本文選題：抽樣技術(shù) + 高階間隔　； 參考：《昆明理工大學(xué)》2014年碩士論文

【摘要】：高帶寬和高吞吐量是目前網(wǎng)絡(luò)發(fā)展的重要方向。隨著網(wǎng)絡(luò)傳輸速率的不斷增大,流量監(jiān)測(cè)的技術(shù)得到不斷完善,目前主要應(yīng)用的技術(shù)是基于報(bào)文或時(shí)間間隔抽樣的抽樣,然后根據(jù)抽樣結(jié)果進(jìn)行均值和方差估算等進(jìn)行網(wǎng)絡(luò)流量評(píng)估。隨著網(wǎng)絡(luò)帶寬的數(shù)量級(jí)提升,數(shù)據(jù)傳輸率和有效吞吐量大幅度上升,在相同情況下采用報(bào)文抽樣所需要的抽樣次數(shù)明顯增加,導(dǎo)致系統(tǒng)付出的資源消耗比也隨之上升；而采用時(shí)間間隔抽樣將會(huì)導(dǎo)致相同時(shí)間間隔中的數(shù)據(jù)量和隨機(jī)躍變概率增加,因此為了獲得相對(duì)準(zhǔn)確的估計(jì)值就需提高抽樣頻率,增加資源消耗。因此伴隨網(wǎng)絡(luò)流量的逐步增大,優(yōu)化常規(guī)流量估計(jì)方法,實(shí)現(xiàn)在相對(duì)較少的抽樣頻率和資源消耗的基礎(chǔ)上,獲得更高的估計(jì)精度是一個(gè)較有意義的研究方向。 為滿足更加高速的數(shù)據(jù)傳輸網(wǎng)絡(luò)流量監(jiān)測(cè)的低資源消耗,高精度的需求,本文在對(duì)大量的抽樣方式進(jìn)行研究的基礎(chǔ)上提出了新的適用于高帶寬高吞吐量的流量監(jiān)測(cè)方法--高階間隔估計(jì)算法,它以減少系統(tǒng)內(nèi)存資源占用及提高流量監(jiān)測(cè)精度為前提,在理論算法及優(yōu)化后的低階間隔抽樣數(shù)據(jù)的基礎(chǔ)上對(duì)高速網(wǎng)絡(luò)的流量監(jiān)測(cè)方案進(jìn)行改進(jìn)。即以低階間隔的抽樣數(shù)據(jù)為基礎(chǔ),以高階間隔估計(jì)算法為理論前提,進(jìn)行高階間隔信息的估算,并根據(jù)高階間隔信息估算值的大小進(jìn)行當(dāng)前高速網(wǎng)絡(luò)流量的評(píng)價(jià)與估計(jì)。針對(duì)報(bào)文抽樣、時(shí)間間隔抽樣和低階抽樣高階間隔估算等方法,文章提出了利用KL散度理論對(duì)網(wǎng)絡(luò)流量監(jiān)測(cè)的精度進(jìn)行評(píng)價(jià),并在不同流量監(jiān)測(cè)方案的基礎(chǔ)上對(duì)系統(tǒng)資源消耗和精度等性能進(jìn)行了仿真對(duì)比和說明。 仿真結(jié)果表明,當(dāng)網(wǎng)絡(luò)帶寬和吞吐量不斷增大時(shí),文章所提出的算法可以有效的解決資源消耗和精度之間的矛盾,確保網(wǎng)絡(luò)流量監(jiān)測(cè)的可行性。隨著采樣間隔的增大,高階估計(jì)算法的綜合效果更加明顯,一方面有效的解決了抽樣頻率較高所引起的高資源消耗的問題,另一方面流量估計(jì)精度也相對(duì)明顯提高。由此可以看出文章所提的方法在更高速的網(wǎng)絡(luò)流量監(jiān)測(cè)中擁有更好的使用價(jià)值。
[Abstract]:High bandwidth and high throughput are the important directions of network development. With the increasing of network transmission rate, the technology of traffic monitoring has been improved. At present, the main technology is based on the sampling of message or time interval sampling. Then the network traffic is evaluated according to the mean and variance estimates of the sampling results. With the increase of network bandwidth, the data transmission rate and effective throughput increase greatly, and the sampling times required for packet sampling increase obviously in the same situation, which leads to the increase of the resource consumption ratio of the system. The time interval sampling will increase the amount of data and the probability of random jump in the same time interval. Therefore, in order to obtain a relatively accurate estimate, it is necessary to increase the sampling frequency and resource consumption. Therefore, with the gradual increase of network traffic, it is a meaningful research direction to optimize conventional traffic estimation methods and achieve higher estimation accuracy on the basis of relatively low sampling frequency and resource consumption. In order to meet the demand of low resource consumption and high precision for more high-speed data transmission network traffic monitoring, Based on the research of a large number of sampling methods, this paper presents a new traffic monitoring method for high bandwidth and high throughput, which is called high-order interval estimation algorithm, which is based on reducing the memory consumption of the system and improving the accuracy of traffic monitoring. Based on the theoretical algorithm and the optimized low-order interval sampling data, the flow monitoring scheme of high-speed network is improved. Based on the sampling data of low order interval and the theoretical premise of high order interval estimation algorithm, the high order interval information is estimated, and the current high speed network traffic is evaluated and estimated according to the size of high order interval information. Aiming at the methods of packet sampling, time interval sampling and high order interval estimation of low order sampling, this paper presents a KL divergence theory to evaluate the accuracy of network traffic monitoring. On the basis of different flow monitoring schemes, the performance of system resource consumption and precision is compared and explained. The simulation results show that the proposed algorithm can effectively solve the contradiction between resource consumption and precision and ensure the feasibility of network traffic monitoring when the network bandwidth and throughput are increasing. With the increase of sampling interval, the synthesis effect of high-order estimation algorithm is more obvious. On the one hand, it solves the problem of high resource consumption caused by high sampling frequency, and on the other hand, the accuracy of flow estimation is improved. From this, we can see that the method proposed in this paper has better use value in higher speed network traffic monitoring.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.06

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