针对网络控制系统(Network Control Systems,NCS)节点中的非周期任务,设计了一种FC-ABS调度算法.该调度算法可以根据非周期任务的时间特性采取不同的调度方式,并通过反馈调度减小非周期任务调度对周期任务的影响.仿真实验证明了算法的...针对网络控制系统(Network Control Systems,NCS)节点中的非周期任务,设计了一种FC-ABS调度算法.该调度算法可以根据非周期任务的时间特性采取不同的调度方式,并通过反馈调度减小非周期任务调度对周期任务的影响.仿真实验证明了算法的正确性.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
This paper considers a reentrant scheduling problem on parallel primary machines with a remote server machine, which is required to carry out the setup operation. In this problem, each job has three operations. The fi...This paper considers a reentrant scheduling problem on parallel primary machines with a remote server machine, which is required to carry out the setup operation. In this problem, each job has three operations. The first and last operations are performed by the same primary machine, implying the reentrance, and the second operation is processed on the single server machine. The order of jobs is predetermined in our context. The challenge is to assign jobs to the primary machines to minimize the makespan. We develop a genetic algorithm(GA) to solve this problem. Based on a simple strategy of assigning jobs in batches on the parallel primary machines, the standardized random key vector representation is employed to split the jobs into batches. Comparisons among the proposed algorithm, the branch and bound(BB) algorithm and the heuristic algorithm, coordinated scheduling(CS), which is only one heuristic algorithm to solve this problem in the literature, are made on the benchmark data. The computational experiments show that the proposed genetic algorithm outperforms the heuristic CS and the maximum relative improvement rate in the makespan is 1.66%.展开更多
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.
基金Supported by National Natural Science Foundation of China(No.61271374)Beijing Natural Science Foundation(No.4122068)
文摘This paper considers a reentrant scheduling problem on parallel primary machines with a remote server machine, which is required to carry out the setup operation. In this problem, each job has three operations. The first and last operations are performed by the same primary machine, implying the reentrance, and the second operation is processed on the single server machine. The order of jobs is predetermined in our context. The challenge is to assign jobs to the primary machines to minimize the makespan. We develop a genetic algorithm(GA) to solve this problem. Based on a simple strategy of assigning jobs in batches on the parallel primary machines, the standardized random key vector representation is employed to split the jobs into batches. Comparisons among the proposed algorithm, the branch and bound(BB) algorithm and the heuristic algorithm, coordinated scheduling(CS), which is only one heuristic algorithm to solve this problem in the literature, are made on the benchmark data. The computational experiments show that the proposed genetic algorithm outperforms the heuristic CS and the maximum relative improvement rate in the makespan is 1.66%.
