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
This paper introduces a pioneering dynamic system optimisation for multiagent(DySOMA)framework,revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems.The core of DySOMA is...This paper introduces a pioneering dynamic system optimisation for multiagent(DySOMA)framework,revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems.The core of DySOMA is an advanced auction-based algorithm coupled with a novel task preemption ranking mechanism,seamlessly integrated with an ontology knowledge graph that dynamically updates.This integration not only enhances the efficiency of task allocation among robots but also significantly improves the adaptability of the system to environmental changes.Compared to other advanced algorithms,the DySOMA algorithm shows significant performance improvements,with its RLB 26.8%higher than that of the best-performing Consensus-Based Parallel Auction and Execution(CBPAE)algorithm at 10 robots and 29.7%higher at 20 robots,demonstrating its superior capability in balancing task loads and optimising task completion times in larger,more complex environments.DySOMA sets a new benchmark for intelligent robot task scheduling,promising significant advancements in the autonomy and flexibility of robotic systems in complex evolving environments.展开更多
Enhanced mobile broadband(eMBB)and ultra-reliable low-latency communication(URLLC)are two critical services in 5G mobile networks.While there has been extensive research on their coexistence,few studies have considere...Enhanced mobile broadband(eMBB)and ultra-reliable low-latency communication(URLLC)are two critical services in 5G mobile networks.While there has been extensive research on their coexistence,few studies have considered the impact of bursty URLLC on their coexistence performance.In this paper,we propose a method to allocate computing and radio resources for coexisting eMBB and bursty URLLC services by preempting both computing queues in the base station(BS)and time-frequency resources at the air interface.Specifically,we first divide the computing resources at the BS into a shared part for both URLLC and eMBB users and an exclusive part only for eMBB users,and propose a queuing mechanism with preemptive-resume priority for accessing the shared computing resources.Furthermore,we propose a preemptive puncturing method and a threshold-based queuing mechanism in the air interface to enable the multiplexing of eMBB and URLLC on shared time-frequency resources.We analytically derive the average queuing delay,average computation delay,and average transmission delay of eMBB and URLLC packets.Based on this analysis,we formulate a mixed-integer nonlinear programming problem to minimize the average delay of URLLC packets while satisfying the average delay and throughput requirements of eMBB by jointly optimizing the eMBB subcarrier allocation,the URLLC subcarrier scheduling and the computing resource allocation.We decompose this problem into three subproblems and solve them alternately using a block coordinate descent algorithm.Numerical results show that our proposed method reduces the outage probability and average delay of URLLC compared to the existing works.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.
基金supported by the Natural Science Foundation of Shandong Province(No.ZR2024MF085)the Jinan Science and Technology Bureau(No.2021GXRC026)+1 种基金Young Scholars Program of Shandong University(No.2018WLJH71)the Fundamental Research Funds of Shandong University and the Taishan Scholar Foundation of Shandong Province.
文摘This paper introduces a pioneering dynamic system optimisation for multiagent(DySOMA)framework,revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems.The core of DySOMA is an advanced auction-based algorithm coupled with a novel task preemption ranking mechanism,seamlessly integrated with an ontology knowledge graph that dynamically updates.This integration not only enhances the efficiency of task allocation among robots but also significantly improves the adaptability of the system to environmental changes.Compared to other advanced algorithms,the DySOMA algorithm shows significant performance improvements,with its RLB 26.8%higher than that of the best-performing Consensus-Based Parallel Auction and Execution(CBPAE)algorithm at 10 robots and 29.7%higher at 20 robots,demonstrating its superior capability in balancing task loads and optimising task completion times in larger,more complex environments.DySOMA sets a new benchmark for intelligent robot task scheduling,promising significant advancements in the autonomy and flexibility of robotic systems in complex evolving environments.
基金supported in part by the Key Research and Development Program of Shaanxi(2024GX-YBXM-019)in part by Open Fund of Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation(CSSAE-2023-007)in part by the UKRI EPSRC(EP/X038971/1).
文摘Enhanced mobile broadband(eMBB)and ultra-reliable low-latency communication(URLLC)are two critical services in 5G mobile networks.While there has been extensive research on their coexistence,few studies have considered the impact of bursty URLLC on their coexistence performance.In this paper,we propose a method to allocate computing and radio resources for coexisting eMBB and bursty URLLC services by preempting both computing queues in the base station(BS)and time-frequency resources at the air interface.Specifically,we first divide the computing resources at the BS into a shared part for both URLLC and eMBB users and an exclusive part only for eMBB users,and propose a queuing mechanism with preemptive-resume priority for accessing the shared computing resources.Furthermore,we propose a preemptive puncturing method and a threshold-based queuing mechanism in the air interface to enable the multiplexing of eMBB and URLLC on shared time-frequency resources.We analytically derive the average queuing delay,average computation delay,and average transmission delay of eMBB and URLLC packets.Based on this analysis,we formulate a mixed-integer nonlinear programming problem to minimize the average delay of URLLC packets while satisfying the average delay and throughput requirements of eMBB by jointly optimizing the eMBB subcarrier allocation,the URLLC subcarrier scheduling and the computing resource allocation.We decompose this problem into three subproblems and solve them alternately using a block coordinate descent algorithm.Numerical results show that our proposed method reduces the outage probability and average delay of URLLC compared to the existing works.
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。
