This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two pe...This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.展开更多
While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore,...While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.展开更多
为了满足运行速度快、时延低、性能好、公平性好等特点,提出了多服务器门限服务系统,并利用BiLSTM(Bi-direc-tional Long Short-Term Memory)神经网络对其进行预测分析,使用多服务器接入方式来降低网络时延,改善系统性能。多个服务器调...为了满足运行速度快、时延低、性能好、公平性好等特点,提出了多服务器门限服务系统,并利用BiLSTM(Bi-direc-tional Long Short-Term Memory)神经网络对其进行预测分析,使用多服务器接入方式来降低网络时延,改善系统性能。多个服务器调度时,可以采用同步和异步两种方式。首先,研究多服务器门限服务的系统模型。其次,在单服务器的基础上,利用嵌入马尔可夫链和概率母函数的分析方法对多服务器门限服务的平均排队队长、平均循环周期和平均时延进行求解;同时,利用Matlab进行仿真实验,分别将单服务器系统与多服务器系统的理论值与仿真值进行系统分析,对比多服务器同步和异步两种方式。最后,构建BiLSTM神经网络来预测多服务器系统的性能。实验结果表明,该多服务器系统异步方式优于同步和单服务器系统,多服务器异步系统的性能更好,时延更低,效率更高。综合对比多服务器的3种基本服务系统,在保证公平性的情况下,门限服务系统更加稳定。并且使用BiLSTM神经网络预测算法能够准确预测系统的性能,提高计算效率,对轮询系统的性能评价具有指导意义。展开更多
文摘This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.
文摘While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.
文摘为了满足运行速度快、时延低、性能好、公平性好等特点,提出了多服务器门限服务系统,并利用BiLSTM(Bi-direc-tional Long Short-Term Memory)神经网络对其进行预测分析,使用多服务器接入方式来降低网络时延,改善系统性能。多个服务器调度时,可以采用同步和异步两种方式。首先,研究多服务器门限服务的系统模型。其次,在单服务器的基础上,利用嵌入马尔可夫链和概率母函数的分析方法对多服务器门限服务的平均排队队长、平均循环周期和平均时延进行求解;同时,利用Matlab进行仿真实验,分别将单服务器系统与多服务器系统的理论值与仿真值进行系统分析,对比多服务器同步和异步两种方式。最后,构建BiLSTM神经网络来预测多服务器系统的性能。实验结果表明,该多服务器系统异步方式优于同步和单服务器系统,多服务器异步系统的性能更好,时延更低,效率更高。综合对比多服务器的3种基本服务系统,在保证公平性的情况下,门限服务系统更加稳定。并且使用BiLSTM神经网络预测算法能够准确预测系统的性能,提高计算效率,对轮询系统的性能评价具有指导意义。
