scheduling

摘要： Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.

摘要： Virtual cloud network(VCN)usage is popular today among large and small organizations due to its safety and money-saving.Moreover,it makes all resources in the company work as one unit.VCN also facilitates sharing of files and applications without effort.However,cloud providers face many issues in managing the VCN on cloud computing including these issues:Power consumption,network failures,and data availability.These issues often occur due to overloaded and unbalanced load tasks.In this paper,we propose a new automatic system to manage VCN for executing the workflow.The new system calledMulti-User Hybrid Scheduling(MUSH)can solve running issues and save power during workflow execution.It consists of three phases:Initialization,virtual machine allocation,and task scheduling algorithms.The MUSH system focuses on the execution of the workflow with deadline constraints.Moreover,it considers the utilization of virtual machines.The new system can save makespan and increase the throughput of the execution operation.

摘要： Cloud computing has gained widespread popularity over the last decade.Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users.Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms.This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm(NAGCSA)to address the scheduling issue in cloud computing.Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation.The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient,while the global search is performed by using levy flights.The amalgamation of NAG and CSA helps in cost reduction and time-saving for users.The simulation has been carried out on the CloudSim tool on three different real datasets;NASA,HPC2N,and SDSC.The results of the proposed hybrid algorithm have been compared with state-of-art scheduling algorithms(GA,PSO,and CSA),and statistical significance is carried on mean,standard deviation,and best for each algorithm.It has been established that the proposed algorithm minimizes the execution cost and makespan,hence enhancing the quality of service for users.

摘要： Planning and scheduling is one of the most important activity in supply chain operation management.Over the years,there have been multiple researches regarding planning and scheduling which are applied to improve a variety of supply chains.This includes two commonly used methods which are mathematical programming models and heuristics algorithms.Flowshop manufacturing systems are seen normally in industrial environments but few have considered certain constraints such as transportation capacity and transportation time within their supply chain.A two-stage flowshop of a single processing machine and a batch processing machine are considered with their capacity and transportation time between twomachines.The objectives of this research are to build a suitable mathematical model capable of minimizing the maximum completion time,to propose a heuristic optimization algorithm to solve the problem,and to develop an applicable program of the heuristics algorithm.AMixed Integer Programming(MIP)model and a heuristics optimization algorithmwas developed and tested using a randomly generated data set for feasibility.The overall results and performance of each approach was compared between the two methods that would assist the decision maker in choosing a suitable solution for their manufacturing line.

摘要： This article presents a mathematical model for the medium-term scheduling of the operating states of electric power systems.The scheduling period is divided into several time intervals.The model can be used to determine the equilibrium state in which each supplier earns maximum profit from supplying electricity to the wholesale market.We estimated the maximum value of public welfare,which indicates the total financial gains of suppliers and consumers,to determine the prices at the nodes of the power system.This was done by considering the balance constraints at the nodes of the power system and constraints on the allowable values of generation,power flows,and volumes of energy resources consumed over several time intervals.This problem belongs to the class of bi-level Stackelberg game-theoretic models with several leaders.The market equilibrium is modeled simultaneously in several intervals,given the multiplicity and duration of interactions.We considered two approaches for solving the multi-interval equilibrium state problem.The first approach involved directly solving a system of joint optimality conditions for electricity suppliers and consumers.The second approach involved iterative searches until the equilibrium state was reached.This article presents the results of medium-term scheduling using a case study of a simplified real-world power system.

摘要： Graph convolutional networks(GCNs) have been applied successfully in social networks and recommendation systems to analyze graph data. Unlike conventional neural networks, GCNs introduce an aggregation phase, which is both computation-and memory-intensive. This phase aggregates features from the neighboring vertices in the graph, which incurs significant amounts of irregular data and memory access.The emerging computation-in-memory(CIM) architecture presents a promising solution to alleviate the problem of irregular accesses and provide fast near-data processing for GCN applications by integrating both three-dimensional stacked CIM and general-purpose processing units in the system. This paper presents Graph-CIM, which exploits the hybrid CIM architecture to determine the allocation of GCN applications.Graph-CIM models the GCN application process as a directed acyclic graph(DAG) and allocates tasks on the hybrid CIM architecture. It achieves fine-grained graph partitioning to capture the irregular characteristics of the aggregation phase of GCN applications. We use a set of representative GCN models and standard graph datasets to evaluate the effectiveness of Graph-CIM. The experimental results show that Graph-CIM can significantly reduce the processing latency and data-movement overhead compared with the representative schemes.

摘要： This paper presents a new,bi-criteria mixed_integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system.The objective of this model is to minimize the makespan and intercell movements simultaneously,while considering sequence-dependent cell setup times.In the cellular manufacturing systems design and planning,three main steps must be considered,namely cell formation(i.e,piece families and machine grouping),inter and intra-cell layouts,and scheduling issue.Due to the fact that the cellular manufacturing systems problem is NP-Hard,a genetic algorithm as an efficient meta-heuristic method is proposed to solve such a hard problem.Finally,a number of test problems are solved to show the efficiency of the proposed genetic algorithm and the related computational results are compared with the results obtained by the use of an optimization tool.

摘要： Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles.However,lithium-ion cells generate immense heat at high-current charging rates.In order to address this problem,an efficient fast charging–cooling scheduling method is urgently needed.In this study,a liquid cooling-based thermal management system equipped with mini-channels was designed for the fastcharging process of a lithium-ion battery module.A neural network-based regression model was proposed based on 81 sets of experimental data,which consisted of three sub-models and considered three outputs:maximum temperature,temperature standard deviation,and energy consumption.Each sub-model had a desirable testing accuracy(99.353%,97.332%,and 98.381%)after training.The regression model was employed to predict all three outputs among a full dataset,which combined different charging current rates(0.5C,1C,1.5C,2C,and 2.5C(1C=5 A))at three different charging stages,and a range of coolant rates(0.0006,0.0012,and 0.0018 kg·s^(-1)).An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments.The results indicated that the battery module’s state of charge value increased by 0.5 after 15 min,with an energy consumption lower than 0.02 J.The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8C,respectively.The approach described herein can be used by the electric vehicles industry in real fast-charging conditions.Moreover,optimal fast charging-cooling schedule can be predicted based on the experimental data obtained,that in turn,can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.

摘要： Recently,the number of Internet of Things(IoT)devices connected to the Internet has increased dramatically as well as the data produced by these devices.This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing.However,different service architecture and offloading strategies have a different impact on the service time performance of IoT applications.Therefore,this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the network.Also,it introduces the offloading latency models to investigate the delay of different offloading scenarios/schemes and explores the effect of computational and communication demand on each one.A series of experiments conducted on an EdgeCloudSim show that different offloading decisions within the Edge-Cloud system can lead to various service times due to the computational resources and communications types.Finally,this paper presents a comprehensive review of the current state-of-the-art research on task offloading issues in the Edge-Cloud environment.

摘要： The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically.The generated schedule directs the project to be completed with the shortest critical path,at the minimum cost,while maintaining its quality.There are several real-world business constraints related to human resources,the similarity of the tasks added to the optimization model,and the literature’s traditional rules.To support the decision-maker to evaluate different decision strategies,we use compromise programming to transform multiobjective optimization(MOP)into a single-objective problem.We designed a genetic algorithm scheme to solve the transformed problem.The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents’fitness function.The optimizer can effectively find compromise solutions even if the user may or may not assign a priority to particular objectives.These are achieved through a combination of nonpreference and preference approaches.The experimental results show that the proposed method worked well on the tested dataset.

摘要： Scheduling is an important step in high-level synthesis and can greatly influence the testability of the synthesized circuits. This paper presents an efficient testability-improved data path scheduling scheme based on mobility scheduling, in which the scheduling begins from the operation with least mobility. In our data path scheduling scheme, the lifetimes of the I/O variables are made as short as possible to enlarge the possibility of the intermediate variables being allocated to the I/O registers. In this way, the controllability/observability of the intermediate variables can be improved. Combined with a weighted graph-based register allocation method, this scheme can obtain better testability. Experimental results on some benchmarks and example circuits show that the proposed scheme can get higher fault coverage compared with other scheduling schemes at little area overhead and even less time delay.

摘要： For WSN node applications in which the reliable operations with limited resources are required,a scheme of Dual Priority Scheduling with Preemption Avoidance(DPS-PA)is proposed.The scheme approached the event driven which has smaller code size and more rapid response time than ordinary multi-threading scheme.Also there is relatively a lager profit for power management.In this scheme,the MAC and Network protocol,devices access and applications must be simultaneously executed.It schedules tasks of two types that are periodic tasks and sporadic tasks.Each task has a piece of simple atomic code suchlike interrupt service routine.In DPS-PA scheme,it is difficult to make those complicated software modules with task level programming like multi-threading scheme.In this paper, we propose a new program design model for the DPS-PA.It has the hierarchical model based on the software block and the execution model based on state machine.

摘要： For WSN node applications in which the reliable operations with limited resources are required,a scheme of Dual Priority Scheduling with Preemption Avoidance(DPS-PA)is proposed.The scheme approached the event driven which has smaller code size and more rapid response time than ordinary multi-threading scheme.Also there is relatively a lager profit for power management.In this scheme,the MAC and Network protocol,devices access and applications must be simultaneously executed.It schedules tasks of two types that are periodic tasks and sporadic tasks.Each task has a piece of simple atomic code suchlike interrupt service routine.In DPS-PA scheme,it is difficult to make those complicated software modules with task level programming like multi-threading scheme.In this paper, we propose a new program design model for the DPS-PA.It has the hierarchical model based on the software block and the execution model based on state machine.

摘要： For WSN node applications in which the reliable operations with limited resources are required,a scheme of Dual Priority Scheduling with Preemption Avoidance(DPS-PA)is proposed.The scheme approached the event driven which has smaller code size and more rapid response time than ordinary multi-threading scheme.Also there is relatively a lager profit for power management.In this scheme,the MAC and Network protocol,devices access and applications must be simultaneously executed.It schedules tasks of two types that are periodic tasks and sporadic tasks.Each task has a piece of simple atomic code suchlike interrupt service routine.In DPS-PA scheme,it is difficult to make those complicated software modules with task level programming like multi-threading scheme.In this paper, we propose a new program design model for the DPS-PA.It has the hierarchical model based on the software block and the execution model based on state machine.

摘要： For WSN node applications in which the reliable operations with limited resources are required,a scheme of Dual Priority Scheduling with Preemption Avoidance(DPS-PA)is proposed.The scheme approached the event driven which has smaller code size and more rapid response time than ordinary multi-threading scheme.Also there is relatively a lager profit for power management.In this scheme,the MAC and Network protocol,devices access and applications must be simultaneously executed.It schedules tasks of two types that are periodic tasks and sporadic tasks.Each task has a piece of simple atomic code suchlike interrupt service routine.In DPS-PA scheme,it is difficult to make those complicated software modules with task level programming like multi-threading scheme.In this paper, we propose a new program design model for the DPS-PA.It has the hierarchical model based on the software block and the execution model based on state machine.

摘要： For WSN node applications in which the reliable operations with limited resources are required,a scheme of Dual Priority Scheduling with Preemption Avoidance(DPS-PA)is proposed.The scheme approached the event driven which has smaller code size and more rapid response time than ordinary multi-threading scheme.Also there is relatively a lager profit for power management.In this scheme,the MAC and Network protocol,devices access and applications must be simultaneously executed.It schedules tasks of two types that are periodic tasks and sporadic tasks.Each task has a piece of simple atomic code suchlike interrupt service routine.In DPS-PA scheme,it is difficult to make those complicated software modules with task level programming like multi-threading scheme.In this paper, we propose a new program design model for the DPS-PA.It has the hierarchical model based on the software block and the execution model based on state machine.

摘要： 本发明涉及一种基于Scheduler和IP核的免疫层析定量检测系统，包括免疫层析试条信号采集装置和上位机，所述免疫层析试条信号采集装置包括中控模块、机械传动模块和光学模块；所述中控模块与机械传动模块和光学模块分别连接；所述光学模块还与机械传动模块连接；所述上位机与免疫层析试条信号采集装置通信交互。本发明通过合作式地调度电机控制任务、AD读取任务和数据发送任务对检测过程进行控制，用户可根据需求自行设置定时器个数，大大提高了检测仪的可裁剪性和可移植性、降低了成本，同时仪器具有操作简单、功耗低、稳定性好等优点。

摘要： 本发明公开一种schedule调度模块的形式化验证方法，针对现有技术对于MILS架构的schedule调度模块的验证过程较为复杂，验证效率低下的问题；本发明通过对schedule调度模块的源代码进行重构操作；对重构之后的代码进行形式化建模；对重构之后的代码进行形式化定理描述；对所建立的形式化模型以及定理描述的状态变化进行形式化证明；保证系统的功能正确性和安全性。