發(fā)布時間：2018-09-10 08:34

【摘要】：云計算的興起正在逐漸地改變整個計算機產(chǎn)業(yè)界和學術界。云計算將大量硬件資源、軟件資源和信息資源鏈接在一起,形成一個規(guī)模巨大的虛擬的共享資源池,為遠程計算機終端用戶提供“召之即來,揮之即去”,并且似乎是“能力無限”的各種服務。云計算中的服務可分為3個層次：基礎設施即服務(IaaS),平臺即服務(PaaS),軟件即服務(SaaS)。 軟件即服務SaaS將軟件和基礎設施的運營、管理、維護及軟件所有權等由用戶轉向外部運營商,用戶不直接擁有軟件和添置硬件,而是通過互聯(lián)網(wǎng)以付費方式租賃和使用軟件服務。SaaS軟件交付模式將應用軟件以服務的形式提供給用戶,用戶通過租用軟件減少構造、使用和維護軟件應用的成本,增強業(yè)務變化的靈活性。 數(shù)據(jù)中心是云計算的基礎,隨著數(shù)據(jù)中心規(guī)模的擴展,能量消耗已經(jīng)成為數(shù)據(jù)中心運營和維護的最大成本。日益顯露的能耗問題嚴重阻礙了云計算技術的普及和發(fā)展。許多云計算廠商都在積極研究綠色節(jié)能技術,通過快速搶占節(jié)能技術領域的制高點來攫取最大利益。 云計算中的關鍵技術主要有：MapReduce編程模式、大規(guī)模數(shù)據(jù)的分布式存儲及管理技術、虛擬化技術、云計算平臺管理技術等。云計算和群體智能算法(如蟻群算法、粒子群算法、遺傳算法等)有著天然的聯(lián)系,云計算MapReduce編程模式中的Map和Reduce單元起源于智能領域；群體智能算法,如蟻群算法、遺傳算法、模擬退火算法等,因為大量采用Monto Carlo方法,具有很高的并行性,可以在云計算系統(tǒng)中實現(xiàn)分布式并行計算,并行計算可以充分發(fā)揮云計算平臺中強大的運算、存儲等處理能力,智能算法將會在云計算平臺中得到很好的應用。 蟻群算法具有自組織性、正反饋性,很強的通用性、魯棒性和高的隱含并行性。 為此,本文研究云計算環(huán)境下面向SaaS服務的蟻群算法及其在SaaS平臺中的應用,包括在多租戶服務定制問題中的應用和在能量感知的服務放置問題中的應用。論文研究的主要內容和創(chuàng)新點如下。 1、研究云計算環(huán)境下的蟻群算法。融合云計算的關鍵技術和蟻群算法,設計出云計算環(huán)境下分布式并行化的蟻群算法,提出了基于MapReduce的改進背包問題蟻群算法(MIAM)。研究該算法求解問題的一般思路、方法、特點、框架及性能,充分發(fā)揮云計算平臺強大的計算能力、分布式存儲和管理能力,為問題的分布式、并行化和智能化求解以及云計算平臺的科學化、智能化管理提供新的思路和方法。應用MapReduce編程模式實現(xiàn)蟻群優(yōu)化算法的并行化計算,應用輪盤賭、交叉、變異等方法來改進蟻群算法,通過改變概率計算時機等來降低蟻群算法的計算復雜度。并應用該算法在云計算環(huán)境中分布式并行地求解大規(guī)模多維背包問題。 2、將云計算環(huán)境下的蟻群算法應用于解決SaaS平臺中多租戶服務定制問題。多租戶服務定制能夠滿足租戶不斷變化的個性化服務需求,也是實現(xiàn)靈活的SaaS多租戶軟件體系結構的核心技術之一。研究SaaS中多租戶服務的有關理論、關鍵技術和實現(xiàn)方法,給出多租戶服務定制的層次結構圖和定制流程,拓寬蟻群算法在SaaS中的智能應用,提高SaaS平臺的服務質量與效益,具有理論意義和實際應用價值。提出了基于MapReduce和多目標蟻群算法的多租戶服務定制算法(MSCMA)。MSCMA算法從眾多業(yè)務流程和海量服務中為租戶定制出最適合的業(yè)務流程和優(yōu)化的服務組合。MSCMA算法設計了多目標蟻群算法,應用MapReduce云計算技術,在云計算環(huán)境中分布式并行地運行優(yōu)化任務,并采用優(yōu)良解保持策略和解多樣性保持策略。仿真實驗結果表明,MSCMA算法在求解多租戶個性化服務定制問題時表現(xiàn)出良好的收斂性和擴展性；該算法具有處理海量數(shù)據(jù)和大規(guī)模問題的能力。 3、將云計算環(huán)境下的蟻群算法應用于解決能量感知的服務放置問題。設計SaaS平臺中服務的分組部署策略及部署算法,來產(chǎn)生閑置服務器,使用戶的服務請求能分發(fā)到數(shù)據(jù)中心適量的服務器上,通過關閉不用的服務器來減少能耗,降低數(shù)據(jù)中心的運維成本,具有重要的應用價值,符合云計算的低碳經(jīng)濟與綠色計算的發(fā)展理念及總體發(fā)展趨勢。設計服務部署算法,提出了基于MapReduce和蟻群算法的服務部署算法。SDMA融合裝箱問題的裝箱策略、蟻群算法、MapReduce、HDFS等云計算技術,將服務部署到盡可能少的服務器上,來進一步實現(xiàn)節(jié)能目標,同時考慮了部署代價最小目標、服務器負載均衡目標。SDMA運行在云計算環(huán)境中分布式并行地求解海量服務的部署問題,并且能適用不同場景的服務部署問題。
[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.
【學位授予單位】：合肥工業(yè)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TP18;TP393.09

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