http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Dynamic Network Provisioning for Time-Varying Traffic
Sharma, Vicky,Kar, Koushik,La, Richard,Tassiulas, Leandros The Korea Institute of Information and Commucation 2007 Journal of communications and networks Vol.9 No.4
In this paper, we address the question of dynamic network provisioning for time-varying traffic rates, with the objective of maximizing the system throughput. We assume that the network is capable of providing bandwidth guaranteed traffic tunnels for an ingress-egress pair and present an approach that (1) updates the tunnel routes and (2) adjusts the tunnel bandwidths, in an incremental, adaptive manner, based on the variations in the incoming traffic. First, we consider a simpler scenario where tunnel routes are fixed, and present an approach for adjusting the tunnel bandwidths dynamically. We show, through simulations, that our dynamic bandwidth assignment algorithm significantly outperforms the optimal static bandwidth provisioning policy, and yields a performance close to that of the optimal dynamic bandwidth provisioning policy. We also propose an adaptive route update algorithm, which can be used in conjunction with our dynamic bandwidth assignment policy, and leads to further improvement in the overall system performance.
Chen, Yuchao,Tang, Haoyue,Wang, Jintao,Yang, Pengkun,Tassiulas, Leandros 한국통신학회 2023 Journal of communications and networks Vol.25 No.5
In this paper, we consider sampling an Ornstein-Uhlenbeck (OU) process through a channel for remote estimation. The goal is to minimize the mean square error (MSE) at the estimator under a sampling frequency constraint when the channel delay statistics is unknown. Sampling for MSE minimization is reformulated into an optimal stopping problem. By revisiting the threshold structure of the optimal stopping policy when the delay statistics is known, we propose an online sampling algorithm to learn the optimum threshold using stochastic approximation algorithm and the virtual queue method. We prove that with probability 1, the MSE of the proposed online algorithm converges to the minimum MSE that is achieved when the channel delay statistics is known. The cumulative MSE gap of our proposed algorithm compared with the minimum MSE up to the $(k+1)$-th sample grows with rate at most $\mathcal{O}(\ln k)$. Our proposed online algorithm can satisfy the sampling frequency constraint theoretically. Finally, simulation results are provided to demonstrate the performance of the proposed algorithm.