With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify hea...With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy.In this paper,we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters.This method is called EBF sketches.Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic accuracy.It can be employed to address problems such as maintaining frequency statistics and finding heavy hitters.Our experimental results validate our theoretical claims and verifies the effectiveness of our techniques.展开更多
The Institute of Cast Metals Engineers announces that it has secured more sponsorship for the World Foundry Congress 2006 from four prominent companies. To be held in Harrogate, in theUnited Kingdom, from 5-7 June 200...The Institute of Cast Metals Engineers announces that it has secured more sponsorship for the World Foundry Congress 2006 from four prominent companies. To be held in Harrogate, in theUnited Kingdom, from 5-7 June 2006, the World Foundry Congress 2006 is set to be the major global event of the year for the cast metals industry.展开更多
Local differential privacy(LDP),which is a technique that employs unbiased statistical estimations instead of real data,is usually adopted in data collection,as it can protect every user’s privacy and prevent the lea...Local differential privacy(LDP),which is a technique that employs unbiased statistical estimations instead of real data,is usually adopted in data collection,as it can protect every user’s privacy and prevent the leakage of sensitive information.The segment pairs method(SPM),multiple-channel method(MCM)and prefix extending method(PEM)are three known LDP protocols for heavy hitter identification as well as the frequency oracle(FO)problem with large domains.However,the low scalability of these three LDP algorithms often limits their application.Specifically,communication and computation strongly affect their efficiency.Moreover,excessive grouping or sharing of privacy budgets makes the results inaccurate.To address the abovementioned problems,this study proposes independent channel(IC)and mixed independent channel(MIC),which are efficient LDP protocols for FO with a large domains.We design a flexible method for splitting a large domain to reduce the number of sub-domains.Further,we employ the false positive rate with interaction to obtain an accurate estimation.Numerical experiments demonstrate that IC outperforms all the existing solutions under the same privacy guarantee while MIC performs well under a small privacy budget with the lowest communication cost.展开更多
随着网络带宽的不断提高,在线识别大流对于拥塞控制、异常检测等网络应用具有重要意义.提出了一种提取大流的算法FEFS(flow extracting with frequency & size),能够通过在线识别和淘汰小流,把大流信息保存在有限的高速存储空间中,...随着网络带宽的不断提高,在线识别大流对于拥塞控制、异常检测等网络应用具有重要意义.提出了一种提取大流的算法FEFS(flow extracting with frequency & size),能够通过在线识别和淘汰小流,把大流信息保存在有限的高速存储空间中,从而快速提取大流.该算法利用LRU(least recently used)定位更新频率低的流,并进一步用流尺寸因子s和自适应调节因子M标记其中相对较小的流,最后用新到达的流将其替换.FEFS把LRU策略和尺寸因子s相结合,同时考虑了流的近期更新频率和累积报文数量,因此能够准确在线识别大流.LRU策略和尺寸因子都利用了流大小的重尾分布特征,因此FEFS能以很低的存储代价保存和更新大流信息.模拟实验表明,在限定存储条件下,FEFS的平均相对误差率明显低于经典的multi-stage filter算法,而平均报文处理时间也短于multi-stage filter算法.展开更多
网络的管理与监测是网络领域的重要话题,这一领域的相关技术通常也称为网络测量(network measurement).网络重要流检测(network heavy hitter detection)是网络测量的一项关键技术,也是研究对象.重要流指占用网络资源(如带宽或发送的数...网络的管理与监测是网络领域的重要话题,这一领域的相关技术通常也称为网络测量(network measurement).网络重要流检测(network heavy hitter detection)是网络测量的一项关键技术,也是研究对象.重要流指占用网络资源(如带宽或发送的数据包数量)超过某一给定标准的流,检测重要流有助于快速识别网络异常,提升网络运行效率,但链路的高速化为其实现带来了挑战.按出现时间顺序,可将重要流检测方法划分为两大类:基于传统网络框架的和基于软件定义网络(SDN)框架的.围绕网络重要流检测相关的框架与算法,系统地总结其发展过程与研究现状,并尝试给出其未来可能的发展方向.展开更多
当前,知识定义网络赋能AI技术发展,算力网络提供AI所需算力资源,二者逐渐趋于融合,形成了知识定义算力网络(Knowledge Defined Computing Networking,KDCN)。KDCN赋能发展了诸多新型网络应用,如元宇宙、AR/VR、东数西算等,这些新型应用...当前,知识定义网络赋能AI技术发展,算力网络提供AI所需算力资源,二者逐渐趋于融合,形成了知识定义算力网络(Knowledge Defined Computing Networking,KDCN)。KDCN赋能发展了诸多新型网络应用,如元宇宙、AR/VR、东数西算等,这些新型应用对算力资源和网络资源有极大的需求,被称为重击流(Heavy Hitter,HH)。HH流的存在严重加剧了KDCN网络的拥塞情况。针对这一挑战,提出了一种智能流量调度机制,旨在通过深度Q神经网络来解决KDCN中的拥塞问题。相较于离线训练过程,通过流量数据检测与采集、在模型训练和拥塞流调决策之间建立实时闭环,来实现深度Q神经网络模型的在线训练。基于该闭环控制,智能流调模型通过不断学习可以实现持续演化,并用于提供实时决策。实验结果表明,该算法在资源利用率、吞吐量、平均丢包率等方面优于现有方法。展开更多
基金This study is supported by National key research and development program(2016YFB0801200).
文摘With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy.In this paper,we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters.This method is called EBF sketches.Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic accuracy.It can be employed to address problems such as maintaining frequency statistics and finding heavy hitters.Our experimental results validate our theoretical claims and verifies the effectiveness of our techniques.
文摘The Institute of Cast Metals Engineers announces that it has secured more sponsorship for the World Foundry Congress 2006 from four prominent companies. To be held in Harrogate, in theUnited Kingdom, from 5-7 June 2006, the World Foundry Congress 2006 is set to be the major global event of the year for the cast metals industry.
基金This work was supported by the National Key R&D Program of China(2018YFB1004401)the National Natural Science Foundation of China(NSFC)(Grant Nos.61772537,61772536,62072460,and 62076245)Beijing Natural Science Foundation(4212022).
文摘Local differential privacy(LDP),which is a technique that employs unbiased statistical estimations instead of real data,is usually adopted in data collection,as it can protect every user’s privacy and prevent the leakage of sensitive information.The segment pairs method(SPM),multiple-channel method(MCM)and prefix extending method(PEM)are three known LDP protocols for heavy hitter identification as well as the frequency oracle(FO)problem with large domains.However,the low scalability of these three LDP algorithms often limits their application.Specifically,communication and computation strongly affect their efficiency.Moreover,excessive grouping or sharing of privacy budgets makes the results inaccurate.To address the abovementioned problems,this study proposes independent channel(IC)and mixed independent channel(MIC),which are efficient LDP protocols for FO with a large domains.We design a flexible method for splitting a large domain to reduce the number of sub-domains.Further,we employ the false positive rate with interaction to obtain an accurate estimation.Numerical experiments demonstrate that IC outperforms all the existing solutions under the same privacy guarantee while MIC performs well under a small privacy budget with the lowest communication cost.
基金Supported by the National Natural Science Foundation of China under Grant No.60573134(国家自然科学基金)the Program for New Century Excellent Talents in University of China(新世纪优秀人才支持计划)
文摘随着网络带宽的不断提高,在线识别大流对于拥塞控制、异常检测等网络应用具有重要意义.提出了一种提取大流的算法FEFS(flow extracting with frequency & size),能够通过在线识别和淘汰小流,把大流信息保存在有限的高速存储空间中,从而快速提取大流.该算法利用LRU(least recently used)定位更新频率低的流,并进一步用流尺寸因子s和自适应调节因子M标记其中相对较小的流,最后用新到达的流将其替换.FEFS把LRU策略和尺寸因子s相结合,同时考虑了流的近期更新频率和累积报文数量,因此能够准确在线识别大流.LRU策略和尺寸因子都利用了流大小的重尾分布特征,因此FEFS能以很低的存储代价保存和更新大流信息.模拟实验表明,在限定存储条件下,FEFS的平均相对误差率明显低于经典的multi-stage filter算法,而平均报文处理时间也短于multi-stage filter算法.
文摘网络的管理与监测是网络领域的重要话题,这一领域的相关技术通常也称为网络测量(network measurement).网络重要流检测(network heavy hitter detection)是网络测量的一项关键技术,也是研究对象.重要流指占用网络资源(如带宽或发送的数据包数量)超过某一给定标准的流,检测重要流有助于快速识别网络异常,提升网络运行效率,但链路的高速化为其实现带来了挑战.按出现时间顺序,可将重要流检测方法划分为两大类:基于传统网络框架的和基于软件定义网络(SDN)框架的.围绕网络重要流检测相关的框架与算法,系统地总结其发展过程与研究现状,并尝试给出其未来可能的发展方向.
文摘当前,知识定义网络赋能AI技术发展,算力网络提供AI所需算力资源,二者逐渐趋于融合,形成了知识定义算力网络(Knowledge Defined Computing Networking,KDCN)。KDCN赋能发展了诸多新型网络应用,如元宇宙、AR/VR、东数西算等,这些新型应用对算力资源和网络资源有极大的需求,被称为重击流(Heavy Hitter,HH)。HH流的存在严重加剧了KDCN网络的拥塞情况。针对这一挑战,提出了一种智能流量调度机制,旨在通过深度Q神经网络来解决KDCN中的拥塞问题。相较于离线训练过程,通过流量数据检测与采集、在模型训练和拥塞流调决策之间建立实时闭环,来实现深度Q神经网络模型的在线训练。基于该闭环控制,智能流调模型通过不断学习可以实现持续演化,并用于提供实时决策。实验结果表明,该算法在资源利用率、吞吐量、平均丢包率等方面优于现有方法。
