本文選題：內(nèi)容中心網(wǎng)絡(luò) + 緩存放置策略��； 參考：《哈爾濱工程大學(xué)》2014年碩士論文

【摘要】：內(nèi)容中心網(wǎng)絡(luò)是未來互聯(lián)網(wǎng)的一種新型體系結(jié)構(gòu)。內(nèi)容中心網(wǎng)絡(luò)緩存機(jī)制的特點(diǎn)是網(wǎng)絡(luò)中任何節(jié)點(diǎn)都具有緩存空間,且節(jié)點(diǎn)對經(jīng)過其進(jìn)行轉(zhuǎn)發(fā)的內(nèi)容不加區(qū)分全部進(jìn)行緩存。內(nèi)容中心網(wǎng)絡(luò)的緩存機(jī)制容易產(chǎn)生內(nèi)容冗余度高、緩存利用率低等問題。緩存放置策略是解決此類問題的有效方法。針對內(nèi)容中心網(wǎng)絡(luò)緩存機(jī)制存在的不足,本文對現(xiàn)有的緩存放置策略進(jìn)行詳細(xì)分析,提出基于預(yù)測的最優(yōu)化緩存放置策略。首先,將緩存放置問題轉(zhuǎn)化為最優(yōu)化問題,綜合考慮影響緩存性能的幾個因素,構(gòu)建最大化收益緩存放置模型(Max-Benefit模型),針對Max-Benefit模型中對象被訪問頻率不能表示對象將來的熱度趨勢這一問題,在Max-Benefit模型中引入預(yù)測機(jī)制,提出基于預(yù)測的最大化收益緩存放置模型(PB-Max-Benefit模型),通過對對象將來的熱度趨勢進(jìn)行預(yù)測實(shí)現(xiàn)盡可能多得對熱門內(nèi)容的緩存,避免無效緩存的發(fā)生,提高緩存性能。然后,基于PB-Max-Benefit模型改進(jìn)標(biāo)準(zhǔn)遺傳算法的選擇算子、交叉算子和變異算子,對其進(jìn)行求解,通過對標(biāo)準(zhǔn)遺傳算法進(jìn)行改進(jìn)來提高它的收斂速度,避免陷入所求問題的局部最優(yōu)解,提高求解PB-Max-Benefit模型全局最優(yōu)解的性能。最后,使用NS-3網(wǎng)絡(luò)模擬器在虛擬機(jī)中進(jìn)行仿真實(shí)驗(yàn)。以緩存命中率、無效緩存率和網(wǎng)絡(luò)平均跳數(shù)作為驗(yàn)證指標(biāo),在不同緩存大小、數(shù)據(jù)訪問模式以及網(wǎng)絡(luò)規(guī)模的仿真環(huán)境下,通過與現(xiàn)有的最優(yōu)化緩存放置策略以及CCN緩存機(jī)制進(jìn)行仿真對比,得到仿真結(jié)果,將仿真結(jié)果以圖表的形式表示出來并對其進(jìn)行分析,最終驗(yàn)證本文提出的基于預(yù)測的最優(yōu)化緩存放置策略的有效性。
[Abstract]:The content center network is a new architecture of the Internet in the future. The feature of content-centric network caching mechanism is that any node in the network has cache space and nodes cache all content transmitted through it without distinction. The cache mechanism of content-centric network is prone to the problems of high content redundancy and low cache utilization. Cache placement strategy is an effective way to solve this problem. In view of the shortcomings of the content center network caching mechanism, this paper analyzes the existing cache placement strategies in detail, and proposes an optimized cache placement strategy based on prediction. First of all, the cache placement problem is transformed into an optimization problem, and several factors affecting cache performance are considered synthetically. The Max-Benefit model is constructed to maximize revenue cache placement. Aiming at the problem that the object access frequency in the Max-Benefit model can not represent the future heat trend of the object, a prediction mechanism is introduced into the Max-Benefit model. This paper proposes a PB-Max-Benefit model based on prediction to maximize revenue cache placement. By predicting the future heat trend of objects, we can cache as many popular content as possible, avoid the occurrence of invalid cache, and improve the cache performance. Then, based on the PB-Max-Benefit model, the selection operator, crossover operator and mutation operator of the standard genetic algorithm are improved, and the convergence rate of the standard genetic algorithm is improved to avoid falling into the local optimal solution of the problem. The performance of solving the global optimal solution of PB-Max-Benefit model is improved. Finally, the NS-3 network simulator is used to simulate the virtual machine. With cache hit ratio, invalid cache rate and average hop number of network as the verification index, under different cache size, data access mode and network scale simulation environment, By comparing with the existing optimal cache placement strategy and CCN cache mechanism, the simulation results are obtained, and the simulation results are represented in the form of charts and analyzed. Finally, the effectiveness of the proposed optimal cache placement strategy based on prediction is verified.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.02

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 魯進(jìn)超;蜂窩與D2D混合網(wǎng)絡(luò)中內(nèi)容緩存與協(xié)作分發(fā)策略研究[D];北京郵電大學(xué);2017年

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本文編號：1963564