Traditional packet switching networks have typically employed window-based congestion control schemes in order to regulate traffic flow. In ATM networks, the high speed of the communication links and the varied nature...Traditional packet switching networks have typically employed window-based congestion control schemes in order to regulate traffic flow. In ATM networks, the high speed of the communication links and the varied nature of the carried traffic make such schemes inappropriate. Therefore, simpler and more efficient schemes have to be proposed to improve the congestion control for ATM switching. This paper presents an exact performance analysis of ATM switching whose inputs consist of Continuous-Bit-Rate(CBR) and bursty traffic. The CBR traffic and bursty traffic are described by Bernoulli process and the Interrupted Bernoulli Process(IBP), respectively. Bursty traffic smoothing mechanism is analyzed. With the use of a recursive algorithm, the cell loss probability and the average delay for ATM switching of mixed CBR and bursty traffic are exactly calculated. Traffic smoothing could be implemented at a slower peak rate keeping the average rate constant or decreasing the average bursty length. Both numerical展开更多
Since Internet is dominated by TCP-based applications, active queue management （AQM） is considered as an effective way for congestion control. However, most AQM schemes suffer obvious performance degradation with dy...Since Internet is dominated by TCP-based applications, active queue management （AQM） is considered as an effective way for congestion control. However, most AQM schemes suffer obvious performance degradation with dynamic traffic. Extensive measurements found that Internet traffic is extremely bursty and possibly self-similar. We propose in this paper a new AQM scheme called multiscale controller （MSC） based on the understanding of traffic burstiness in multiple time scale. Different from most of other AQM schemes, MSC combines rate-based and queue-based control in two time scales. While the rate-based dropping on burst level （large time scales） determines the packet drop aggressiveness and is responsible for low and stable queuing delay, good robustness and responsiveness, the queue-based modulation of the packet drop probability on packet level （small time scales） will bring low loss and high throughput. Stability analysis is performed based on a fluid-flow model of the TCP/MSC congestion control system and simulation results show that MSC outperforms many of the current AQM schemes.展开更多
Spectrum prediction plays an important role for the secondary user(SU)to utilize the shared spectrum resources.However,currently utilized prediction methods are not well applied to spectrum with high burstiness,as par...Spectrum prediction plays an important role for the secondary user(SU)to utilize the shared spectrum resources.However,currently utilized prediction methods are not well applied to spectrum with high burstiness,as parameters of prediction models cannot be adjusted properly.This paper studies the prediction problem of bursty bands.Specifically,we first collect real Wi Fi transmission data in 2.4GHz Industrial,Scientific,Medical(ISM)band which is considered to have bursty characteristics.Feature analysis of the data indicates that the spectrum occupancy law of the data is time-variant,which suggests that the performance of commonly used single prediction model could be restricted.Considering that the match between diverse spectrum states and multiple prediction models may essentially improve the prediction performance,we then propose a deep-reinforcement learning based multilayer perceptron(DRL-MLP)method to address this matching problem.The state space of the method is composed of feature vectors,and each of the vectors contains multi-dimensional feature values.Meanwhile,the action space consists of several multilayer perceptrons(MLPs)that are trained on the basis of multiple classified data sets.We finally conduct experiments with the collected real data and simulations with generated data to verify the performance of the proposed method.The results demonstrate that the proposed method significantly outperforms the stateof-the-art methods in terms of the prediction accuracy.展开更多
基于排队论、有效带宽及有效容量理论,研究时延QoS(Quality of Service)约束下的网络带宽估计问题。考虑到网络流量具有的突发性,采用MMOO(Markov Modulated On Off)过程描述业务到达过程。通过建模分析网络队列系统,解析时延QoS参数与...基于排队论、有效带宽及有效容量理论,研究时延QoS(Quality of Service)约束下的网络带宽估计问题。考虑到网络流量具有的突发性,采用MMOO(Markov Modulated On Off)过程描述业务到达过程。通过建模分析网络队列系统,解析时延QoS参数与服务参数的关系,对网络流量的带宽需求进行估计。展开更多
Mobile Cloud Computing (MCC) is a modern architecture that brings together cloudcomputing, mobile computing and wireless networks to assist mobile devices in storage,computing and communication. A cloud environment is...Mobile Cloud Computing (MCC) is a modern architecture that brings together cloudcomputing, mobile computing and wireless networks to assist mobile devices in storage,computing and communication. A cloud environment is developed to support a widerange of users that request the cloud resources in a dynamic environment with possible constraints. Burstiness in users service requests causes drastic and unpredictableincreases in the resource requests that have a crucial impact on policies of resourceallocation. How can the cloud system efficiently handle such burstiness when the cloudresources are limited? This problem becomes a hot issue in the MCC research area. Inthis paper, we develop a system model for the resource allocation based on the SemiMarkovian Decision Process (SMDP), able of dynamically assigning the mobile servicerequests to a set of cloud resources, to optimize the usage of cloud resources and maximize the total long-term expected system reward when the arrival process is a finitestate Markov-Modulated Poisson Process (MMPP). Numerical results show that ourproposed model performs much better than the Greedy algorithm in terms of achievinghigher system rewards and lower service requests blocking probabilities, especially whenthe burstiness degree is high, and the cloud resources are limited.展开更多
基金Supported by the National Natural Science Foundation of ChinaFoundation of the Acadency of Electronic Science,Chinathe National Postdoctoral Science Fund of China
文摘Traditional packet switching networks have typically employed window-based congestion control schemes in order to regulate traffic flow. In ATM networks, the high speed of the communication links and the varied nature of the carried traffic make such schemes inappropriate. Therefore, simpler and more efficient schemes have to be proposed to improve the congestion control for ATM switching. This paper presents an exact performance analysis of ATM switching whose inputs consist of Continuous-Bit-Rate(CBR) and bursty traffic. The CBR traffic and bursty traffic are described by Bernoulli process and the Interrupted Bernoulli Process(IBP), respectively. Bursty traffic smoothing mechanism is analyzed. With the use of a recursive algorithm, the cell loss probability and the average delay for ATM switching of mixed CBR and bursty traffic are exactly calculated. Traffic smoothing could be implemented at a slower peak rate keeping the average rate constant or decreasing the average bursty length. Both numerical
基金Supported by the National Grand Fundamental Research 973 Program of China under Grant No. 2003CB314801, the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No. 20040286001 and the National Natural Science Foundation of China under Grant No. 90604003. Acknowledgments The authors would like to thank Professor Guan-Qun Gu for his supervision and Professor Jun Shen for his comments on an early draft of this paper.
文摘Since Internet is dominated by TCP-based applications, active queue management （AQM） is considered as an effective way for congestion control. However, most AQM schemes suffer obvious performance degradation with dynamic traffic. Extensive measurements found that Internet traffic is extremely bursty and possibly self-similar. We propose in this paper a new AQM scheme called multiscale controller （MSC） based on the understanding of traffic burstiness in multiple time scale. Different from most of other AQM schemes, MSC combines rate-based and queue-based control in two time scales. While the rate-based dropping on burst level （large time scales） determines the packet drop aggressiveness and is responsible for low and stable queuing delay, good robustness and responsiveness, the queue-based modulation of the packet drop probability on packet level （small time scales） will bring low loss and high throughput. Stability analysis is performed based on a fluid-flow model of the TCP/MSC congestion control system and simulation results show that MSC outperforms many of the current AQM schemes.
基金supported in part by the China National Key R&D Program(no.2020YF-B1808000)Beijing Natural Science Foundation(No.L192002)+2 种基金in part by the Fundamental Research Funds for the Central Universities(No.328202206)the National Natural Science Foundation of China(No.61971058)in part by"Advanced and sophisticated"discipline construction project of universities in Beijing(No.20210013Z0401)。
文摘Spectrum prediction plays an important role for the secondary user(SU)to utilize the shared spectrum resources.However,currently utilized prediction methods are not well applied to spectrum with high burstiness,as parameters of prediction models cannot be adjusted properly.This paper studies the prediction problem of bursty bands.Specifically,we first collect real Wi Fi transmission data in 2.4GHz Industrial,Scientific,Medical(ISM)band which is considered to have bursty characteristics.Feature analysis of the data indicates that the spectrum occupancy law of the data is time-variant,which suggests that the performance of commonly used single prediction model could be restricted.Considering that the match between diverse spectrum states and multiple prediction models may essentially improve the prediction performance,we then propose a deep-reinforcement learning based multilayer perceptron(DRL-MLP)method to address this matching problem.The state space of the method is composed of feature vectors,and each of the vectors contains multi-dimensional feature values.Meanwhile,the action space consists of several multilayer perceptrons(MLPs)that are trained on the basis of multiple classified data sets.We finally conduct experiments with the collected real data and simulations with generated data to verify the performance of the proposed method.The results demonstrate that the proposed method significantly outperforms the stateof-the-art methods in terms of the prediction accuracy.
文摘基于排队论、有效带宽及有效容量理论,研究时延QoS(Quality of Service)约束下的网络带宽估计问题。考虑到网络流量具有的突发性,采用MMOO(Markov Modulated On Off)过程描述业务到达过程。通过建模分析网络队列系统,解析时延QoS参数与服务参数的关系,对网络流量的带宽需求进行估计。
文摘Mobile Cloud Computing (MCC) is a modern architecture that brings together cloudcomputing, mobile computing and wireless networks to assist mobile devices in storage,computing and communication. A cloud environment is developed to support a widerange of users that request the cloud resources in a dynamic environment with possible constraints. Burstiness in users service requests causes drastic and unpredictableincreases in the resource requests that have a crucial impact on policies of resourceallocation. How can the cloud system efficiently handle such burstiness when the cloudresources are limited? This problem becomes a hot issue in the MCC research area. Inthis paper, we develop a system model for the resource allocation based on the SemiMarkovian Decision Process (SMDP), able of dynamically assigning the mobile servicerequests to a set of cloud resources, to optimize the usage of cloud resources and maximize the total long-term expected system reward when the arrival process is a finitestate Markov-Modulated Poisson Process (MMPP). Numerical results show that ourproposed model performs much better than the Greedy algorithm in terms of achievinghigher system rewards and lower service requests blocking probabilities, especially whenthe burstiness degree is high, and the cloud resources are limited.
