Resource Scheduling Scheme Research Articles

Modelling and simulation of cloud‐native‐based edge computing terminals for power distribution

AbstractThe introduction of cloud‐native technology has significantly changed the architecture of applications and the mechanism for the collaborative operation of components in power distribution edge computing terminals (PDECT). To develop an effective quantitative analysis tool for PDECT performance, the composition and characteristics of cloud‐native PDECT are studied, and the modelling and simulation of cloud‐native PDECT are proposed. Subsequently, modelling is implemented through the simulation software CloudSim, achieving the simulation of microservices, containers, declarative configuration, and container orchestration with the consideration of power distribution scenarios. Then, by the proposed simulation scenario module, various elements of the power distribution scenarios can be self‐defined. Finally, by demonstrating the principles and implementation mechanisms of the proposed modelling method and simulation tool, and comparing simulation results for different service time ranges, access devices, resource configurations of PDECT, request occurrence rates, and resource scheduling strategies, the validity and effectiveness of the proposed modelling method and simulation tool are verified.

Multi-layer resource scheduling architecture and algorithm for a service-oriented optical network based on a fine grain OTN

With the development of service, there are demands for flexible connection scheduling based on customer-side services, fast connection response, and efficient scheduling of line-side resources in an optical transport network (OTN). This article proposes a service-oriented multi-layer resource scheduling architecture and algorithm based on the introduction of OTN fine grain technology. Specifically, we propose a service awareness scheme that enables OTNs to better match service requirements. Based on a centralized distributed collaborative architecture, we extend a path computation element communication protocol (PCEP) to achieve more efficient connection distribution. We further design a resource scheduling strategy and an algorithm based on multi-layer collaboration to perform differentiated scheduling for services with different priorities. Through the mechanisms and algorithms proposed, it is technically feasible to achieve precise matching of customer-side services, flexible scheduling of connections and resources, and efficient service response, and promote the evolution of the OTN service supply from traditional fixed point-to-point connections to flexible service-oriented optical network (SOON) connections.

Non-orthogonal resource scheduling with enhanced preamble detection method for cellular random access networks

In this article, we propose a non-orthogonal resource scheduling (NORS) scheme with an enhanced preamble detection (PD) method for cellular random access (RA) systems. The enhanced PD method is designed based on a deep learning technique that can classify three PD statuses: idle, collision-free, and collision. Because of this collision resolution capability, the base station (BS) can exploit radio resources non-orthogonally in contention-based RA protocols. In addition, we mathematically analyze the performance of our proposed NORS scheme in terms of RA success probability and resource utilization efficiency and conduct a comparison with the baseline orthogonal resource scheduling (ORS) scheme. Through simulations, we verify the validity of our mathematical analysis. As a result, our results will be a useful guideline for resource scheduling and provide support for massive connectivity.

The analysis of educational informatization management learning model under the internet of things and artificial intelligence

This study explores the influence of the Internet of Things (IoT) and Artificial Intelligence (AI)-enhanced learning models on student management in educational informatization management. A game-theoretic enhanced learning model is proposed to achieve this objective, incorporating resource scheduling strategies under fog computing and a student management system that integrates IoT and AI technologies. This model’s performance and the student management system are then tested. The results indicate that the fog computing-based hierarchical Q-learning (Q) model proposed in this study achieves faster convergence than a single Q model, reaching convergence after 80 training rounds, ten rounds earlier than the comparative algorithm. The model exhibits a lower average workload delay of 0.5 ms and fog node delay below 1 ms, showcasing significant advantages in terms of overall cost-effectiveness, thus minimizing service costs. The student management system has 3000 concurrent user connections, static page request times ranging from 0 to 25 s, login response time predominantly at 60 s, and a capacity to process up to 20 parallel tasks per second with zero errors. The system functionalities are fully realized, meeting usage demands effectively and achieving the highest average functional score of 9.03 for online interaction functionality. This study demonstrates the efficacy of the game-theoretic enhanced learning model in a fog computing environment and the positive impact of IoT and AI technologies on student management. The proposed student management system better caters to individual student needs, enhancing learning outcomes and experiences. The study's innovation lies in the integration of IoT technology with AI-enhanced learning models, coupled with the introduction of game-theoretic resource scheduling strategies, enabling the student management system to intelligently identify student requirements, allocate learning resources, and dynamically optimize the educational process, ultimately improving learning outcomes. This holds significant implications for enhancing education quality and promoting personalized student development.

Optimizing Maintenance Resource Scheduling and Site Selection for Urban Metro Systems: A Multi-Objective Approach to Enhance System Resilience

This study developed an optimization model for the strategic location of maintenance resource supply sites and the scheduling of multiple resources following failures in urban metro systems, with the objective of enhancing system resilience. The model employs a multi-objective optimization framework, focusing primarily on minimizing resource scheduling time and reducing costs. It incorporates critical factors such as spatial location, network topology, station size, and passenger flow. A hybrid method, combining the non-dominated sorting genetic algorithm III and the technique for order of preference by similarity to ideal solution, is used to solve the model, with its effectiveness confirmed through a case study of the Nanjing Metro system. The simulation results yielded an optimal number of 21 maintenance resource supply stations and provided their placement. In the event of large-scale failures, the optimal resource scheduling strategy ensures demand satisfaction rates exceed 90% at critical stations, maintaining an overall rate of 87.09%, therefore significantly improving resource scheduling efficiency and the system’s emergency response capabilities and enhancing the physical resilience and recovery capabilities of the urban metro system. Moreover, the model accounts for economic factors, striving to balance emergency response capabilities with production continuity and cost efficiency through effective maintenance strategies and resource utilization. This approach provides a systematic framework for urban metro systems to manage sudden failures, ensuring rapid recovery to normal operations and minimizing operational disruptions in scenarios of limited resources.

Saver: a proactive microservice resource scheduling strategy based on STGCN

Saver: a proactive microservice resource scheduling strategy based on STGCN

Priority-weight-based computing and spectrum resource scheduling for dependent tasks in EON-supported computing first networks

The computing first network (CFN) supported by the elastic optical network (EON) is proposed as an innovative information infrastructure, where end users can access computing resources on demand. However, since the link capacity is limited, the network capacity bottleneck may cause task offloading failures even with sufficient computing resources. This paper proposes a cost-efficient computing and spectrum resource scheduling scheme to solve the above problem. The goal is to determine offloading decisions for dependent tasks by minimizing the completion delay and the energy consumption on computing nodes powered by non-green energy (i.e., non-green energy consumption). A priority-weight-based computing and spectrum resource scheduling (PW-CSRS) algorithm is proposed, which can sort the execution order of sub-tasks and optimize computing node selection and spectrum allocation. PW-CSRS is compared with three benchmarks in the 52-node and 33-node systems, and simulation results show that PW-CSRS can effectively improve the acceptance ratio and reduce non-green energy consumption.

Collaborative trajectory planning and transmit resource scheduling for multiple target tracking in distributed radar network system with GTAR

A reasonable resource management strategy can maximize the sensing performance of radar network system via adaptively controlling the working parameters of interest. In this paper, a collaborative trajectory planning and transmit resource scheduling (CTPTRS) strategy for multiple target tracking (MTT) in distributed radar network system with ground-based transmitters and airborne receivers (GTAR) is proposed. Firstly, the CTPTRS for the distributed radar network with GTAR (DRN-GTAR) is formulated as a non-convex optimization problem. To be more precise, the transmit power with the bandwidth of each transmitter and the trajectory of each airborne front-receiver are controlled jointly and adaptively to maximize the MTT performance. Secondly, to tackle the non-convex optimization problem, an efficient two-stage solution technique integrated with the semi-definite programming (SDP), the Latin hypercube sampling-based stochastic simulation (LHS) and the improved particle swarm optimization-tabu search (IPSOTS) is proposed. Finally, simulation results demonstrate the effectiveness of the proposed CTPTRS strategy. Furthermore, in terms of the MTT performance, the proposed strategy owns superior performance compared with six other existing baseline strategies.

A study on the resource scheduling strategy in substations with high penetration of photovoltaic electricity

Abstract In 2020, China introduced the strategic objectives of reaching the peak of carbon emissions and achieving carbon neutrality. However, due to the limited self-regulation capabilities of the power grid and increasingly prominent issues such as insufficient consumption of photovoltaic electricity generation, there are challenges. Therefore, in this paper, a coordinated interaction model of source-load-storage is established with the aim of optimizing the on-site utilization of photovoltaic electricity. Rolling scheduling of substation resources is conducted at three temporal scales: pre-scheduled, interim, and actual-time, and the effectiveness of the proposed approach is verified using actual case studies. The data suggests that compared with single-time-scale scheduling schemes, the on-site consumption rate of photovoltaic electricity increases by 0.32% in the case study substations, validating that the scheduling approach can improve the photovoltaic electricity consumption rate in the substations.

An integrated optimization method to task scheduling and VM placement for green datacenters

In the realm of cloud computing, effective resource allocation can significantly enhance the energy efficiency of datacenters. Task scheduling and Virtual Machine Placement (VMP) are two pivotal aspects of resource allocation. However, in current research, they are often treated separately, overlooking the potential for integrated optimization. In this paper, we propose an integrated solution for task scheduling and VMP in energy-efficient datacenters, based on queueing theory and Deep Reinforcement Learning (DRL) methods. This novel and comprehensive approach provides an alternative perspective for resource scheduling strategies in datacenters. We construct a queueing theory model for task scheduling, aiming to minimize the number of VMs that need to be instantiated, while ensuring that Service Level Agreement (SLA) violation remains at a low level. Furthermore, we design a VMP algorithm based on DRL for real-time selection of Physical Hosts (PHs) for deploying VMs. Finally, we conduct a simulation evaluation using a small-scale datacenter. The experimental results demonstrate that our method consistently ensures a lower rate of SLA violation. Compared to existing algorithms, the DRL-based VMP algorithm enables a more balanced utilization of the various resources in the PHs and reduces the total power consumption of the datacenter by more than 10% on average.

Distributed collaborative scheduling technology for random access in real-time

The planning and deployment of mega constellations has posed increasing pressure on the existing telemetry, track and command (TTC) resource scheduling strategy. The current plan-based scheduling strategy distributes resources according to needs, however, it cannot address sudden resource requests with prompt responses. By analyzing the status quo of resource scheduling systems, we have proposed a distributed collaborative scheduling technology for random access in real-time, based on the theory of low costs and rapid transfers of scheduling task resources. First, we use knowledge graph technology to map the link between scheduling tasks and idle resources and develop a graph network. Furthermore, by analyzing and evaluating the resource transfer capability of a multi-target TTC system in hemispherical coverage (MT-TTC), we propose the concept of a virtual resource pool and develop a two-layer scheduling framework in which the central scheduling system coordinates with multiple regional systems. This scheduling strategy works well when a sudden TTC request occurs. The central system manages the satellite requests and make plans, while regional systems are responsible for handling the sudden requests and report to the central system on its resource change. With regional systems sharing some degree of autonomy, the central system making a final decision and systems across the framework coordinating in real-time, we design a model on resource transfers for MT-TTC equipment and a collaboration mechanism that coordinates the resource transfers across MT-TTC equipment and other single-beam equipment within and between regions. We do experiments in simulation scheduling environments. The results show that the regional scheduling system based on MT-TTC equipment is able to manage continuous random-access requests in real-time, and avoid resource conflicts across regions by collaborating with the central scheduling system. It is of value to be applied in real scenarios.

An ICN-Based On-Path Computing Resource Scheduling Architecture with User Preference Awareness for Computing Network

The Computing Network is an emerging network paradigm that aims to realize computing resource scheduling through the intrinsic capabilities of the network. However, existing resource scheduling architectures based on conventional TCP/IP networks for the Computing Network suffer from deficiencies in routing flexibility and a lack of user preference awareness, while Information-Centric Networking (ICN) holds the potential to address these issues. ICN inherently supports dynamic routing in scenarios such as multi-homing and mobility due to its routing mechanism that is based on content names rather than host addresses, and it is further enhanced by the integration with Software-Defined Networking (SDN) technologies, which facilitate convenient network-layer route readdressing, thus offering a conducive environment for flexible routing scheduling. Furthermore, ICN introduces novel routing protocols that, compared with the more rigid protocol designs in conventional TCP/IP networks, offer greater flexibility in field usage. This flexibility allows for the incorporation of customized fields, such as “preference”, enabling the perception of user preferences within the network. Therefore, this paper proposes a novel ICN-based on-path computing resource scheduling architecture named IPCRSA. Within this architecture, an original design for computing resource request packet format is developed based on the IPv6 extension header. Additionally, preference-based computing resource scheduling strategies are incorporated, which employ the technique for order preference by similarity to ideal solution (TOPSIS) combined with the entropy weight method, to comprehensively evaluate computing resource nodes and use a roulette-selection algorithm to accomplish the probability selection of destination nodes. Experimental results indicate that, in comparison to alternative scheduling schemes, IPCRSA exhibits significant advantages in enhancing scheduling flexibility, improving scheduling success rates, and catering to diverse user requirements.

An improved resource scheduling strategy through concatenated deep learning model for edge computing IoT networks

SummaryWith increasing challenges and research in edge‐assisted IoT models, an improved resource scheduling approach exploiting deep learning concepts is proposed in this research work. Improvement in performance in the proposed work is achieved primarily by addressing the response time and waiting time. This could be achieved if the optimal resources are scheduled without any delay. The presented concatenated deep learning technique considers the time series IoT network source requirements and allocates optimal resources from the resource pool, considering resource availability, workload, and computation time. Two deep learning techniques, namely, CNN and GRU, are utilized for the concatenation process, while resource characteristics are considered as features that are extracted and classified to schedule optimal resources. Novelty in the proposed work is exhibited in the form of the concatenation process proposed. The proposed resource scheduling performance metrics are compared with the performance of the existing scheduling model through simulation analysis for better validation. The proposed model selects the optimal resources from the resource pool using concatenated features and schedules for respective requests with minimum delay and waiting time, which increases the overall efficiency of the edge computing IoT networks.

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.

The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning

Abstract Time-Sensitive Networking (TSN) occupies a vital position in modern communication domains, with the 802.1Qbv standard being an important network technology designed to meet real-time requirements. This standard requires network traffic to be transmitted within strict time windows, presenting challenges in network planning, necessitating efficient resource allocation and scheduling strategies. This paper addresses the 802.1Qbv planning problem through the introduction of reinforcement learning algorithms, offering an automated and intelligent solution. We have designed a reinforcement learning agent capable of perceiving network status, learning optimal resource allocation strategies, and dynamically adjusting in real-time environments. Through simulation and experimentation, we have validated the effectiveness of our proposed method, comparing it with traditional planning approaches. The contribution of this study lies in introducing a novel solution to the 802.1Qbv planning problem for time-sensitive networks, enhancing network resource utilization and performance. This approach offers strong support for the development and enhancement of TSN-like networks, holding significant importance for meeting the growing demands of real-time applications.

Research on 5G Network Slicing Algorithm Based on the Smart Grid

To address the problem of network-slicing resource allocation in the smart grid, this paper puts forward a hierarchical network-slicing resource scheduling strategy for the smart grid based on priority and network utility, which is a study of the 5G net slice resource allocation (the max algorithm of utility). It is divided into two layers, which are the user layer and the slice allocation layer. By adopting different resource allocation methods to better meet the service and the experience quality of power users in different slices, the simulation results playing the strategy are better than the other number of resource allocation schemes.

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.

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.

Performance Optimization for Energy-Efficient Industrial Internet of Things Based on Ambient Backscatter Communication: An A3C-FL Approach

The recent advent of the Industry 4.0 era has led to the need to transform the industrial Internet of Things towards green, low-carbon and sustainable development. This is due to the fact that traditional industries consume too much energy. It is urgent to make use of digital technology for energy saving and emission reduction. However, there are still some unresolved issues in the transformation process: 1) the inability to use equipment resources thoroughly and efficiently, 2) the waste caused by overly simple resource management. In this paper, based on the above issues, we develop the ambient backscatter system to optimize the overall resource scheduling scheme and combine intelligent algorithms to solve the problem of offloading tasks. The solution optimizes offloading decisions to minimize system energy consumption and latency. Meanwhile, the proposed optimization problem is designed as a Markov decision process by combining the proposed federated learning assigned with asynchronous advantage actor-critic algorithm to obtain the optimal policy. The final evaluation results significantly show that the system performance indicator based on our proposed solution is better than others.

PDPoSt+sPBFT: A High Performance Blockchain-Assisted Parallel Reinforcement Learning in Industrial Edge-Cloud Collaborative Network

With the increasing demand for resource scheduling efficiency in Industrial Internet of Things (IIoT), parallel reinforcement learning (PRL) based distributed edge-cloud collaborative resource scheduling scheme has attracted enormous attention. However, the computing and communication capacities, the security degree of massive distributed edge computing servers are different. It is difficult to make a large number of edge servers carry out security and efficiency PRL based edge-cloud collaboration resource scheduling scheme. Thus, in this paper, a large-scale distributed edge-cloud collaborative resource scheduling method based on picture delegated proof of state and suspicious practical byzantine fault tolerance (pDPoSt+sPBFT) consensus algorithm is proposed. To be specific, we first propose a collaborative edge-cloud industrial network architecture to support massive industrial intelligence tasks, then a distributed PRL based resource allocation scheme is utilized. Secondly, in order to improve the efficiency and security of distributed PRL training, we propose a server filtering strategy based on pDPoSt algorithm. Finally, a sPBFT algorithm is proposed to further realize security parameter aggregation of distributed PRL. Experimental results show that the proposed method has good efficiency and security performance compared with the traditional distributed edge-cloud collaborative resource scheduling algorithm. The proposed approach has great potential in complex IIoT scenarios.