Online Scheduling Method Research Articles

Real-time adaptive scheduling optimization for inter-satellite contact window resources in dynamic satellite networks

Real-time scheduling of contact window resources is a fundamental technology for the future construction of Space–Terrestrial Integrated Networks (STIN). The spatio-temporal dynamics of satellite networks result in continuous variations in satellite relative positions and inter-satellite visibility window resource status. Furthermore, frequent changes in task priority make real-time resource scheduling extremely challenging. Centralized online scheduling methods often lead to a significant amount of waiting time, while distributed online scheduling methods struggle to adapt to the high dynamics of satellite networks. Therefore, the real-time scheduling technology for contact window resources in satellite networks is still in the exploration phase. We propose an inter-satellite online resource allocation method to solve the latency issue caused by the seizure of contact window resources. First, we determine the shortest and maximum flow paths set in the satellite network according to task requirements and construct them as the core network. Then, we comprehensively analyze the supply and demand situation of the task and the core network at a specific moment and verify the reliability of our minimum cost flow-solving method based on the core network. Moreover, we consider factors such as changes in task priority and edge modifications and validate the correctness of our method to update the minimum cost flow at the next moment based on local network changes. Finally, through extensive comparative simulations, we evaluate the absolute advantage of our inter-satellite online scheduling method and validate a significant improvement in task scheduling timeliness.

Online Optimization of Vehicle-to-Grid Scheduling to Mitigate Battery Aging

The penetration of electric vehicles (EVs) in vehicle-to-grid (V2G) interaction can effectively assist the grid in achieving frequency regulation and peak load balancing. However, the customer perceives that participating in V2G services would result in the additional cycling of the battery and the accelerated aging of the EVs’ power battery, which has become a major obstacle to the widespread adoption of V2G services. Most existing methods require long-term cycling data and battery parameters to quantify battery aging, which is not suitable for the V2G scenario with large-scale and short-time intervals. Consequently, the real-time and accurate quantification of battery aging for optimization remains a challenge. This study proposes a charging scheduling method for EVs that can accurately and online quantify battery aging. Firstly, V2G scheduling is formulated as an optimization problem by defining an online sliding window to collect real-time vehicle information on the grid, enabling online optimization. Secondly, battery aging is more accurately quantified by proposing a novel amplitude-based rain-flow cycle counting (MRCC) method, which utilizes the charging information of the battery within a shorter time period. Lastly, an intelligent optimization algorithm is employed to optimize the charging and discharging power of EVs, aiming to minimize grid fluctuations and battery aging. The proposed method is validated using a V2G scenario with 50 EVs with randomly generated behaviors, and the results demonstrate that the proposed online scheduling method not only reduces the EFCC of the battery by 8.4%, but also achieves results close to global optimization.

An online method for robust target tracking using a wireless sensor network

This work addresses the target tracking problem using a wireless sensor network (WSN). A set of sensors is randomly deployed to continuously monitor the moving targets, with their spatial trajectories already known but with estimated temporal trajectories and thus, possible delays and advances. The network has to transfer the data collected by the sensors to a base station, with hop-communication between the sensors. Previous works focused on the computation of a robust activation schedule which is passively tracking the targets (i.e., with no reaction to the actual behavior of the targets) by just activating sensors according to the pre-computed schedule. It has a maximized stability radius which is the guarantee on the maximal delay or advance of the targets it can cover, however it loses some targets whenever a delay or advance is beyond the value of its stability radius. Consequently, it is observed in many instances that a target is lost despite being in the range of a fully functioning WSN. A major novelty of the present work is to propose an online method that actively reacts to the delays and advances of the targets to extend the coverage offered by the robust schedule. The proposed method both detects the delays and advances beyond the stability radius, and constantly modifies the schedule to cover the targets as long as possible, at a computational cost that remains acceptable in a real-life application. It lets the WSN monitor the targets despite delays and advances that may not be covered by the guarantee of the stability radius. The second major novelty of our work is the introduction of the dynamic stability radius; it is a guarantee on the greatest delay or advance that can be covered by an online scheduling method. It is, by definition, greater than or equal to the classical stability radius but it is dynamically recomputed such that this guarantee increases over time. Our online method is dynamically optimizing the dynamic stability radius, meaning that each online decision is made so that the WSN has increasing capacities to cover the targets over time. Numerical experiments show that the online method proposed in this work offers more robustness than the previous works, with a dynamic stability radius that improves significantly over the stability radius from previous works, and that increases over time. Experiments also highlight the possibility to use such a method in a real-time context because of its fast computation time.

Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice

The power distribution network business gradually extends from the grid domain to the social service domain, and the new business keeps expanding. The edge device uses microservice architecture and container technology to realize the processing of different services by one physical device. Although the power distribution network IoT with cloud-edge architecture has good scalability, scenarios with insufficient resources for edge devices may occur. In order to support the scheduling and collaborative processing of tasks under the resource-constrained scenario from the edge device, this paper proposes a cloud-edge collaborative online scheduling method for the distribution of station area tasks under the microservice architecture. The article models the characteristics of power tasks and their constraints in the cloud-edge containerized scenario, designs the priority policy and task assignment policy based on the cloud-edge scheduling mechanism of containerized power tasks, and schedules the tasks in real time by an improved online algorithm. Simulation results show that the algorithm proposed in this paper has high task execution efficiency, can improve the completion rate of important tasks with limited resources of edge devices, and improves system security through resource replacement.

A Stochastic Programming Model for Service Scheduling with Uncertain Demand: an Application in Open-Access Clinic Scheduling

This paper addressed a scheduling problem which handles urgent tasks along with existing schedules. The uncertainties in this problem come from random process of existing schedules and unknown upcoming urgent tasks. To deal with the uncertainties, this paper proposes a stochastic integer programming (SIP) based aggregated online scheduling method. The method is illustrated through a study case from the outpatient clinic block-wise scheduling system which is under a hybrid scheduling policy combining regular far-in-advance policy and the open-access policy. The COVID-19 pandemic brings more challenges for the healthcare system including the fluctuations of service time and increasing urgent requests which this paper is designed for. The schedule framework designed in the method is comprehensive to accommodate various uncertainties in the healthcare service system, such as: no-shows, cancellations and punctuality of patients as well as preference of patients over time slots and physicians.Supplementary InformationThe online version contains supplementary material available at 10.1007/s43069-021-00089-6.

Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning

The proliferation of distributed renewable energy resources (RESs) poses major challenges to the operation of microgrids due to uncertainty. Traditional online scheduling approaches relying on accurate forecasts become difficult to implement due to the increase of uncertain RESs. Although several data-driven methods have been proposed recently to overcome the challenge, they generally suffer from a scalability issue due to the limited ability to optimize high-dimensional continuous control variables. To address these issues, we propose a data-driven online scheduling method for microgrid energy optimization based on continuous-control deep reinforcement learning (DRL). We formulate the online scheduling problem as a Markov decision process (MDP). The objective is to minimize the operating cost of the microgrid considering the uncertainty of RESs generation, load demand, and electricity prices. To learn the optimal scheduling strategy, a Gated Recurrent Unit (GRU)-based network is designed to extract temporal features of uncertainty and generate the optimal scheduling decisions in an end-to-end manner. To optimize the policy with high-dimensional and continuous actions, proximal policy optimization (PPO) is employed to train the neural network-based policy in a data-driven fashion. The proposed method does not require any forecasting information on the uncertainty or a prior knowledge of the physical model of the microgrid. Simulation results using realistic power system data of California Independent System Operator (CAISO) demonstrate the effectiveness of the proposed method.

Secure and Efficient Content Distribution in Crowdsourced Vehicular Content-Centric Networking

In future intelligent transportation systems, a large amount of content needs to be efficiently and securely exchanged between vehicles and roadside units via vehicular networks to improve the driving and traveling experience. To solve the challenges caused by poor-quality wireless links and the mobility of vehicles, vehicular content-centric networking (VCCN) emerges as a promising paradigm, which has a better content distribution efficiency, mobility, and security via named data and in-networking caching compared with an IP-based network. However, providing a high-quality experience for content distribution in VCCN is challenging due to the dynamic network topologies, varying wireless channel conditions, and vehicle user privacy. In this paper, we propose a novel crowdsourced VCCN framework for secure and efficient content distribution. This framework enables the nearby vehicles to crowdsource their caching resources and radio links for cooperative content distribution. We formulate the problem as the maximization of all users’ payoff and propose an online scheduling method to solve this solution. Furthermore, we adopt identity-based proxy reencryption and named function networking to secure the process of content distribution. The simulation results show that our proposals improve the performance of VCCN in terms of average requester utility compared with original CCN forwarding strategies.

From rescheduling to online scheduling

We first review advances in rescheduling, traditionally viewed as an approach to tackle uncertainty, including methods that rely on recourse through feedback as well as methods that account for uncertainty a priori. Then, we show that traditional event-triggered rescheduling has some shortcomings which can be addressed if rescheduling is approached as an online problem. We review methods that consider aspects of this online problem and define notation and some key features of this problem. Furthermore, we propose a broad framework for the classification of online scheduling methods. Finally, we discuss a number of open research questions, including the generation of high quality of closed-loop (implemented) schedules through the selection of appropriate model, horizon length, time-step, objective function modifications, and constraint addition.

A Markov Decision Model for Adaptive Scheduling of Stored Scalable Videos

We propose two scheduling algorithms that seek to optimize the quality of scalably coded videos that have been stored at a video server before transmission. The first scheduling algorithm is derived from a Markov decision process (MDP) formulation developed here. We model the dynamics of the channel as a Markov chain and reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite-state space. Based on the MDP formulation, a near-optimal scheduling policy is computed that minimizes the mean square error. Using insights taken from the development of the optimal MDP-based scheduling policy, the second proposed scheduling algorithm is an online scheduling method that only requires easily measurable knowledge of the channel dynamics, and is thus viable in practice. Simulation results show that the performance of both scheduling algorithms is close to a performance upper bound also derived in this paper.

Dual representation and its online scheduling method for event-varying DESs with capacity constraints

This research focuses on event varying discrete event systems with capacity and order constraints, and derives two state-space representations that stand for the earliest and latest times of event occurrences. Moreover, we consider rescheduling methods for these representations that can be applied even when the relevant parameters are changed after the commencement of the job. The two state-space representations derived have forms that are similar to dual systems in modern control theory. In online scheduling, it is often essential to keep track of state changes or the float times of jobs continuously. For the calculation of the float times, both earliest and latest times must be calculated. Hence, the derived representations are an attractive tool for solving online scheduling problems.

Online Scheduling of Integrated Single-Wafer Processing Tools With Temporal Constraints

This paper addresses the issues of online scheduling for integrated single-wafer processing tools with temporal constraints. The integrated single-wafer processing tool is an integrated processing system consisting of single-wafer processing modules and transfer modules. Certain chemical processes require that the wafer flow satisfies temporal constraints, especially, postprocessing residency constraints. This paper proposes an online scheduling method that guarantees both logical and temporal correctness for the integrated single-wafer processing tools. First, mathematical formulation of the scheduling problem using temporal constraint sets is presented. Then, an online, noncyclic scheduling algorithm with polynomial complexity is developed. The proposed scheduling algorithm consists of two subalgorithms: FEASIBLE/spl I.bar/SCHED/spl I.bar/SPACE and OPTIMAL/spl I.bar/SCHED. The former computes the feasible solution space in the continuous time domain, and the latter computes the optimal solution that minimizes the completion time of the last operation of a newly inserted wafer.

Online Scheduling Aiming to Satisfy Due Date for Flexible Flow Shops

The simplest method for making a production schedule is dispatch of jobs to idle machine tools. The method has a tendency of making a compact and efficient schedule, however the completion time of a job is not determined till the job is selected as the next job on an idle machine. Then, online scheduling methods have been developed, in which the production schedule is made as a job arrives at a machine. In this paper, an online scheduling method is developed for flexible flow shops under the due-date related criteria. A special feature of the method is to set the target completion time for each job and each stage. By numerical experiments, it is clarified that the method is better than FIFO (First In First Out) rule and especially the performance of the online scheduling is greatly superior to FIFO for the shops with heavy workload.

Providing Quality-of-Protection Classes Through Control-Message Scheduling in DWDM Mesh Networks With Capacity Sharing

This paper considers the problem of providing quality-of-protection (QoP) classes and improving the failure-recovery time performance in dense wavelength-division-multiplexing (DWDM) networks that use mesh-based path restoration schemes with capacity sharing. We focus on the signaling process required for reconfiguring the nodes along the preplanned restoration paths, and propose a novel approach for reducing the restoration time and meeting the QoP requirements by coordinating the setup procedures for the backup paths through scheduling. We present priority-based online scheduling algorithms that are amenable to distributed implementation for the problems of: 1) minimizing the worst case restoration time and 2) maximizing the number of connections that meet their QoP-class-specific restoration time deadlines. We also present mixed-integer-linear-program (MILP) formulations for both problems for comparison purposes. The online scheduling methods that we propose use simple connection and/or class-specific information and can be easily implemented with minor modifications to the currently proposed signaling protocols. We apply these methods to signaling protocols that require cross-connect configurations at different nodes to be done in sequence as in the current generalized multiprotocol label switching specification, as well as signaling protocols that allow cross-connect configurations to be done in parallel. It is shown that in both cases, significant performance improvements are achievable through scheduling in terms of both the QoP grades that can be supported and the restoration times, with both the MILP solutions and the heuristics. The improvement in restoration time and restorability through our heuristics can be quite high (e.g., increase from a network restorability performance of 40% to a network restorability of 88%, and a 12% reduction in worst case restoration time).

On-Line Scheduling Method for Track Systems in Semiconductor Fabrication

This paper addresses an on-line scheduling method for track systems in semiconductor fabrication. A track system is a clustered equipment performing photolithography process in semiconductor fabrication. Trends toward high automation and flexibility in the track systems accelerate the necessity of the intelligent controller that can guarantee reliability and optimize productivity of the track systems. This paper proposes an-efficient on-line scheduling method that can avoid deadlock inherent to track systems and optimize the productivity. We employ two procedures for the on-line scheduling. First, we define potential deadlock set to apply deadlock avoidance policy efficiently. After introducing the potential deadlock set, we propose a deadlock avoidance policy using an on-line Gantt chart, which can generate optimal near-optimal schedule without deadlock. The proposed on-line scheduling method is shown to be efficient in handling deadlock inherent to the track systems through simulation.

Rescheduling frequency in an FMS with uncertain processing times and unreliable machines

Abstract This paper studies the scheduling/rescheduling problem in a multi-resource FMS environment. Several reactive scheduling policies are proposed to address the effects of machine breakdowns and processing time variations. Both off-line and on-line scheduling methods are tested under a variety of experimental conditions. The performance of the system is measured for mean tardiness and makespan criteria. The relationships between scheduling frequency and other scheduling factors are investigated. The results indicated that a periodic response with an appropriate period length would be sufficient to cope with interruptions. It was also observed that machine breakdowns have more significant impact on the system performance than processing time variations. In addition, dispatching rules were found to be more robust to interruptions than the optimum-seeking off-line scheduling algorithm. A comprehensive bibliography is also included in the paper.