本文選題：云平臺(tái)數(shù)據(jù)庫　切入點(diǎn)：負(fù)載控制　出處：《計(jì)算機(jī)仿真》2017年07期 　論文類型：期刊論文

【摘要】：對(duì)云平臺(tái)下實(shí)驗(yàn)室數(shù)據(jù)庫資源負(fù)載進(jìn)行優(yōu)化控制,可提高實(shí)驗(yàn)室數(shù)據(jù)的管理效率。進(jìn)行資源控制時(shí),應(yīng)將實(shí)驗(yàn)室數(shù)據(jù)庫資源損耗特征按性能進(jìn)行分類后,通過提高實(shí)驗(yàn)室數(shù)據(jù)庫資源的吞吐量和整體穩(wěn)定性完成控制,但是傳統(tǒng)方法通過控制輸入給數(shù)據(jù)庫的負(fù)載數(shù)量來提高數(shù)據(jù)庫系統(tǒng)的穩(wěn)定,但是忽略了實(shí)驗(yàn)室數(shù)據(jù)庫資源的吞吐量和整體穩(wěn)定性,降低了數(shù)據(jù)庫資源負(fù)載控制的性能。提出一種云平臺(tái)下實(shí)驗(yàn)室數(shù)據(jù)庫資源負(fù)載優(yōu)化控制方法。首先,運(yùn)用基于多目標(biāo)的優(yōu)化算法以數(shù)據(jù)庫負(fù)載特征參數(shù)為負(fù)載控制的依據(jù),根據(jù)各個(gè)負(fù)載具有的不同屬性特征,獲取每個(gè)負(fù)載控制時(shí)間距離內(nèi)價(jià)值大且平均響應(yīng)時(shí)間少的負(fù)載,確定數(shù)據(jù)庫負(fù)載控制的形態(tài)特征。其次利用改進(jìn)特征向量增量聚類算法,依據(jù)負(fù)載控制的形態(tài)特征對(duì)負(fù)載進(jìn)行分類,采用動(dòng)態(tài)聚類形式提取負(fù)載狀態(tài)特征,將具備同類性能特征和數(shù)據(jù)庫資源耗損特征的負(fù)載分為一類,通過負(fù)載控制以提高數(shù)據(jù)庫系統(tǒng)的吞吐量和整體穩(wěn)定性,最終實(shí)現(xiàn)云平臺(tái)下實(shí)驗(yàn)室數(shù)據(jù)庫資源負(fù)載優(yōu)化控制。仿真結(jié)果表明,通過確定負(fù)載形態(tài)特征,然后對(duì)形態(tài)特征進(jìn)行分類后實(shí)現(xiàn)了數(shù)據(jù)庫的負(fù)載控制,優(yōu)化了云平臺(tái)虛擬實(shí)驗(yàn)室數(shù)據(jù)庫資源負(fù)載的控制性能。
[Abstract]:The optimal control of laboratory database resource load under cloud platform can improve the management efficiency of laboratory data. In resource control, the resource loss characteristics of laboratory database should be classified according to its performance. By improving the throughput and overall stability of the laboratory database resources, the traditional method improves the stability of the database system by controlling the amount of load input to the database. However, the throughput and overall stability of laboratory database resources are ignored, and the performance of database resource load control is reduced. Based on the multi-objective optimization algorithm, the load characteristic parameters of the database are taken as the basis of load control. According to the different attribute characteristics of each load, the load with large value and less average response time is obtained within the control time distance of each load. Secondly, the improved feature vector incremental clustering algorithm is used to classify the load according to the morphological characteristics of the load control, and the dynamic clustering method is used to extract the load state feature. The load with the same performance characteristics and the characteristics of database resource consumption is divided into one class, which can improve the throughput and overall stability of the database system through load control. The simulation results show that the load control of the database is realized by determining the morphological characteristics of the load and then classifying the morphological features of the database. The control performance of resource load of virtual laboratory database on cloud platform is optimized.
【作者單位】： 山西大學(xué)商務(wù)學(xué)院;
【分類號(hào)】：TP311.13;TP393.09

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本文編號(hào)：1651695