【摘要】：云計(jì)算的興起正在逐漸地改變整個(gè)計(jì)算機(jī)產(chǎn)業(yè)界和學(xué)術(shù)界。云計(jì)算將大量硬件資源、軟件資源和信息資源鏈接在一起,形成一個(gè)規(guī)模巨大的虛擬的共享資源池,為遠(yuǎn)程計(jì)算機(jī)終端用戶提供“召之即來(lái),揮之即去”,并且似乎是“能力無(wú)限”的各種服務(wù)。云計(jì)算中的服務(wù)可分為3個(gè)層次：基礎(chǔ)設(shè)施即服務(wù)(IaaS),平臺(tái)即服務(wù)(PaaS),軟件即服務(wù)(SaaS)。 軟件即服務(wù)SaaS將軟件和基礎(chǔ)設(shè)施的運(yùn)營(yíng)、管理、維護(hù)及軟件所有權(quán)等由用戶轉(zhuǎn)向外部運(yùn)營(yíng)商,用戶不直接擁有軟件和添置硬件,而是通過(guò)互聯(lián)網(wǎng)以付費(fèi)方式租賃和使用軟件服務(wù)。SaaS軟件交付模式將應(yīng)用軟件以服務(wù)的形式提供給用戶,用戶通過(guò)租用軟件減少構(gòu)造、使用和維護(hù)軟件應(yīng)用的成本,增強(qiáng)業(yè)務(wù)變化的靈活性。 數(shù)據(jù)中心是云計(jì)算的基礎(chǔ),隨著數(shù)據(jù)中心規(guī)模的擴(kuò)展,能量消耗已經(jīng)成為數(shù)據(jù)中心運(yùn)營(yíng)和維護(hù)的最大成本。日益顯露的能耗問(wèn)題嚴(yán)重阻礙了云計(jì)算技術(shù)的普及和發(fā)展。許多云計(jì)算廠商都在積極研究綠色節(jié)能技術(shù),通過(guò)快速搶占節(jié)能技術(shù)領(lǐng)域的制高點(diǎn)來(lái)攫取最大利益。 云計(jì)算中的關(guān)鍵技術(shù)主要有：MapReduce編程模式、大規(guī)模數(shù)據(jù)的分布式存儲(chǔ)及管理技術(shù)、虛擬化技術(shù)、云計(jì)算平臺(tái)管理技術(shù)等。云計(jì)算和群體智能算法(如蟻群算法、粒子群算法、遺傳算法等)有著天然的聯(lián)系,云計(jì)算MapReduce編程模式中的Map和Reduce單元起源于智能領(lǐng)域；群體智能算法,如蟻群算法、遺傳算法、模擬退火算法等,因?yàn)榇罅坎捎肕onto Carlo方法,具有很高的并行性,可以在云計(jì)算系統(tǒng)中實(shí)現(xiàn)分布式并行計(jì)算,并行計(jì)算可以充分發(fā)揮云計(jì)算平臺(tái)中強(qiáng)大的運(yùn)算、存儲(chǔ)等處理能力,智能算法將會(huì)在云計(jì)算平臺(tái)中得到很好的應(yīng)用。 蟻群算法具有自組織性、正反饋性,很強(qiáng)的通用性、魯棒性和高的隱含并行性。 為此,本文研究云計(jì)算環(huán)境下面向SaaS服務(wù)的蟻群算法及其在SaaS平臺(tái)中的應(yīng)用,包括在多租戶服務(wù)定制問(wèn)題中的應(yīng)用和在能量感知的服務(wù)放置問(wèn)題中的應(yīng)用。論文研究的主要內(nèi)容和創(chuàng)新點(diǎn)如下。 1、研究云計(jì)算環(huán)境下的蟻群算法。融合云計(jì)算的關(guān)鍵技術(shù)和蟻群算法,設(shè)計(jì)出云計(jì)算環(huán)境下分布式并行化的蟻群算法,提出了基于MapReduce的改進(jìn)背包問(wèn)題蟻群算法(MIAM)。研究該算法求解問(wèn)題的一般思路、方法、特點(diǎn)、框架及性能,充分發(fā)揮云計(jì)算平臺(tái)強(qiáng)大的計(jì)算能力、分布式存儲(chǔ)和管理能力,為問(wèn)題的分布式、并行化和智能化求解以及云計(jì)算平臺(tái)的科學(xué)化、智能化管理提供新的思路和方法。應(yīng)用MapReduce編程模式實(shí)現(xiàn)蟻群優(yōu)化算法的并行化計(jì)算,應(yīng)用輪盤賭、交叉、變異等方法來(lái)改進(jìn)蟻群算法,通過(guò)改變概率計(jì)算時(shí)機(jī)等來(lái)降低蟻群算法的計(jì)算復(fù)雜度。并應(yīng)用該算法在云計(jì)算環(huán)境中分布式并行地求解大規(guī)模多維背包問(wèn)題。 2、將云計(jì)算環(huán)境下的蟻群算法應(yīng)用于解決SaaS平臺(tái)中多租戶服務(wù)定制問(wèn)題。多租戶服務(wù)定制能夠滿足租戶不斷變化的個(gè)性化服務(wù)需求,也是實(shí)現(xiàn)靈活的SaaS多租戶軟件體系結(jié)構(gòu)的核心技術(shù)之一。研究SaaS中多租戶服務(wù)的有關(guān)理論、關(guān)鍵技術(shù)和實(shí)現(xiàn)方法,給出多租戶服務(wù)定制的層次結(jié)構(gòu)圖和定制流程,拓寬蟻群算法在SaaS中的智能應(yīng)用,提高SaaS平臺(tái)的服務(wù)質(zhì)量與效益,具有理論意義和實(shí)際應(yīng)用價(jià)值。提出了基于MapReduce和多目標(biāo)蟻群算法的多租戶服務(wù)定制算法(MSCMA)。MSCMA算法從眾多業(yè)務(wù)流程和海量服務(wù)中為租戶定制出最適合的業(yè)務(wù)流程和優(yōu)化的服務(wù)組合。MSCMA算法設(shè)計(jì)了多目標(biāo)蟻群算法,應(yīng)用MapReduce云計(jì)算技術(shù),在云計(jì)算環(huán)境中分布式并行地運(yùn)行優(yōu)化任務(wù),并采用優(yōu)良解保持策略和解多樣性保持策略。仿真實(shí)驗(yàn)結(jié)果表明,MSCMA算法在求解多租戶個(gè)性化服務(wù)定制問(wèn)題時(shí)表現(xiàn)出良好的收斂性和擴(kuò)展性；該算法具有處理海量數(shù)據(jù)和大規(guī)模問(wèn)題的能力。 3、將云計(jì)算環(huán)境下的蟻群算法應(yīng)用于解決能量感知的服務(wù)放置問(wèn)題。設(shè)計(jì)SaaS平臺(tái)中服務(wù)的分組部署策略及部署算法,來(lái)產(chǎn)生閑置服務(wù)器,使用戶的服務(wù)請(qǐng)求能分發(fā)到數(shù)據(jù)中心適量的服務(wù)器上,通過(guò)關(guān)閉不用的服務(wù)器來(lái)減少能耗,降低數(shù)據(jù)中心的運(yùn)維成本,具有重要的應(yīng)用價(jià)值,符合云計(jì)算的低碳經(jīng)濟(jì)與綠色計(jì)算的發(fā)展理念及總體發(fā)展趨勢(shì)。設(shè)計(jì)服務(wù)部署算法,提出了基于MapReduce和蟻群算法的服務(wù)部署算法。SDMA融合裝箱問(wèn)題的裝箱策略、蟻群算法、MapReduce、HDFS等云計(jì)算技術(shù),將服務(wù)部署到盡可能少的服務(wù)器上,來(lái)進(jìn)一步實(shí)現(xiàn)節(jié)能目標(biāo),同時(shí)考慮了部署代價(jià)最小目標(biāo)、服務(wù)器負(fù)載均衡目標(biāo)。SDMA運(yùn)行在云計(jì)算環(huán)境中分布式并行地求解海量服務(wù)的部署問(wèn)題,并且能適用不同場(chǎng)景的服務(wù)部署問(wèn)題。
[Abstract]:The rise of cloud computing is gradually changing the entire computer industry and academia. Cloud computing links a large number of hardware resources, software resources and information resources together to form a large-scale virtual pool of shared resources for remote computer end-users to provide "call-and-go" and seems to be "incompetent." Services in cloud computing can be divided into three levels: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
Software as a Service SaaS transfers software and infrastructure operations, management, maintenance and software ownership from users to external operators. Instead of directly owning software and adding hardware, users rent and use software services through the Internet at a fee. SaaS software delivery model provides application software to users in the form of services. By leasing software, users reduce the cost of building, using and maintaining software applications, and enhance the flexibility of business changes.
Data center is the foundation of cloud computing. With the expansion of the scale of data center, energy consumption has become the biggest cost of operation and maintenance of data center. The commanding heights of the field are to gain the best interests.
The key technologies in cloud computing are: MapReduce programming mode, distributed storage and management of large-scale data, virtualization technology, cloud computing platform management technology, etc. Cloud computing and swarm intelligence algorithms (such as ant colony algorithm, particle swarm optimization, genetic algorithm, etc.) have a natural relationship, cloud computing MapReduce programming mode Map. And Reduce unit originated in the field of intelligence; swarm intelligence algorithm, such as ant colony algorithm, genetic algorithm, simulated annealing algorithm, because a large number of Monto Carlo method, has a high degree of parallelism, can be implemented in the cloud computing system distributed parallel computing, parallel computing can give full play to the powerful computing and storage in the cloud computing platform. Intelligent algorithm will be well applied in cloud computing platform.
The ant colony algorithm is self-organizing, positive and negative feedback, strong versatility, robustness and high implicit parallelism.
Therefore, this paper studies the ant colony algorithm for SaaS service in cloud computing environment and its application in SaaS platform, including multi-tenant service customization problem and energy-aware service placement problem.
1. Study the ant colony algorithm in cloud computing environment. Fuse the key technology of cloud computing and ant colony algorithm, design a distributed and parallel ant colony algorithm in cloud computing environment. Propose an improved backpack ant colony algorithm (MIAM) based on MapReduce. Cloud computing platform has powerful computing ability, distributed storage and management ability, which provides new ideas and methods for distributed, parallel and intelligent problem solving, scientific and intelligent management of cloud computing platform. The algorithm improves the ant colony algorithm and reduces the computational complexity of the ant colony algorithm by changing the time of probability calculation.
2. Applying ant colony algorithm in cloud computing environment to solve the multi-tenant service customization problem in SaaS platform. Multi-tenant service customization can meet the changing personalized service requirements of tenants, and is also one of the core technologies to realize flexible SaaS multi-tenant software architecture. This paper presents the hierarchical structure diagram and customization process of multi-tenant service customization, expands the intelligent application of ant colony algorithm in SaaS, and improves the service quality and efficiency of SaaS platform. It has theoretical and practical value. A multi-tenant service customization algorithm based on MapReduce and multi-objective ant colony algorithm (MSCMA) is proposed. The MSCMA algorithm designs a multi-objective ant colony algorithm, and uses MapReduce cloud computing technology to run optimization tasks in a distributed and parallel manner in a cloud computing environment. The simulation results show that the MSCMA algorithm has good convergence and scalability in solving multi-tenant personalized service customization problem, and it has the ability to deal with massive data and large-scale problems.
3. Ant Colony Algorithm in Cloud Computing Environment is applied to solve the problem of energy-aware service placement. Packet deployment strategy and deployment algorithm of services in SaaS platform are designed to generate idle servers, so that users'service requests can be distributed to a moderate number of servers in the data center. By shutting down unused servers, energy consumption can be reduced and the number of servers can be reduced. According to the operation and maintenance cost of the center, it has important application value and conforms to the development concept and overall development trend of low-carbon economy and green computing of cloud computing.The service deployment algorithm is designed and a service deployment algorithm based on MapReduce and Ant Colony Algorithm is proposed. SDMA runs in a cloud computing environment to solve the deployment of massive services in a distributed and parallel manner, and can be applied to different scenarios of service deployment.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP18;TP393.09

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