大规模需求环境下基于服务模式的服务组合优化方法
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摘要
随着信息技术的发展，传统服务逐渐向现代服务转型。现代服务体现为以先进IT技术为支撑且与现实要素相结合的复杂的领域交互过程。现代服务业的深度变革导致服务的目标发生重大的变化：从追求基本的服务功能和性能转变为关注以服务收益和顾客满意度为代表的服务价值，从追求简单服务价值转变为追求整合的服务价值增值。实现该过程的核心问题在于如何根据领域客户的个性化需求进行价值优化的服务组合。然而，在以云计算、大数据为代表的服务化和大规模整合的趋势下，领域服务的构成方式日趋复杂，使得这一问题呈现出大规模需求、海量候选服务和服务容量受限等新特征。以上特征从问题规模、优化准则和限制条件等方面对服务组合优化提出了新的挑战。
鉴于现有方法在应对以上问题的不足，结合当前服务工程在解决领域问题中呈现出需求与服务交汇的趋势，本文针对领域中大规模需求环境下服务组合优化问题，给出基于服务模式的解决框架及方法。该框架一方面深入刻画和组织领域中的大规模时序并发需求，通过限制不同方案的可用服务能力来优化配置领域服务，另一方面基于服务模式刻画领域业务和优化经验，用以组织候选服务和支持最终优化方案的快速形成。主要研究工作包括以下几个方面：
（1）系统化的服务组合优化框架及相关概念。考虑不同优化层面和手段间的关联，给出领域中大规模需求环境下服务组合优化问题的系统化解决框架及相关概念。具体地，对大规模领域需求的特征进行分析并给出相应的应对思路，提出基于服务模式的服务组合优化思路。进而，将领域中的服务组合优化过程分为面向大规模需求的服务配置、基于服务模式的服务组合方案空间缩减和支持服务模式构造的组合服务选择三个阶段，识别出各阶段的主要问题，并对整体优化过程进行了描述。
（2）大规模服务需求分析与服务优化配置。鉴于当前研究仅关注于单个需求的现状，将服务组合优化的研究范畴扩展到领域中大规模需求的场景，通过将针对大规模需求的服务组合优化问题转化为针对一组需求类的优化问题加以解决。具体地，考虑大规模时序并发需求的时空关联特性对服务需求进行分段聚类，使其共享部分优化结果以提高优化效率；提出相应的服务能力预留和分配方法，以保障有限服务能力在时空两个维度上分配的公平性；其中，考虑到对于服务收益和公平性的不同侧重，分别给出基于调度和基于均衡的服务能力分配方法。
（3）基于服务模式的服务组合方案空间优化。这一阶段主要利用领域业务和优化经验来缩减服务组合优化的方案空间，从而简化优化问题。具体地，提出服务模式的概念，对领域中的共性业务过程进行刻画；通过对服务模式及其相关优势服务进行组织和预提取，从而在优化过程中进行重用；为适应可用服务的变化，给出相关优势服务的按需更新方法；为进一步提高优化效率，给出基于贝叶斯的候选服务缩减方法，利用历史优化经验概率地减小优化问题规模。
（4）支持服务模式构造的服务组合优化选择。这一阶段基于上一阶段的优化结果将优化问题进一步转化到单个需求的层面加以解决。首先，为了保证聚类中的不同需求获得个性化的服务方案，提出候选服务过滤的方法以保证服务方案的多样化；而后，分别针对需要重点优化的关键模式、收益优化问题和海量候选服务的场景，提出面向组合服务执行过程不确定性的分阶段优化策略、基于价格启发的迭代优化方法和改进的人工蜂群算法，为不同场景下的优化问题得到最终的具体服务方案。
最后，为了验证理论研究成果，给出服务组合优化工具的设计思路，并结合智慧家庭采购业务实例，对本文提出的理论和方法进行应用验证。
With the development of information technology, traditional services graduallytransform into modern services. Modern services assume as domain-specific andcomplicated multi-party interactive processes, which are supported by advanced ITtechnology and hybrided with realistic ingredients. The profound revolution of modernservice industry has led to significant changes in the goal of services, i.e., from pursuingthe basic functionality and performance of services to focusing on the overall servicevalue characterized by service profit and customer satisfaction; and from pursuingsimple service value to seeking for integrated service value-added. The core issuetowards enabling this process is to conduct value-optimized service compositionsaccording to customers’ personalized requirements. However, under the trend ofservicitization and massive integration characterized by cloud computing and big data,the building approach of domain services become more complicated, which entitles theoptimization problem with new features such as the massiveness of requests andcandidate services, and constrained service capacity. Those features pose newchallenges to service composition optimization with respect to problem scale,optimization criteria and related constraints.
In the view of the defficiencies of existing methods in dealing with above problems,this paper presents a service pattern-based framework and related methods for solvingthe service composition optimization problem under domain environments with massiverequirements, with reference to the trend of requirement-service convergence exibitedby service engineering in solving domain problems. The main contribution of this thesisincludes the following aspects:
(1) Systematic service composition optimization framework and related concepts.Considering the relevance among different optimization levels and techniques, thispaper presents a systematic framework and related concepts for solving the servicecomposition optimization problem under domain environments with massiverequirements. In particular, the features of massive requirements are analyzied andcorresponding measures are provided. And then a service pattern-based optimizationframework is proposed, which divides the optimization process into three stages, i.e.,massive requiremetns-oriented service configuration, service pattern-based solutionspace reduction and service pattern-oriented composite service selection. The mainproblems of each stages are identified, and the overall optimization process is described.
(2) Massive requirements-oriented service configuration optimization. In the viewthat current research only focuss on single requriements, this paper extends the researchscope to scenario of massive requirements, and deal with the scenario by transformingthe problem specific to massive temporal sequential and concurrent requirements into that specific to groups of similar requirements. In particular, considering thespa-temporal relevance among the temporal sequential and concurrent massiverequirements, the requirements are segmented and clustered, so that they can share someoptimization results and the optimization efficiency can be improved. Correspondingmethods for reserving and distributing service capability are proposed so as to ensurethe equal distribution of limited service capability in both spatial and temporaldimensions. Considering differed emphases, scheduling-based and equivalence-basedservice capability distribution methods are provided, respectively.
(3) Service pattern-based solution space optimization for service composition. Thisstage utilize domain experiences in business and optimization process to reduce thesolution space of service composition, so as to simplify the optimization problem. Inparticular, the concept of service pattern is proposed to describe common processes indomain business. Both service patterns and related advantageous services arepre-computed and organized, so that they can be reused in optimization. To adapt todynamicity in available services, a method is proposed for updating the advantageousservices on-demand. To further improve optimization efficiency, a Bayesian-basedcandidate service reductionmethod is proposed to empiriclaly and probabilisticallyreduce problem scale.
(4) Service pattern-oriented composite service selection optimization. This stagefurther transform the optimization problems into single requirement-specific ones andsolve them based on previous stages’ outcomes, A candiate services filtering-basedmethod is proposed first to enable diversified and personalized solutions for differentrequirements within a same cluster. And then, different service selection strategies andmethods are proposed suitable for different scenarios, including uncertainty-basedservice selection strategy for key patterns, price-heuristic method for profit optimization,and improved artificial bee colony approach for dealing with massive candidateservices.
Finally, to verify the theretical research results, a service composition optimizationtool is designed. Based on case study on the procurement business of the Smart HomeServices (SHS), the proposed theories and methods are verified.
鉴于现有方法在应对以上问题的不足，结合当前服务工程在解决领域问题中呈现出需求与服务交汇的趋势，本文针对领域中大规模需求环境下服务组合优化问题，给出基于服务模式的解决框架及方法。该框架一方面深入刻画和组织领域中的大规模时序并发需求，通过限制不同方案的可用服务能力来优化配置领域服务，另一方面基于服务模式刻画领域业务和优化经验，用以组织候选服务和支持最终优化方案的快速形成。主要研究工作包括以下几个方面：
（1）系统化的服务组合优化框架及相关概念。考虑不同优化层面和手段间的关联，给出领域中大规模需求环境下服务组合优化问题的系统化解决框架及相关概念。具体地，对大规模领域需求的特征进行分析并给出相应的应对思路，提出基于服务模式的服务组合优化思路。进而，将领域中的服务组合优化过程分为面向大规模需求的服务配置、基于服务模式的服务组合方案空间缩减和支持服务模式构造的组合服务选择三个阶段，识别出各阶段的主要问题，并对整体优化过程进行了描述。
（2）大规模服务需求分析与服务优化配置。鉴于当前研究仅关注于单个需求的现状，将服务组合优化的研究范畴扩展到领域中大规模需求的场景，通过将针对大规模需求的服务组合优化问题转化为针对一组需求类的优化问题加以解决。具体地，考虑大规模时序并发需求的时空关联特性对服务需求进行分段聚类，使其共享部分优化结果以提高优化效率；提出相应的服务能力预留和分配方法，以保障有限服务能力在时空两个维度上分配的公平性；其中，考虑到对于服务收益和公平性的不同侧重，分别给出基于调度和基于均衡的服务能力分配方法。
（3）基于服务模式的服务组合方案空间优化。这一阶段主要利用领域业务和优化经验来缩减服务组合优化的方案空间，从而简化优化问题。具体地，提出服务模式的概念，对领域中的共性业务过程进行刻画；通过对服务模式及其相关优势服务进行组织和预提取，从而在优化过程中进行重用；为适应可用服务的变化，给出相关优势服务的按需更新方法；为进一步提高优化效率，给出基于贝叶斯的候选服务缩减方法，利用历史优化经验概率地减小优化问题规模。
（4）支持服务模式构造的服务组合优化选择。这一阶段基于上一阶段的优化结果将优化问题进一步转化到单个需求的层面加以解决。首先，为了保证聚类中的不同需求获得个性化的服务方案，提出候选服务过滤的方法以保证服务方案的多样化；而后，分别针对需要重点优化的关键模式、收益优化问题和海量候选服务的场景，提出面向组合服务执行过程不确定性的分阶段优化策略、基于价格启发的迭代优化方法和改进的人工蜂群算法，为不同场景下的优化问题得到最终的具体服务方案。
最后，为了验证理论研究成果，给出服务组合优化工具的设计思路，并结合智慧家庭采购业务实例，对本文提出的理论和方法进行应用验证。
With the development of information technology, traditional services graduallytransform into modern services. Modern services assume as domain-specific andcomplicated multi-party interactive processes, which are supported by advanced ITtechnology and hybrided with realistic ingredients. The profound revolution of modernservice industry has led to significant changes in the goal of services, i.e., from pursuingthe basic functionality and performance of services to focusing on the overall servicevalue characterized by service profit and customer satisfaction; and from pursuingsimple service value to seeking for integrated service value-added. The core issuetowards enabling this process is to conduct value-optimized service compositionsaccording to customers’ personalized requirements. However, under the trend ofservicitization and massive integration characterized by cloud computing and big data,the building approach of domain services become more complicated, which entitles theoptimization problem with new features such as the massiveness of requests andcandidate services, and constrained service capacity. Those features pose newchallenges to service composition optimization with respect to problem scale,optimization criteria and related constraints.
In the view of the defficiencies of existing methods in dealing with above problems,this paper presents a service pattern-based framework and related methods for solvingthe service composition optimization problem under domain environments with massiverequirements, with reference to the trend of requirement-service convergence exibitedby service engineering in solving domain problems. The main contribution of this thesisincludes the following aspects:
(1) Systematic service composition optimization framework and related concepts.Considering the relevance among different optimization levels and techniques, thispaper presents a systematic framework and related concepts for solving the servicecomposition optimization problem under domain environments with massiverequirements. In particular, the features of massive requirements are analyzied andcorresponding measures are provided. And then a service pattern-based optimizationframework is proposed, which divides the optimization process into three stages, i.e.,massive requiremetns-oriented service configuration, service pattern-based solutionspace reduction and service pattern-oriented composite service selection. The mainproblems of each stages are identified, and the overall optimization process is described.
(2) Massive requirements-oriented service configuration optimization. In the viewthat current research only focuss on single requriements, this paper extends the researchscope to scenario of massive requirements, and deal with the scenario by transformingthe problem specific to massive temporal sequential and concurrent requirements into that specific to groups of similar requirements. In particular, considering thespa-temporal relevance among the temporal sequential and concurrent massiverequirements, the requirements are segmented and clustered, so that they can share someoptimization results and the optimization efficiency can be improved. Correspondingmethods for reserving and distributing service capability are proposed so as to ensurethe equal distribution of limited service capability in both spatial and temporaldimensions. Considering differed emphases, scheduling-based and equivalence-basedservice capability distribution methods are provided, respectively.
(3) Service pattern-based solution space optimization for service composition. Thisstage utilize domain experiences in business and optimization process to reduce thesolution space of service composition, so as to simplify the optimization problem. Inparticular, the concept of service pattern is proposed to describe common processes indomain business. Both service patterns and related advantageous services arepre-computed and organized, so that they can be reused in optimization. To adapt todynamicity in available services, a method is proposed for updating the advantageousservices on-demand. To further improve optimization efficiency, a Bayesian-basedcandidate service reductionmethod is proposed to empiriclaly and probabilisticallyreduce problem scale.
(4) Service pattern-oriented composite service selection optimization. This stagefurther transform the optimization problems into single requirement-specific ones andsolve them based on previous stages’ outcomes, A candiate services filtering-basedmethod is proposed first to enable diversified and personalized solutions for differentrequirements within a same cluster. And then, different service selection strategies andmethods are proposed suitable for different scenarios, including uncertainty-basedservice selection strategy for key patterns, price-heuristic method for profit optimization,and improved artificial bee colony approach for dealing with massive candidateservices.
Finally, to verify the theretical research results, a service composition optimizationtool is designed. Based on case study on the procurement business of the Smart HomeServices (SHS), the proposed theories and methods are verified.
引文
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