The composite scheme based on preemption and small buffers is an efficient method for contention resolution. To support services differentiation, it is the first time that the analytical model of delay preemption base...The composite scheme based on preemption and small buffers is an efficient method for contention resolution. To support services differentiation, it is the first time that the analytical model of delay preemption based priority is built. Further, in order to guarantee the low-loss requirement for high priority bursts, an improved scheme is proposed and investigated by limiting the buffered right of low priority bursts within the specific traffic states. The simulation results show that, without the deterioration of blocking performance, there is more than 40% reduction on burst loss being achieved under the conditionρ=1.0 for high priority bursts.展开更多
With the popularization of multi-variety and small-batch production patterns,the flexible job shop scheduling problem(FJSSP)has been widely studied.The sharing of processing resources by multiple machines frequently o...With the popularization of multi-variety and small-batch production patterns,the flexible job shop scheduling problem(FJSSP)has been widely studied.The sharing of processing resources by multiple machines frequently occurs due to space constraints in a flexible shop,which results in resource preemption for processing workpieces.Resource preemption complicates the constraints of scheduling problems that are otherwise difficult to solve.In this paper,the flexible job shop scheduling problem under the process resource preemption scenario is modeled,and a two-layer rule scheduling algorithm based on deep reinforcement learning is proposed to achieve the goal of minimum scheduling time.The simulation experiments compare our scheduling algorithm with two traditional metaheuristic optimization algorithms among different processing resource distribution scenarios in static scheduling environment.The results suggest that the two-layer rule scheduling algorithm based on deep reinforcement learning is more effective than the meta-heuristic algorithm in the application of processing resource preemption scenarios.Ablation experiments,generalization,and dynamic experiments are performed to demonstrate the excellent performance of our method for FJSSP under resource preemption.展开更多
IP over WDM网络中,业务疏导能有效提高波长带宽利用率。然而,单一化的业务疏导机制难以满足不同业务的QoS要求,也很难做到经济合理地使用光路。提出了一种支持多优先级业务的疏导机制,该机制根据光路传输的时延和丢包特性来选择恰当的...IP over WDM网络中,业务疏导能有效提高波长带宽利用率。然而,单一化的业务疏导机制难以满足不同业务的QoS要求,也很难做到经济合理地使用光路。提出了一种支持多优先级业务的疏导机制,该机制根据光路传输的时延和丢包特性来选择恰当的疏导路径,同时,该机制结合了抢占和流量分割技术,能有效地实现流量分割和多路抢占,降低了业务的阻塞概率。仿真结果显示,该疏导机制在保证业务QoS的同时,降低了高优先级业务的阻塞概率;在业务负载低时,能有效减少抢占。展开更多
文摘The composite scheme based on preemption and small buffers is an efficient method for contention resolution. To support services differentiation, it is the first time that the analytical model of delay preemption based priority is built. Further, in order to guarantee the low-loss requirement for high priority bursts, an improved scheme is proposed and investigated by limiting the buffered right of low priority bursts within the specific traffic states. The simulation results show that, without the deterioration of blocking performance, there is more than 40% reduction on burst loss being achieved under the conditionρ=1.0 for high priority bursts.
文摘With the popularization of multi-variety and small-batch production patterns,the flexible job shop scheduling problem(FJSSP)has been widely studied.The sharing of processing resources by multiple machines frequently occurs due to space constraints in a flexible shop,which results in resource preemption for processing workpieces.Resource preemption complicates the constraints of scheduling problems that are otherwise difficult to solve.In this paper,the flexible job shop scheduling problem under the process resource preemption scenario is modeled,and a two-layer rule scheduling algorithm based on deep reinforcement learning is proposed to achieve the goal of minimum scheduling time.The simulation experiments compare our scheduling algorithm with two traditional metaheuristic optimization algorithms among different processing resource distribution scenarios in static scheduling environment.The results suggest that the two-layer rule scheduling algorithm based on deep reinforcement learning is more effective than the meta-heuristic algorithm in the application of processing resource preemption scenarios.Ablation experiments,generalization,and dynamic experiments are performed to demonstrate the excellent performance of our method for FJSSP under resource preemption.
文摘IP over WDM网络中,业务疏导能有效提高波长带宽利用率。然而,单一化的业务疏导机制难以满足不同业务的QoS要求,也很难做到经济合理地使用光路。提出了一种支持多优先级业务的疏导机制,该机制根据光路传输的时延和丢包特性来选择恰当的疏导路径,同时,该机制结合了抢占和流量分割技术,能有效地实现流量分割和多路抢占,降低了业务的阻塞概率。仿真结果显示,该疏导机制在保证业务QoS的同时,降低了高优先级业务的阻塞概率;在业务负载低时,能有效减少抢占。
