project environments. The research simplifies the problem by adopting a single-project perspective using gain coefficients.Design/methodology/approachIt employs uncertainty theory and multi-objective programming to construct a model. The optimal solution is identified using Matlab, while LINGO determines satisfactory alternatives. By combining these methods and considering actual construction project situations, a compromise solution closely approximating the optimal one is derived.FindingsThe study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.Originality/valueThe research highlights three main contributions: effective resource utilization, project prioritization and conflict management, and addressing uncertainty. It offers decision support for project managers to balance resource allocation, resolve conflicts, and adapt to changing project demands.

AoI-Aware Resource Scheduling for Industrial IoT with Deep Reinforcement Learning

Effective resource scheduling methods in certain scenarios of Industrial Internet of Things are pivotal. In time-sensitive scenarios, Age of Information is a critical indicator for measuring the freshness of data. This paper considers a densely deployed time-sensitive Industrial Internet of Things scenario. The industrial wireless device transmits data packets to the base station with limited channel resources under the constraints of Age of Information. It is assumed that each device has the capacity to store the packets it generates. The device will discard the data to alleviate the data queue backlog when the Age of Information of the data packet exceeds the threshold. We developed a new system utility equation to represent the scheduling problem and the problem is expressed as a trade-off between minimizing the average Age of Information and maximizing network throughput. Inspired by the success of reinforcement learning in decision-processing problems, we attempt to obtain an optimal scheduling strategy via deep reinforcement learning. In addition, a reward function is constructed to enable the agent to achieve improved convergence results. Compared with the baseline, our proposed algorithm can achieve better system utility and lower Age of Information violation rate.

Simulation Analysis of Fire-Fighting Path Planning Based On SLAM

Reasonable and optimal planning of fire fighting paths is crucial to improving the efficiency of fire fighting, and has attracted increasing research attention in recent years. The traditional fire path selection method is mainly to locate the hot spot of the fire scene, and at the same time, use the hot spot sensor to locate the path correction algorithm to realize the planning of the fire path. Thanks to the rapid development of artificial intelligence algorithms, the fire extinguishing path planning and disaster relief resource scheduling methods based on artificial intelligence algorithms have continuously made breakthroughs in accuracy and speed. Based on simultaneous localization and mapping (SLAM) technology, this paper solves the positioning and mapping problems simultaneously to determine the optimal path. Specifically, this paper mainly uses the map construction and navigation functions of SLAM, and uses Gazebo to simulate an open shopping mall environment and a factory environment with multiple obstacles. Through simulation experiments, the results verify the feasibility of this proposed method in fire evacuation path planning, and can realize the evacuation path planning in case of fire.

Research on Regional Energy System Planning Considering Flexible Access of Electric Vehicles

The high proportion of renewable energy sources and intermittent loads pose challenges to the flexible operation of regional energy systems. To improve the flexible operation level of regional energy system, this paper considers the uncertainty of energy storage charging and discharging, demand-side management, and V2G of electric vehicles, and establishes a flexible resource supply capacity and cost model. With the goal of minimum flexibility demand and economic optimization, the implementation of flexible supply and demand balance constraints is added. The advantages and disadvantages of the traditional resource scheduling scheme and the unified flexible resource scheduling method are compared from the net load volatility, the maximum operating volatility and the whole time scale flexible supply and demand balance index. The algorithm analysis proves the advantages of the proposed scheduling scheme in improving the flexibility of the regional energy system, which can describe the flexibility of the regional energy system well and can be quantitatively characterized.

Optimization of Emergency Resource Dispatch for Overseas Long-distance Natural Gas Pipeline Leaks

Overseas long-distance natural gas pipelines usually span across multiple regions or even countries, facing more severe environmental conditions and more prone to leakage and other accidents, and the study of the optimization method of emergency resource scheduling for overseas long-distance natural gas pipeline leakage accidents can help to reduce the casualties and property losses. Therefore, this paper establishes an emergency resource scheduling model in line with the emergency disposal mechanism of overseas long-distance natural gas pipeline leakage accidents, takes into account the variability of the operating ability of maintenance personnel, and improves the accuracy of the scheduling program to a certain extent, and also considers the actual environment of overseas long-distance natural gas pipelines, takes into account the impact of landslides on the emergency scheduling time, and calculates the probability of landslides occurring in the landslide point by using the interpolated value of rainfall The rainfall interpolation is used to calculate the probability of landslide at the landslide point, and the emergency dispatching time is corrected to make the dispatching program more in line with the actual demand. The analysis of the results shows that the emergency resource scheduling time is dominant in the case of small amount of maintenance and repair work and short total maintenance and repair time.

Mathematical and Intellectual Innovation in Higher Education Talent Training Models

Abstract The economy and society have entered a highly transformative digital age. Digital Intelligence promotes the change of social and economic forms and promotes the integration and innovation of technology, economy and society. In this paper, we formulate the goal of training talents with digital intelligence thinking and the model of training talents with digital intelligence thinking, and propose a strategy for implementing talent training. Using big data technology (cloud computing resource scheduling method, virtual scene dynamic splicing technology) to assist in the implementation of teaching strategies. Conduct simulation experiments to analyze the performance of the cloud resource scheduling algorithm proposed in this paper on different numbers of virtual machines. Evaluate the effect of virtual scene splicing technology on image alignment. Discuss the influences factors from the three aspects of cultivation objectives, curriculum arrangement, and scientific research training in light of the satisfaction of the implementation of the mathematical and intellectual talent cultivation model. The cultivation objectives for numerical intelligence ability are viewed by 40.61% of the students as average, and 13.75% of them feel completely satisfied. Only 4.31% of the students agreed on scientific research and innovation. It can be seen that most of the students did not carry out any interdisciplinary research in depth. It shows that the implementation of the strategy for cultivating mathematical and intellectual talents still needs improvement and strengthening.

A new multi-domain cooperative resource scheduling method using proximal policy optimization

A new multi-domain cooperative resource scheduling method using proximal policy optimization

Retraction: Balanced scheduling method of network information resources for cloud storage

Retraction: Balanced scheduling method of network information resources for cloud storage

Dispatching Method of Distributed Energy Storage Resources in Substation Based on Peak-shaving Compensation Response Model

The conventional distributed energy storage resource scheduling method is mainly based on automatic load demand response scheduling, and the load response gap between peak hours and low hours is large, which affects the economic benefits of energy storage resource scheduling. Therefore, a distributed energy storage resource scheduling method based on a peak shaving compensation response model is designed. The cluster scheduling characteristics of distributed energy storage resources are extracted, and the distributed energy storage resources are charged and discharged synchronously at the same scheduling node. Combined with the resource scheduling strategy of the substation, the scheduling load of energy storage resources is limited so that the scheduling impact on the substation power grid is reduced. Based on the peak-shaving compensation response model, the dispatching objective function of distributed energy storage resources in the substation is optimized, the dispatching of distributed energy storage resources in the substation is optimized from the perspective of safe and economic operation, and the stochastic error of distributed energy storage resource scheduling is avoided in the form of a compensation response constraint to meet the resource scheduling requirements. Comparative experiments show that this method is more economical and can be applied to real life.

Resource Scheduling Method for Equipment Maintenance Based on Dynamic Pricing Model in Cloud Manufacturing

Cloud manufacturing, as a novel service mode in the manufacturing field with the features of flexible resource assignment, timely service, and quantity-based pricing, has attracted extensive attention in recent years. The cloud manufacturing industry uses a significant amount of smart equipment. In this context, equipment maintenance resource scheduling (EMRS) is an important subject that needs to be studied. Cloud manufacturing platforms must provide effective services for equipment maintenance in a timely manner. In order to improve the efficiency of cloud manufacturing platforms and meet the needs of users, an effective EMRS scheme is required. In this paper, we propose a dynamic resource allocation model for cloud manufacturing to meet the needs of users and maximize the benefit of a cloud manufacturing platform. The model takes into account the needs of users and the benefits of a cloud production platform. The contributions of this paper are divided into the following three aspects. First, the E-CARGO model using role-based collaboration theory is introduced to formally model EMRS activities, forming a solvable optimization model. Second, a dynamic pricing model with a center symmetric curve is designed to realize the flexible conversion between time, cost, and price. Third, the concept of satisfaction in fuzzy mathematics is introduced, in order to meet the different needs of users and platforms, in terms of time, price, and cost, while ensuring service quality and the platform’s benefits. Finally, an improved genetic algorithm is used to solve the cloud manufacturing resource scheduling problem, and good experimental results are obtained. These results demonstrate that the proposed dynamic pricing model is reasonable, and the allocation scheme obtained through a genetic algorithm is feasible and effective.

Software-Defined Satellite Observation: A Fast Method Based on Virtual Resource Pools

In recent years, the proliferation of remote sensing satellites has dramatically increased the demands of Earth observation and observing efficiency. Designing a promising satellite resource scheduling method is a pivotal way to meet the requirements of this scenario. However, with hundreds or more satellites involved, the existing optimization methods struggle to address the NP-hard resource scheduling problem effectively. In this paper, an approach named software-defined satellite observation (SDSO) is proposed. First, adopting the new design ideology, we define a unified specification based on a discrete spatial grid to describe the observation capability of all satellites. The observation resources are virtualized using the virtual resource pool technique and then stored in the database in advance, implementing on-demand acquisition for observation resources. Next, we designed a model of the remote sensing satellite resource scheduling problem based on a virtual resource pool and designed a solution method for searching information within the virtual resource pool. Finally, the experimental results show that the computational efficiency of the proposed SDSO methodology has a substantial advantage over the traditional methods. Meanwhile, with the growing number of satellites involved in scheduling, there is only a slight degradation in the execution performance of our method, while the time complexity of optimization-based approaches increases exponentially.

A novel hierarchical distributed vehicular edge computing framework for supporting intelligent driving

Recently, various infrastructure-assisted or onboard driving assistant applications have been proposed as a component of intelligent transportation systems (ITS) to improve the transportation system’s efficiency and release public concern about road safety. However, such AI-assisted intelligent applications are mainly data-driven and put great demands on the computing power of the ITS systems. Therefore, in the highly dynamic Internet-of-Vehicles environment in ITS, how to effectively coordinate the limited computing power of the various components of the system and realize reliable support for such resource-consuming applications through efficient resource allocation methods is the focus of our research. Accordingly, a novel joint computing and communication resource scheduling method is proposed to fulfill those ITS applications’ inherent heterogeneous quality of service (QoS) requirements. By fully exploiting the computing resources provided by the onboard computing device, the edge computing device located in the vehicle’s proximity and remote data center, we designed a hierarchical three-layer Vehicular Edge Computing (VEC) framework. Briefly, an onboard joint computation offloading and transmission scheduling policy is designed to assign corresponding offloading decisions to the locally generated computing tasks by considering the vehicle’s computing resources and real-time network link status. Additionally, a new distributed resource allocation policy is developed for the edge devices, in which we derive a server selection policy and allocate communication time based on our proposed metric. To evaluate the performance and validate the effectiveness of our proposed method, we conduct extensive simulation tests and ablation experiments, respectively. The results show that our approach can achieve stable performance in various experimental settings. Also, compared to the state-of-the-art algorithms, our joint resource allocation policy significantly reduces the scheduling overhead, improves the utilization of system resources, and minimizes the data transmission delay caused by vehicle motion.

Indoor millimeter wave D2D communication resource optimization based on improved PMVC and CTRA algorithms

In order to improve the quality of millimeter wave communication in indoor environment and ensure the maximum use of link resources. It is proposed to construct indoor communication transmission characteristics through multi sphere link model and indoor millimeter wave reflection blocking model. At the same time, the improved PMVC algorithm and CTRA algorithm are used to optimize the resource scheduling of links in indoor central scenes and edge scenes respectively. The simulation results show that compared with the original PVC algorithm and greedy algorithm, PMVC has a maximum link throughput of 5.01 Gbps under the optimized scheduling of the improved vertex coloring algorithm, while the maximum throughput of the traditional vertex coloring algorithm and greedy algorithm are 4.73 Gbps and 4.57 Gbps respectively. In the case of fixed link transmission beam width, as the number of link flows increases, the slot throughput of the link decreases continuously. Under the width of 30, the maximum slot throughput of PMVC algorithm is 2.75 Gbps, and the maximum slot throughput of greedy algorithm is 2.48 Gbps; When the beam width is 60, the maximum slot throughput of PMVC algorithm and greedy algorithm is 2.25 Gbps and 1.55 Gbps respectively. The proposed PMVC resource scheduling method can effectively alleviate the shortage of indoor spectrum resources and reduce indoor transmission interference of millimeter wave.

A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning

Space-based systems providing remote sensing, communication, and navigation services are essential to the economy and national defense. Users’ demand for satellites has increased sharply in recent years, but resources such as storage, energy, and computation are limited. Therefore, an efficient resource scheduling strategy is urgently needed to satisfy users’ demands maximally and get high task execution benefits. A hierarchical scheduling method is proposed in this work, which combines improved ant colony optimization and an improved deep Q network. The proposed method considers the quality of current task execution and resource load balance. The entire resource scheduling process contains two steps, task allocation and resource scheduling in the timeline. The former mainly implements load balance by improved ant colony optimization, while the latter mainly accomplishes the high task completion rate by an improved deep Q network. Compared with several other heuristic algorithms, the proposed approach is proven to have advantages in terms of CPU runtime, task completion rate, and resource variance between satellites. In the simulation scenarios, the proposed method can achieve up to 97.3% task completion rate, with almost 50% of the CPU runtime required by HAW and HADRT. Furthermore, this method has successfully implemented load balance.

Research on dynamic scheduling and perception method of assembly resources based on digital twin

ABSTRACT The uncertainty and dynamic changes in assembly resources can seriously affect the normal operation of the assembly plant. In response to the problems of incomprehensive resource control mechanism, poor timeliness of monitoring data, and low level of scheduling intelligence in the assembly plant, a dynamic scheduling and perception method of assembly resources based on digital twin is proposed so that the uncertainties in the assembly process can be monitored and dealt with in time. In this paper, a dynamic scheduling model of assembly resources based on a digital twin is constructed, and the operation mechanism of assembly resources in the constructed digital twin model is expounded. And the dynamic perception method of assembly resources based on the Petri network is studied in detail, and the perception and interaction models of four assembly resources in the product assembly process are constructed: workpiece, handling equipment, assembly center, and storage area. Finally, combined with the assembly workshop of enterprise A’s frame factory, the Petri network model is simulated with the help of the CPN Tools simulation tool to obtain real-time and simulation data such as assembly resources and workstation operation time are obtained, which provides a scientific basis for the smooth implementation of enterprise assembly plan and dynamic scheduling of assembly resources.

Control-Aware Resource Scheduling Method for Wireless Networked Control Systems

Control-Aware Resource Scheduling Method for Wireless Networked Control Systems