The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent t...The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied.Due to the limitation of onboard computational resources and time,common online onboard rescheduling methods for such problems usually adopt simple greedy methods,sacrificing the solution quality to deliver timely solutions.To better solve the problem,a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed.This method uses high computational power on the ground and generates multiple solutions,changing the complex onboard rescheduling problem to a solution selection problem.With this method,it is possible that little time is used to generate a solution that is as good as the solutions on the ground.We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process(MMDP)and mixed-integer programming(MIP).These methods enable the satellite to make independent decisions and produce high-quality solutions.Compared with the traditional centralized scheduling method,the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks.Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.展开更多
Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to t...Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability.展开更多
A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process u...A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.展开更多
Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance...Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.展开更多
Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of...Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.展开更多
A scheduling algorithm is presented aiming at the task scheduling problem in the phased array radar. Rather than assuming the scheduling interval(SI) time, which is the update interval of the radar invoking the schedu...A scheduling algorithm is presented aiming at the task scheduling problem in the phased array radar. Rather than assuming the scheduling interval(SI) time, which is the update interval of the radar invoking the scheduling algorithm, to be a fixed value,it is modeled as a fuzzy set to improve the scheduling flexibility.The scheduling algorithm exploits the fuzzy set model in order to intelligently adjust the SI time. The idle time in other SIs is provided for SIs which will be overload. Thereby more request tasks can be accommodated. The simulation results show that the proposed algorithm improves the successful scheduling ratio by 16%,the threat ratio of execution by 16% and the time utilization ratio by 15% compared with the highest task mode priority first(HPF)algorithm.展开更多
How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data cente...How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.展开更多
How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we ca...How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.展开更多
Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time o...Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.展开更多
In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground conditions.In this scenario,we adopt the leader-follower...In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground conditions.In this scenario,we adopt the leader-follower control mode and propose a modified Lyapunov guidance vector field(LGVF)approach for improving the precision of surveillance trajectory tracking.Then,in order to adopt to poor communication conditions,we propose a prediction-based synchronization method for keeping the formation consistently.Moreover,in order to adapt the multi-UAV system to dynamic and uncertain environment,this paper proposes a hierarchical dynamic task scheduling architecture.In this architecture,we firstly classify all the algorithms that perform tasks according to their functions,and then modularize the algorithms based on plugin technology.Afterwards,integrating the behavior model and plugin technique,this paper designs a three-layer control flow,which can efficiently achieve dynamic task scheduling.In order to verify the effectiveness of our architecture,we consider a multi-UAV traffic monitoring scenario and design several cases to demonstrate the online adjustment from three levels,respectively.展开更多
When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and ...When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O(v2), where v represents the number of tasks.展开更多
How to make use of limited onboard resources for complex and heavy space tasks has attracted much attention.With the continuous improvement on satellite payload capacity and the increasing complexity of observation re...How to make use of limited onboard resources for complex and heavy space tasks has attracted much attention.With the continuous improvement on satellite payload capacity and the increasing complexity of observation requirements,the importance of satellite autonomous task scheduling research has gradually increased.This article first gives the problem description and mathematical model for the satellite autonomous task scheduling and then follows the steps of"satellite autonomous task scheduling,centralized autonomous collaborative task scheduling architecture,distributed autonomous collaborative task scheduling architecture,solution algorithm".Finally,facing the complex and changeable environment situation,this article proposes the future direction of satellite autonomous task scheduling.展开更多
Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem ...Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem for AEOS(OSPFAS).Since the observation scheduling problem for AEOS with comprehensive task clustering(OSWCTC)is a dynamic combination optimization problem,two optimization objectives,the loss rate(LR)of the image quality and the energy consumption(EC),are proposed to format OSWCTC as a bi-objective optimization model.Harnessing the power of an adaptive large neighborhood search(ALNS)algorithm with a nondominated sorting genetic algorithm II(NSGA-II),a bi-objective optimization algorithm,ALNS+NSGA-II,is developed to solve OSWCTC.Based on the existing instances,the efficiency of ALNS+NSGA-II is analyzed from several aspects,meanwhile,results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.展开更多
Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer pr...Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer programming with constraint programming is presented. This algorithm is deployed in this problem through two steps. The first step is to decompose the original problem into master and sub-problem using the logic-based Benders decomposition; then a circus combines master and sub-problem solving process together, and the connection between them is general Benders cut. This hybrid algorithm is tested by a set of derived experiments. The result is compared with corresponding outcomes generated by the strength Pareto evolutionary algorithm and the pure constraint programming solver GECODE, which is an open source software. These tests and comparisons yield promising effect.展开更多
A new static task scheduling algorithm named edge-zeroing based on dynamic critical paths is proposed. The main ideas of the algorithm are as follows: firstly suppose that all of the tasks are in different clusters; s...A new static task scheduling algorithm named edge-zeroing based on dynamic critical paths is proposed. The main ideas of the algorithm are as follows: firstly suppose that all of the tasks are in different clusters; secondly, select one of the critical paths of the partially clustered directed acyclic graph; thirdly, try to zero one of graph communication edges; fourthly, repeat above three processes until all edges are zeroed; finally, check the generated clusters to see if some of them can be further merged without increasing the parallel time. Comparisons of the previous algorithms with edge-zeroing based on dynamic critical paths show that the new algorithm has not only a low complexity but also a desired performance comparable or even better on average to much higher complexity heuristic algorithms.展开更多
A reservation-based feedback scheduling （FS-CBS） of a set of model predictive control （MPC） tasks is presented to optimize the global control performance subject to limited computational resource. Implemented as a...A reservation-based feedback scheduling （FS-CBS） of a set of model predictive control （MPC） tasks is presented to optimize the global control performance subject to limited computational resource. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance. Each MPC task is assigned with a constant bandwidth server （CBS）, whose reserved processor time is adjusted dynamically. The FS-CBS is shown robust against the varying of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.展开更多
An online pulse interleaving scheduling algorithm is proposed for a solution to the task scheduling problem in the digital array radar(DAR). The full DAR task structure is explicitly considered in a way that the waiti...An online pulse interleaving scheduling algorithm is proposed for a solution to the task scheduling problem in the digital array radar(DAR). The full DAR task structure is explicitly considered in a way that the waiting duration is able to be utilized to transmit or receive subtasks, namely the pulse interleaving,as well as the receiving durations of different tasks are able to be overlapped. The algorithm decomposes the pulse interleaving scheduling analysis into the time constraint check and the energy constraint check, and schedules online all kinds of tasks that are able to be interleaved. Thereby the waiting duration and the receiving duration in the DAR task are both fully utilized. The simulation results verify the performance improvement and the high efficiency of the proposed algorithm compared with the existing ones.展开更多
Without considering security, existing message scheduling mechanisms may expose critical messages to malicious threats like confidentiality attacks. Incorporating confidentiality improvement into message scheduling, t...Without considering security, existing message scheduling mechanisms may expose critical messages to malicious threats like confidentiality attacks. Incorporating confidentiality improvement into message scheduling, this paper investigates the problem of scheduling aperiodc messages with time-critical and security-critical requirements. A risk-based security profit model is built to quantify the security quality of messages; and a dynamic programming based approximation algorithm is proposed to schedule aperiodic messages with guaranteed security performance. Experimental results illustrate the efficiency and effectiveness of the proposed algorithm.展开更多
A new two-stage soft real-time scheduling algorithm based on priority table was proposed for task dispatch and selection in cluster systems with inaccurate parameters. The inaccurate characteristics of the system were...A new two-stage soft real-time scheduling algorithm based on priority table was proposed for task dispatch and selection in cluster systems with inaccurate parameters. The inaccurate characteristics of the system were modeled through probability analysis. By taking into account the multiple important system parameters, including task deadline, priority, session integrity and memory access locality, the algorithm is expected to achieve high quality of service. Lots of simulation results collected under different load conditions demonstrate that the algorithm can not only effectively overcome the inaccuracy of the system state, but also optimize the task rejected ratio, value realized ratio, differentiated service guaranteed ratio, and session integrity ensured ratio with the average improvement of 3.5%, 5.8%, 7.6% and 5. 5%, respectively. Compared with many existing schemes that cannen deal with the inaccurate parameters of the system, the proposed scheme can achieve the best system performance by carefully adjusting scheduling probability. The algorithm is expected to be promising in systems with soft real-time scheduling requirement such as E-commerce applications.展开更多
基金supported by the National Natural Science Foundation of China(72001212,71701204,71801218)the China Hunan Postgraduate Research Innovating Project(CX2018B020)。
文摘The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied.Due to the limitation of onboard computational resources and time,common online onboard rescheduling methods for such problems usually adopt simple greedy methods,sacrificing the solution quality to deliver timely solutions.To better solve the problem,a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed.This method uses high computational power on the ground and generates multiple solutions,changing the complex onboard rescheduling problem to a solution selection problem.With this method,it is possible that little time is used to generate a solution that is as good as the solutions on the ground.We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process(MMDP)and mixed-integer programming(MIP).These methods enable the satellite to make independent decisions and produce high-quality solutions.Compared with the traditional centralized scheduling method,the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks.Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.
基金Project (60505018) supported by the National Natural Science Foundation of China
文摘Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability.
基金This work was supported by the National Key Research and Development Program of China(2021YFB2900603)the National Natural Science Foundation of China(61831008).
文摘A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.
基金supported by the National Natural Science Foundation of China (61104180)the National Basic Research Program of China(973 Program) (97361361)
文摘Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.
文摘Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.
基金supported by the National Youth Foundation(61503408)
文摘A scheduling algorithm is presented aiming at the task scheduling problem in the phased array radar. Rather than assuming the scheduling interval(SI) time, which is the update interval of the radar invoking the scheduling algorithm, to be a fixed value,it is modeled as a fuzzy set to improve the scheduling flexibility.The scheduling algorithm exploits the fuzzy set model in order to intelligently adjust the SI time. The idle time in other SIs is provided for SIs which will be overload. Thereby more request tasks can be accommodated. The simulation results show that the proposed algorithm improves the successful scheduling ratio by 16%,the threat ratio of execution by 16% and the time utilization ratio by 15% compared with the highest task mode priority first(HPF)algorithm.
基金supported by the National Natural Science Foundation of China(6120200461272084)+9 种基金the National Key Basic Research Program of China(973 Program)(2011CB302903)the Specialized Research Fund for the Doctoral Program of Higher Education(2009322312000120113223110003)the China Postdoctoral Science Foundation Funded Project(2011M5000952012T50514)the Natural Science Foundation of Jiangsu Province(BK2011754BK2009426)the Jiangsu Postdoctoral Science Foundation Funded Project(1102103C)the Natural Science Fund of Higher Education of Jiangsu Province(12KJB520007)the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(yx002001)
文摘How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.
基金supported by the National Natural Science Foundation of China(71401131)the MOE(Ministry of Education in China)Project of Humanities and Social Sciences(13XJC630011)the Ministry of Education Research Fund for the Doctoral Program of Higher Education(20120184120040)
文摘How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.
基金supported by the National Natural Science Foundation of China(6120235461272422)the Scientific and Technological Support Project(Industry)of Jiangsu Province(BE2011189)
文摘Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.
基金Project(2017YFB1301104)supported by the National Key Research and Development Program of ChinaProjects(61906212,61802426)supported by the National Natural Science Foundation of China。
文摘In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground conditions.In this scenario,we adopt the leader-follower control mode and propose a modified Lyapunov guidance vector field(LGVF)approach for improving the precision of surveillance trajectory tracking.Then,in order to adopt to poor communication conditions,we propose a prediction-based synchronization method for keeping the formation consistently.Moreover,in order to adapt the multi-UAV system to dynamic and uncertain environment,this paper proposes a hierarchical dynamic task scheduling architecture.In this architecture,we firstly classify all the algorithms that perform tasks according to their functions,and then modularize the algorithms based on plugin technology.Afterwards,integrating the behavior model and plugin technique,this paper designs a three-layer control flow,which can efficiently achieve dynamic task scheduling.In order to verify the effectiveness of our architecture,we consider a multi-UAV traffic monitoring scenario and design several cases to demonstrate the online adjustment from three levels,respectively.
文摘When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O(v2), where v represents the number of tasks.
基金supported by the National Natural Science Foundation of China(72001212,61773120)Hunan Postgraduate Research Innovation Project(CX20210031)+1 种基金the Foundation for the Author of National Excellent Doctoral Dissertation of China(2014-92)the Innovation Team of Guangdong Provincial Department of Education(2018KCXTD031)。
文摘How to make use of limited onboard resources for complex and heavy space tasks has attracted much attention.With the continuous improvement on satellite payload capacity and the increasing complexity of observation requirements,the importance of satellite autonomous task scheduling research has gradually increased.This article first gives the problem description and mathematical model for the satellite autonomous task scheduling and then follows the steps of"satellite autonomous task scheduling,centralized autonomous collaborative task scheduling architecture,distributed autonomous collaborative task scheduling architecture,solution algorithm".Finally,facing the complex and changeable environment situation,this article proposes the future direction of satellite autonomous task scheduling.
文摘Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem for AEOS(OSPFAS).Since the observation scheduling problem for AEOS with comprehensive task clustering(OSWCTC)is a dynamic combination optimization problem,two optimization objectives,the loss rate(LR)of the image quality and the energy consumption(EC),are proposed to format OSWCTC as a bi-objective optimization model.Harnessing the power of an adaptive large neighborhood search(ALNS)algorithm with a nondominated sorting genetic algorithm II(NSGA-II),a bi-objective optimization algorithm,ALNS+NSGA-II,is developed to solve OSWCTC.Based on the existing instances,the efficiency of ALNS+NSGA-II is analyzed from several aspects,meanwhile,results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.
基金supported by the National Security Fundamental Research Foundation of China (61361)the National Natural Science Foundation of China (61104180)
文摘Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer programming with constraint programming is presented. This algorithm is deployed in this problem through two steps. The first step is to decompose the original problem into master and sub-problem using the logic-based Benders decomposition; then a circus combines master and sub-problem solving process together, and the connection between them is general Benders cut. This hybrid algorithm is tested by a set of derived experiments. The result is compared with corresponding outcomes generated by the strength Pareto evolutionary algorithm and the pure constraint programming solver GECODE, which is an open source software. These tests and comparisons yield promising effect.
文摘A new static task scheduling algorithm named edge-zeroing based on dynamic critical paths is proposed. The main ideas of the algorithm are as follows: firstly suppose that all of the tasks are in different clusters; secondly, select one of the critical paths of the partially clustered directed acyclic graph; thirdly, try to zero one of graph communication edges; fourthly, repeat above three processes until all edges are zeroed; finally, check the generated clusters to see if some of them can be further merged without increasing the parallel time. Comparisons of the previous algorithms with edge-zeroing based on dynamic critical paths show that the new algorithm has not only a low complexity but also a desired performance comparable or even better on average to much higher complexity heuristic algorithms.
文摘A reservation-based feedback scheduling （FS-CBS） of a set of model predictive control （MPC） tasks is presented to optimize the global control performance subject to limited computational resource. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance. Each MPC task is assigned with a constant bandwidth server （CBS）, whose reserved processor time is adjusted dynamically. The FS-CBS is shown robust against the varying of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.
文摘An online pulse interleaving scheduling algorithm is proposed for a solution to the task scheduling problem in the digital array radar(DAR). The full DAR task structure is explicitly considered in a way that the waiting duration is able to be utilized to transmit or receive subtasks, namely the pulse interleaving,as well as the receiving durations of different tasks are able to be overlapped. The algorithm decomposes the pulse interleaving scheduling analysis into the time constraint check and the energy constraint check, and schedules online all kinds of tasks that are able to be interleaved. Thereby the waiting duration and the receiving duration in the DAR task are both fully utilized. The simulation results verify the performance improvement and the high efficiency of the proposed algorithm compared with the existing ones.
基金supported by the National Natural Science Foundation of China (60673142)the National High Technology Research and Development Progrm of China (863 Program) (2006AA01Z1732007AA01Z131)
文摘Without considering security, existing message scheduling mechanisms may expose critical messages to malicious threats like confidentiality attacks. Incorporating confidentiality improvement into message scheduling, this paper investigates the problem of scheduling aperiodc messages with time-critical and security-critical requirements. A risk-based security profit model is built to quantify the security quality of messages; and a dynamic programming based approximation algorithm is proposed to schedule aperiodic messages with guaranteed security performance. Experimental results illustrate the efficiency and effectiveness of the proposed algorithm.
基金Project(60573127) supported by the National Natural Science Foundation of China project(05JJ40131) supported by theNatural Science Foundation of Hunan Province
文摘A new two-stage soft real-time scheduling algorithm based on priority table was proposed for task dispatch and selection in cluster systems with inaccurate parameters. The inaccurate characteristics of the system were modeled through probability analysis. By taking into account the multiple important system parameters, including task deadline, priority, session integrity and memory access locality, the algorithm is expected to achieve high quality of service. Lots of simulation results collected under different load conditions demonstrate that the algorithm can not only effectively overcome the inaccuracy of the system state, but also optimize the task rejected ratio, value realized ratio, differentiated service guaranteed ratio, and session integrity ensured ratio with the average improvement of 3.5%, 5.8%, 7.6% and 5. 5%, respectively. Compared with many existing schemes that cannen deal with the inaccurate parameters of the system, the proposed scheme can achieve the best system performance by carefully adjusting scheduling probability. The algorithm is expected to be promising in systems with soft real-time scheduling requirement such as E-commerce applications.
