Showing papers on "Fair-share scheduling published in 2022"

Deep reinforcement learning for dynamic scheduling of a flexible job shop

Abstract: The ability to handle unpredictable dynamic events is becoming more important in pursuing agile and flexible production scheduling. At the same time, the cyber-physical convergence in production system creates massive amounts of industrial data that needs to be mined and analysed in real-time. To facilitate such real-time control, this research proposes a hierarchical and distributed architecture to solve the dynamic flexible job shop scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agents, to capture the relationship between production information and scheduling objectives, and make real-time scheduling decisions for a flexible job shop with constant job arrivals. Specialised state and action representations are proposed to handle the variable specification of the problem in dynamic scheduling. Additionally, a surrogate reward-shaping technique to improve learning efficiency and scheduling effectiveness is developed. A simulation study is carried out to validate the performance of the proposed approach under different scenarios. Numerical results show that not only does the proposed approach deliver superior performance as compared to existing scheduling strategies, its advantages persist even if the manufacturing system configuration changes.

Hybrid energy-efficient scheduling measures for flexible job-shop problem with variable machining speeds

Abstract: Production scheduling has a significant impact on energy savings in manufacturing system from the viewpoint of operation management. Taking the flexibility of machining speeds taken into account, the flexible job-shop scheduling problem will get closer to the real manufacturing environment, and the associated energy consumption for shifting speeds should also be noticeable. In this paper, a novel energy-aware estimation model is proposed to compute different energy consumptions for different running conditions of a machine. Then, a multi-objective optimization model is formulated for the dual flexible job-shop scheduling problem to minimize the makespan and total energy consumption. Hybrid energy-efficient scheduling measures are developed to reduce each kind of energy consumption on the machines. Finally, experimental results show the consistency between optimization objectives and demonstrate superior performance of these energy-efficient scheduling measures.

A new median-average round Robin scheduling algorithm: An optimal approach for reducing turnaround and waiting time

Abstract: A variety of algorithms handles processes on the CPU. The round-robin algorithm is an efficient CPU scheduling mechanism for a time-sharing operating system. The system processes the methods based on the time slice; however, determining the time slice has proven highly challenging for the researchers. As a result, a variety of dynamic time quantum scheduling techniques are presented by various academics to address this challenge. This study aims to determine how to best schedule resources to maximize efficiency. It is important to note that this scheduling mechanism rotates between the processes after the static quantum time is complete. However, the quantum decision affects how effectively and efficiently the procedures may be scheduled. Additionally, the quantum decision has an impact on the scheduling of processes. The average waiting time, turnaround time, and context switch times of the Round Robin scheduling algorithm are high enough to influence the system's performance. To get over the round-drawbacks, robin's the authors in this study suggest using the improved algorithm Median-Average Round Robin (MARR). Using the median and average of the burst time of each process, the author proposes a dynamic time quantum for the system. The authors compared the proposed model with four other scheduling algorithms. The results vividly depict that the proposed algorithms successfully give effective results with reduced average turnaround time and waiting time. In the future, cost and RAM utilization will be considered to enhance the algorithm.

Dynamic scheduling method for integrated process planning and scheduling problem with machine fault

Abstract: • A pre-reactive scheduling method is designed to deal with the integrated process planning and scheduling problem with machine fault. • The process adjustment method based on jobs classification is proposed to improve the stability of rescheduling. • Two hybrid algorithms combining genetic algorithm and variable neighborhood algorithm are proposed to solve the initial scheduling scheme and the rescheduling scheme respectively. The integration of process planning and job shop scheduling is of great significance to improve the performance of manufacturing system. Many studies on integrating process planning and scheduling problem focused on static workshop environment. However, there are a lot of uncertain factors in the workshop that need to be dealt with. Therefore, this paper studies the dynamic scheduling method of dynamic integrated process planning and scheduling problem under machine fault. To solve the dynamic integrated process planning and scheduling problem, two hybrid algorithms combining genetic algorithm with neighborhood search algorithm are designed. To improve the stability of rescheduling scheme, according to the characteristics of sequencing flexibility, processing flexibility and machine flexibility of the integrated process planning and scheduling problem, a process adjustment method based on job classification is proposed. To dynamically adjust the diversity and convergence of population, an adaptive hierarchical migration strategy is proposed. To decode dynamic scheduling scheme, the greedy decoding method is improved. The designed dynamic scheduling method is applied to the famous benchmark to verify the effectiveness of the proposed method in solving the dynamic integrated process planning and scheduling problem with machine fault.

Optimized task scheduling for cost-latency trade-off in mobile fog computing using fuzzy analytical hierarchy process

Abstract: Mobile Fog Computing (MFC) paradigm can be integrated as a unit called as Multi-Access Edge Computing (MFC) in a fifth-generation (5G) network. There are extensive researches coercing to the MFC. Task scheduling is an important issue in the area of MFC to solve computing capacities such as limited CPU power, storage capacity, memory constraints, and limited battery life in Mobile Devices (MDs). The multi-criteria decision-making problem in fog nodes has not been widely studied. According to the variety and difficulty of criteria, the scheduling in the fog node has become a challenge. The previous works in the tasks scheduling context considered a few criteria of dynamic scheduling without covering other enough criteria. Besides, in MFC, the tasks come with different priorities. We present a scheduling algorithm based on the Priority Queue, Fuzzy and Analytical Hierarchy Process namely PQFAHP in our paper. We use PQFAHP to combine several priorities and prioritize multi-criteria. In our paper, dynamic scheduling includes the completion time, energy consumption, RAM, and deadline criteria. Our experimental results show that the proposed algorithm can consider multi-criteria for scheduling Our proposed work is one of the multi-criteria algorithms that performs optimal results than several benchmark algorithms in terms of waiting time, delay, service level, mean response time, and the number of scheduled tasks on the MFC side. This paper has considerable contributions related to the scheduling of fog computing. For instance, it could decrease 14.2%, 49%, and 26% in average waiting time, delay, and energy consumption respectively, and increase 10.8% in service level.

TC-Flow: Chain Flow Scheduling for Advanced Industrial Applications in Time-Sensitive Networks

Abstract: Time-sensitive networking (TSN) can help standardize deterministic Ethernet across industrial automation. The deterministic guarantee of TSN is based on network resource scheduling in the unit of flow. However, the state-of-the-art TSN single flow scheduling scheme cannot meet the coordinated scheduling requirements of multiple data flows in advanced industrial applications (e.g., control and safety applications). In this article, we propose a TSN chain flow abstraction, TC-Flow, for a coordinated multiple-flow scheduling model in industrial control and safety applications. Based on the proposed TC-Flow model, we design an offline TC-Flow scheduling algorithm using integer linear programming and an online heuristic TC-Flow scheduling algorithm to handle network dynamics. To deploy the proposed TC-Flow model and scheduling algorithms in the TSN, we design a CF-TSN network architecture that is compatible with the existing TSN single-flow scheduling scheme. Finally, we implement the proposed CF-TSN architecture and TC-Flow scheduling algorithms in real-world network environments. Experimental results show that the proposed scheduling algorithms can increase the number of schedulable flows by 26 percent compared to the state-of-the-art TSN scheduling benchmark.

Integrated Production and Transportation Scheduling Method in Hybrid Flow Shop

Abstract: Abstract The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem (IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem (HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles (AGVs) is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation (including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.

Efficient Flow Scheduling for Industrial Time-Sensitive Networking: A Divisibility Theory-Based Method

Abstract: As an emerging communication technology, time-sensitive networking (TSN) promises the real time and deterministic interaction of massive data in Industrial Internet of Things. However, it is challenging to schedule the time-sensitive flows timely and superiorly through the mechanism analysis for current TSN scheduling models, especially in complex industrial scenarios. In this article, we propose an analysis approach of flow sequences based on divisibility theory to characterize the flow conflicts and dependencies, which derives the scheduling flexibility based on flow position diversity (PD) and the equivalent flow judgment conditions for slot occupancy. Integrating the abovementioned derivation, a parallel computing framework with the generalized slot length is established to lower the scheduling complexity. Within each computing unit, an incremental scheduling algorithm with the flow judgment conditions and PD-based search boundary is proposed. It reduces the scheduling complexity further while maintaining load balance for the mixed transmission of periodic and aperiodic flows. To achieve the optimality of runtime and load balance, two PD-based flow sorting strategies are designed, respectively. The evaluation results show that compared with the existing works, the runtime efficiency of scheduling at scale is increased by at least 1500 times in complex traffic scenarios while the load balance on the network links is also improved.

Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems

Abstract: Due to poor predictability of resources and difficulty in perception of task execution status, traditional Automatic Guide Vehicle (AGV) scheduling systems need a lot of extra time in the charging process. To solve this problem, a digital twin-based dynamic AGV scheduling (DTDAS) method is proposed, including four functions, namely the knowledge support system, the scheduling model, the scheduling optimization, and the scheduling simulation. With the features of virtual reality data interaction, symbiosis, and fusion from the digital twin technology, the proposed DTDAS method can solve the AGV charging problem in the AGV scheduling system, effectively improving the operating efficiency of the workshop. An AGV scheduling process in a discrete manufacturing workshop is taken as a case study to verify the effectiveness of the proposed method. The results show that, compared with the traditional AGV scheduling method, the DTDAS method proposed in this article can reduce makespan 10.7% and reduce energy consumption by 1.32%.

A Survey of Low-Energy Parallel Scheduling Algorithms

Abstract: High energy consumption is one of the biggest obstacles to the rapid development of computing systems, and reducing energy consumption is quite urgent and necessary for sustainable computing. Low-energy scheduling based on dynamic voltage and frequency scaling (DVFS) is one of the most commonly used energy optimization techniques. Recent survey works have reviewed some low-energy scheduling algorithms, but there is currently no systematic review in low-energy parallel scheduling algorithms. With the increasing complexity of function requirements, many parallel applications have been executed in various sustainable computing systems. In this paper, we survey recent advances in low-energy parallel scheduling algorithms according to three scheduling styles, namely: 1) energy-efficient parallel scheduling algorithms; 2) energy-aware parallel scheduling algorithms; and 3) energy-conscious parallel scheduling algorithms. Low-energy parallel scheduling algorithms basically involve five categories of 1) heuristic algorithms; 2) meta-heuristic algorithms; 3) integer programming algorithms; 4) machine learning algorithms; and 5) game theory algorithms. Further, we introduce the future trends in low-energy parallel scheduling algorithms from the perspectives of new requirements and future developments. By surveying the recent advances and introducing the future trends, we expect to provide researchers with a systematic reference and development directions in low-energy parallel scheduling for sustainable computing systems.

Two-Phase Scheduling for Efficient Vehicle Sharing

Abstract: Cooperative Intelligent Transport Systems (C-ITS) is a promising technology to make transportation safer and more efficient. Ridesharing for long-distance is becoming a key means of transportation in C-ITS. In this paper, we focus on private long-distance ridesharing, which reduces the total cost of vehicle utilization for long-distance journeys. In this context, we investigate journey scheduling problem with shared vehicles to reduce the total cost of vehicle utilization. Most of the existing works directly schedule journeys to vehicles with long scheduling time and only consider the cost of driving travellers instead of the total cost. In contrast, to reduce the total cost and scheduling time, we propose a comprehensive cost model and a two-phase journey scheduling approach, which includes path generation and path scheduling. On this basis, we propose two path generation methods: a simple near optimal method and a reset near optimal method as well as a greedy based path scheduling method. Finally, we present an experimental evaluation with different path generation and path scheduling methods with synthetic data generated based on real-world data. The results reveal that the proposed scheduling approach significantly outperforms baseline methods in terms of total cost (up to 69.8%) and scheduling time (up to 84.0%) and the scheduling time is reasonable (up to 0.16s). The results also show that our approach has higher efficiency (up to 141.7%) than baseline methods.

Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops

Abstract: The traditional shop floor scheduling problem mainly focuses on the static environment, which is unrealistic in actual production. To solve this problem, this paper proposes a digital twin-driven shop floor adaptive scheduling method. Firstly, a digital twin model of the actual production line is established to monitor the operation of the actual production line in real time and provide a real-time data source for subsequent scheduling; secondly, to address the problem that the solution quality and efficiency of the traditional genetic algorithm cannot meet the actual production demand, the key parameters in the genetic algorithm are dynamically adjusted using a reinforcement learning enhanced genetic algorithm to improve the solution efficiency and quality. Finally, the digital twin system captures dynamic events and issues warnings when dynamic events occur in the actual production process, and adaptively optimizes the initial scheduling scheme. The effectiveness of the proposed method is verified through the construction of the digital twin system, extensive dynamic scheduling experiments, and validation in a laboratory environment. It achieves real-time monitoring of the scheduling environment, accurately captures abnormal events in the production process, and combines with the scheduling algorithm to effectively solve a key problem in smart manufacturing.

Deep reinforcement learning based scheduling within production plan in semiconductor fabrication

Abstract: In the semiconductor industry, efficient production planning and scheduling decisions are required to enhance the manufacturing productivity of a company as the system is complicated due to a re-entry characteristic and requires a long production lead time. Production planning is implemented before scheduling and is important for successful manufacturing operations. However, if scheduling at the operation level cannot execute the production plan, failures occur because of inconsistent decisions. Therefore, scheduling needs to fulfill the production plan to ensure realistic decision-making processes for the companies aiming for economic growth and global competitiveness. In this study, deep reinforcement learning (RL) is employed to deal with a scheduling process operating within the production plan. As the algorithm of the deep RL, Deep Q-network is conjugated, and a novel state, action, and reward are suggested to optimize the scheduling policy. As a result, the performance of the proposed deep RL method is in comparison with other dispatching rules, and the proposed method outperforms the other scheduling methods in diverse cases.

An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

Abstract: Abstract Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.

Control and communication scheduling co-design for networked control systems: a survey

Abstract: Communication and control are two intensively coupled aspects in a networked control system (NCS), the design of one may affect the quality or performance of the other. The co-design problem of control and communication scheduling for NCSs has attracted tremendous attention. This paper provides an overview of recent advances in control and scheduling co-design for networked control systems. First, a basic framework of control and scheduling co-design problem setup is established. Second, representative results and methodologies reported in the literature are reviewed and some in-depth analysis and discussions are given, along the lines of the scheduling schemes including static scheduling, dynamic scheduling, and random scheduling. Finally, some unsettled issues and trending topics in this direction are outlined for future research.

Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE

Abstract: In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning (RL), more specifically, the Q-learning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users (PUs), and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users (SUs), and they could be un-licensed subscribers that don't pay for their service, device-to-device communications, or sensors. Each user whether it’s a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost 90% utilization of the spectrum, and provided fair shares of the spectrum among users.

A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm

Abstract: Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling.

Hierarchical scheduling for multi-composite tasks in cloud manufacturing

Abstract: Cloud manufacturing (CMfg) is a new manufacturing mode formed by the integration of information technology and communication technology with manufacturing. As a core role in CMfg, the CMfg platform is responsible for decomposing a large number of tasks from demander and allocating them to available services. The scheduling requires comprehensive consideration of the relevance, complexity and dynamics of task and service. When the decomposable task is multi-composite, how to allocate the optimum services to multi-composite tasks is a tricky and important problem. To solve the issue, a hierarchical scheduling model for multi-composite tasks is proposed, which is divided into user-level scheduling and sublevel scheduling to reduce the scale and difficulty of scheduling. User-level scheduling achieves two-way matching between demander and provider based on various attributes. For the sublevel scheduling, an improved firefly genetic algorithm is created for multi-objective optimisation. A detailed analysis of the hierarchical scheduling strategy is performed by testing several different instances. Experimental results indicate that this strategy reduces the complexity than collective scheduling; and has a better comprehensive balance effect on multiple optimisation goals than sequential scheduling.

An online algorithm for scheduling big data analysis jobs in cloud environments

Abstract: Cloud computing has become a popular platform for processing big data analysis jobs with its advantages of high-availability, elasticity and cost-efficiency. Many big data analysis service providers use cloud instances to process users’ big data analysis job execution requests and they need efficient scheduling algorithms to improve job execution efficiency and economic benefits. This paper presents a problem of minimizing the execution time of a batch of big data analysis jobs without changing the number of cloud instances. Solving this problem can not only improve big data job execution efficiency in cloud environments and user satisfaction, but also bring higher economic benefits to big data analysis service providers. This paper proposes an online scheduling algorithm, which can make full use of the parallelism of big data analysis jobs to optimize job scheduling decisions on the premise that the job execution time cannot be accurately known. For evaluating the performance of the proposed online scheduling algorithm, a traditional two-phase scheduling algorithm is introduced as a benchmark for comparison in this paper. Theoretical analysis and extensive simulation experiments based on real datasets show that the online scheduling algorithm proposed in this paper can achieve more stable performance compared with the benchmark two-phase scheduling algorithm.

VMAgent: A Practical Virtual Machine Scheduling Platform

Abstract: Virtual machine (VM) scheduling is one of the critical tasks in cloud computing. Many works have attempted to incorporate machine learning, especially reinforcement learning, to empower VM scheduling procedures. Although improved results are shown in several demo simulators, the performances in real-world scenarios are still underexploited. In this paper, we design a practical VM scheduling platform, i.e., VMAgent, to assist researchers in developing their methods on the VM scheduling problem. VMAgent consists of three components: simulator, scheduler, and visualizer. The simulator abstracts three general realistic scheduling scenarios (fading, recovering, and expansion) based on Huawei Cloud’s scheduling data, which is the core of our platform. Flexible configurations are further provided to make the simulator compatible with practical cloud computing architecture (i.e., Multi Non-Uniform Memory Access) and scenarios. Researchers then need to instantiate the scheduler to interact with the simulator, which is also pre-built in various types (e.g., heuristic, machine learning, and operations research) of scheduling algorithms to speed up the algorithm design. The visualizer, as an auxiliary component of the simulator and scheduler, facilitates researchers to conduct an in-depth analysis of the scheduling procedure and comprehensively compare different scheduling algorithms. We believe that VMAgent would shed light on the AI for the VM scheduling community, and the demo video is presented in https://bit.ly/vmagent-demo-video.

Optimization of Apron Support Vehicle Operation Scheduling Based on Multi-Layer Coding Genetic Algorithm

Abstract: Operation scheduling of apron support vehicles is an important factor affecting aircraft support capability. However, at present, the traditional support methods have the problems of low utilization rate of support vehicles and low support efficiency in multi-aircraft support. In this paper, a vehicle scheduling model is constructed, and a multi-layer coding genetic algorithm is designed to solve the vehicle scheduling problem. In this paper, the apron support vehicle operation scheduling problem is regarded as a Resource-Constrained Project Scheduling Problem (RCPSP), and the support vehicles and their support procedures are adjusted via the sequential sorting method to achieve the optimization goals of shortening the support time and improving the vehicle utilization rate. Based on a specific example, the job scheduling before and after the optimization of the number of support vehicles is simulated using a multi-layer coding genetic algorithm. The results show that compared with the traditional support scheme, the vehicle scheduling time optimized via the multi-layer coding genetic algorithm is obviously shortened; after the number of vehicles is optimized, the support time is further shortened and the average utilization rate of vehicles is improved. Finally, the optimized apron support vehicle number configuration and the best scheduling scheme are given.