本文選題：服務(wù)等級合約 + 服務(wù)組合��； 參考：《復(fù)旦大學(xué)》2014年博士論文

【摘要】：本文關(guān)注服務(wù)組合場景下面向SLA(service level agreement)的QoS管理與優(yōu)化問題。在開放的服務(wù)計(jì)算環(huán)境下,服務(wù)組合方需要有效的QoS管理機(jī)制,通過對其運(yùn)行環(huán)境的靈活配置與優(yōu)化滿足前端多用戶的動態(tài)QoS需求。另一方面,服務(wù)雙方跨管理域的業(yè)務(wù)關(guān)系由SLA及其管理機(jī)制來規(guī)范和保證,作為一種聲明式合約,SLA反映了服務(wù)使用方的QoS需求并為服務(wù)提供方的QoS管理提供了依據(jù)和基礎(chǔ),因此需要在SLA框架下考慮和設(shè)計(jì)服務(wù)組合方QoS管理的功能結(jié)構(gòu)和管理流程,其中服務(wù)組合前端QoS需求由前端SLA合約的簽訂情況決定,而后端SLA合約則保證了各伙伴服務(wù)的服務(wù)質(zhì)量。由于服務(wù)組合其前后端QoS屬性之間的關(guān)聯(lián)性,其前端所能支持和保證的服務(wù)質(zhì)量不僅與底層計(jì)算資源配置有關(guān),而且取決于后端各伙伴服務(wù)的SLA合約簽訂等級。因此面對前端多用戶的動態(tài)SLA簽訂情況,服務(wù)組合方需要實(shí)現(xiàn)前后兩端SLA合約的動態(tài)協(xié)調(diào)并同時完成內(nèi)部計(jì)算資源的優(yōu)化配置,才能在滿足前端QoS需求的同時最大化其SLA收益,而這兩方面資源的優(yōu)化配置依賴于有效的優(yōu)化方法。本文充分考慮現(xiàn)有的SLA管理框架及技術(shù),在前后端一致的SLA環(huán)境下,本文提出一個面向SLA的服務(wù)組合QoS管理模型。模型中首先針對服務(wù)組合及其運(yùn)行環(huán)境中兩方面的可配置資源,形式化定義了服務(wù)組合QoS管理中兩個核心的多目標(biāo)優(yōu)化問題,并進(jìn)一步建立了二者之間的關(guān)系,使其可以在統(tǒng)一的優(yōu)化方法下解決。隨后本文提出一個面向SLA的服務(wù)組合QoS管理模型,在SLA合約管理層、服務(wù)組合實(shí)例管理層以及計(jì)算資源配置層三個管理層次分別給出了其管理實(shí)體、功能構(gòu)成以及主要的管理活動,并在配置方案優(yōu)化的基礎(chǔ)上建立了各層次功能以及管理實(shí)體之間的對應(yīng)關(guān)系,構(gòu)成完整的系統(tǒng)模型。最后在分級和定制兩種管理方式下,給出了整個系統(tǒng)的動態(tài)配置流程。為了高效得到滿足前端QoS需求的最優(yōu)配置方案,本文針對服務(wù)組合QoS管理中的這一核心問題,分別在考慮偏好和不考慮偏好兩種情況下給出了兩種多目標(biāo)優(yōu)化方法及算法。當(dāng)不考慮各個QoS維度上的偏好時,針對現(xiàn)有單純基于帕累托評價(jià)的優(yōu)化方法中結(jié)果集規(guī)模不可控問題,引入多目標(biāo)決策輔助方法：PROMETHEE,從而在不提供偏好的情況下實(shí)現(xiàn)對候選配置方案的高區(qū)分度量化評價(jià),在此基礎(chǔ)上上提出了遺傳算法：P_MOEA,高效返回top-k個PROMETHEE意義上的帕累托最優(yōu)配置方案。在考慮偏好時,針對現(xiàn)有研究中廣泛使用的線性效用函數(shù)方法存在的不足與局限,引入Tchebycheff距離作為服務(wù)組合QoS優(yōu)化問題的標(biāo)量化函數(shù),從而根據(jù)偏好設(shè)置定位其最優(yōu)配置方案；隨后針對偏好設(shè)置的困難,給出一個基于模糊謂詞的偏好模型,并提出偏好設(shè)置過程將模糊謂詞表示的偏好信息轉(zhuǎn)化為標(biāo)量化函數(shù)定義需的量化偏好權(quán)重；最后提出了兩個具有不同優(yōu)化目標(biāo)和特點(diǎn)的遺傳算法：single_EA與hybrid_EA,前者返回單個最優(yōu)配置方案并且效率較高,適合于運(yùn)行時的配置方案優(yōu)化過程。后者返回top-k個最優(yōu)配置方案集合,支持交互式?jīng)Q策過程,適合于配置方案的預(yù)先規(guī)劃與創(chuàng)建。本文通過詳細(xì)的實(shí)驗(yàn)研究驗(yàn)證了算法的性能。
[Abstract]:This article focuses on the QoS management and optimization of the SLA (service level agreement) under the service composition scenario. In an open service computing environment, the service composition party needs an effective QoS management mechanism to meet the dynamic QoS requirements of the front-end multiuser through the flexible configuration and optimization of its running environment. On the other hand, the service is cross managed by both parties. The business relationship of the domain is regulated and guaranteed by SLA and its management mechanism. As a declarative contract, SLA reflects the QoS requirements of the service users and provides the basis and foundation for the QoS management of the service provider. Therefore, the functional structure and management process of the QoS management of the service composition party should be considered and designed under the SLA framework, in which the service group is in the service group. The QoS requirements for the front end are determined by the signing of the front end SLA contract, while the back end SLA contract guarantees the service quality of the partners. The service quality that the front end can support and guarantee is not only related to the underlying computing resource configuration, but also depends on the partner service at the back end because of the association between the front and back end QoS attributes of the service composition. In the face of the dynamic SLA signing of the front end multiuser, the service composition party needs to realize the dynamic coordination of the SLA contracts at both ends and complete the optimization configuration of the internal computing resources at the same time, so as to maximize the SLA revenue at the same time to meet the needs of the front-end QoS, and the two aspects of the optimization of the resources are dependent on the two aspects. This paper takes full consideration of the existing SLA management framework and technology, and proposes a SLA oriented QoS management model for the service composition in the same SLA environment with the same front and back end. The model firstly defines two configurable resources in the service composition and its running environment, and defines the two cores of the QoS management of the service composition. The problem of multi-objective optimization and the relationship between the two are further established so that it can be solved under the unified optimization method. Then a SLA oriented service combination QoS management model is proposed, and the management reality is given in the SLA contract management layer, the service composition instance management layer and the three management levels of the computing resource configuration layer respectively. Body, functional composition and main management activities, and on the basis of the optimization of the configuration scheme, set up the functions of all levels and the corresponding relationship between the management entities, and form a complete system model. Finally, the dynamic configuration process of the whole system is given under two management modes of classification and customization. In order to efficiently meet the needs of the front end QoS, In this paper, two multi-objective optimization methods and algorithms are given under two conditions of preference and without preference. When the preference on each QoS dimension is not considered, the size of the result set in the existing Dan Chunji's Pareto evaluation method is not considered. In the uncontrollable problem, the multiobjective decision assistant method is introduced, PROMETHEE, to realize the quantized evaluation of the high area diversity of the candidate allocation scheme without providing preference. On this basis, the genetic algorithm is proposed: P_MOEA, which returns the Pareto optimal allocation scheme in the sense of Top-k PROMETHEE efficiently. There are shortcomings and limitations of the linear utility function method widely used in the study. The Tchebycheff distance is introduced as the scalar function of the service combination QoS optimization problem, and the optimal allocation scheme is located according to the preference setting. Then, a preference model based on the fuzzy predicate is given for the difficulty of preference setting, and the bias is put forward. The good setting process converts the preference information expressed by the fuzzy predicate to the quantized preference weight required by the scalar function definition. Finally, two genetic algorithms with different optimization objectives and characteristics are proposed: single_EA and hybrid_EA, the former returns to a single optimal configuration scheme and has a higher efficiency, and is suitable for the optimization of the configuration scheme at run time. The latter returns to the Top-k optimal configuration set, supports the interactive decision-making process, and is suitable for the pre planning and creation of the configuration scheme. This paper validates the performance of the algorithm through a detailed experimental study.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP393.09

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