【摘要】：在云計(jì)算環(huán)境下軟件測試面臨許多新的挑戰(zhàn)。本文通過對(duì)云測試服務(wù)研究現(xiàn)狀的調(diào)查,發(fā)現(xiàn)利用云測試服務(wù)進(jìn)行測試時(shí),需要可靠的資源分配策略作為保障。目前,對(duì)于云資源分配策略的研究具備一定的理論研究和實(shí)際操作,但缺乏一個(gè)同時(shí)站在云測試用戶和云服務(wù)提供商需求角度考慮的整體資源分配架構(gòu)。本研究在參考云服務(wù)模型的基礎(chǔ)上,設(shè)計(jì)了一個(gè)云測試服務(wù)模型。模型中包含兩個(gè)組件:一是為云測試用戶提供面向資源可用性分配策略的資源分配器1;另一個(gè)是在滿足用戶的可用性需求基礎(chǔ)上,為云服務(wù)提供商提供面向資源有效性分配策略的資源分配器2。然后使用預(yù)測分配的方法對(duì)資源分配策略進(jìn)行研究,并對(duì)兩個(gè)組件實(shí)現(xiàn)的算法展開闡述。資源分配器1采用BP神經(jīng)網(wǎng)絡(luò)對(duì)云測試虛擬機(jī)的CPU利用率、可用內(nèi)存大小進(jìn)行預(yù)測,從而為虛擬機(jī)資源的分配提供一種策略。在預(yù)測過程中,為提高資源預(yù)測精度,引入改進(jìn)的智能算法即粒子群算法對(duì)BP神經(jīng)網(wǎng)絡(luò)的初始閾值和權(quán)值進(jìn)行優(yōu)化,最后采用對(duì)比實(shí)驗(yàn)證明了改進(jìn)算法的有效性。在資源分配器1分配的多臺(tái)云測試虛擬機(jī)已滿足資源可用性的基礎(chǔ)上,資源分配器2采用遺傳算法,以最小化云服務(wù)器內(nèi)存為目標(biāo),實(shí)現(xiàn)云測試虛擬機(jī)到云服務(wù)器的安置,從而為虛擬機(jī)資源的分配提供另一種策略。在分配過程中,傳統(tǒng)的遺傳算法易求得不可行解,引入單點(diǎn)交叉修復(fù)、旋轉(zhuǎn)變異以及外部罰函數(shù)理論對(duì)遺傳算法進(jìn)行改進(jìn)。最后采用實(shí)驗(yàn)證明了改進(jìn)算法尋優(yōu)能力的可行性。
[Abstract]:Software testing faces many new challenges in cloud computing environment. Based on the investigation of the research status of cloud test services, this paper finds that reliable resource allocation strategy is needed to ensure the use of cloud test services for testing. At present, the research on cloud resource allocation strategy has some theoretical research and practical operation, but there is a lack of an overall resource allocation architecture from the perspective of cloud test users and cloud service providers at the same time. Based on the reference of cloud service model, a cloud test service model is designed in this paper. The model contains two components: one is to provide resource allocation policy-oriented resource allocators for cloud test users; the other is to provide cloud service providers with resource allocation policy-oriented resource allocators for resource efficiency allocation policies on the basis of meeting the availability needs of users. Then the resource allocation strategy is studied by using the method of predictive allocation, and the algorithm implemented by the two components is described. Resource allocator 1 uses BP neural network to predict the CPU utilization of cloud test virtual machines and the size of available memory, thus providing a strategy for the allocation of virtual machine resources. In the process of prediction, in order to improve the accuracy of resource prediction, an improved intelligent algorithm, particle swarm optimization algorithm, is introduced to optimize the initial threshold and weight of BP neural network. Finally, the effectiveness of the improved algorithm is proved by comparative experiments. On the basis that multiple cloud test virtual machines allocated by resource allocator 1 have satisfied the availability of resources, resource allocator 2 adopts genetic algorithm to minimize cloud server memory to realize the placement of cloud test virtual machines to cloud servers, thus providing another strategy for the allocation of virtual machine resources. In the process of allocation, the traditional genetic algorithm is easy to obtain the infeasible solution, and the single point cross repair, rotation mutation and external penalty function theory are introduced to improve the genetic algorithm. Finally, the feasibility of improving the optimization ability of the algorithm is proved by experiments.
【學(xué)位授予單位】：重慶郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09;TP311.53

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