To meet the demand for high on-chip network performance, flexible routing algorithms supplying path diversity and congestion alleviation are required. We propose a CAOE-FA router as a combination of congestionawarenes...To meet the demand for high on-chip network performance, flexible routing algorithms supplying path diversity and congestion alleviation are required. We propose a CAOE-FA router as a combination of congestionawareness and fair arbitration. Buffer occupancies from downstream neighbors are collected to indicate the congestion levels, among the candidate outputs permitted by the odd-even(OE) turn model, the lightest loaded direction is selected; fair arbitration is employed for the condition of the same congestion level to replace random selection. Experimental results show that the CAOE-FA can reduce the average packet latency by up to 22.18% and improve the network throughput by up to 68.58%, with ignorable price of hardware cost.展开更多
The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for...The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods.However,these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically.To overcome this drawback,we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources.In a proposed routing protocol,either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state,Q-value,and reward function to set the desired path toward the destination.A new reward function that consists of a buffer occupancy,link reliability and hop count is considered.Moreover,look ahead algorithm is employed to update the Q-value with values within two hops simultaneously.This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account,accordingly.Finally,the simulation results presented approximately 20%higher packet delivery ratio and 15%shorter end-to-end delay,compared to those with the existing scheme by avoiding congestion adaptively.展开更多
基金Project supported by the National Natural Science Foundation of China(No.61625403)
文摘To meet the demand for high on-chip network performance, flexible routing algorithms supplying path diversity and congestion alleviation are required. We propose a CAOE-FA router as a combination of congestionawareness and fair arbitration. Buffer occupancies from downstream neighbors are collected to indicate the congestion levels, among the candidate outputs permitted by the odd-even(OE) turn model, the lightest loaded direction is selected; fair arbitration is employed for the condition of the same congestion level to replace random selection. Experimental results show that the CAOE-FA can reduce the average packet latency by up to 22.18% and improve the network throughput by up to 68.58%, with ignorable price of hardware cost.
基金This work was supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2019-0-01343,Training Key Talents in Industrial Convergence Security)and Research Cluster Project,R20143,by Zayed University Research Office.
文摘The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods.However,these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically.To overcome this drawback,we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources.In a proposed routing protocol,either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state,Q-value,and reward function to set the desired path toward the destination.A new reward function that consists of a buffer occupancy,link reliability and hop count is considered.Moreover,look ahead algorithm is employed to update the Q-value with values within two hops simultaneously.This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account,accordingly.Finally,the simulation results presented approximately 20%higher packet delivery ratio and 15%shorter end-to-end delay,compared to those with the existing scheme by avoiding congestion adaptively.
