OptiMAM: Optimising Model-Driven Service Design via Stochastic Analysis Methods

该项目侧重于服务计算的性能分析和优化算法。服务计算是IT和业务管理之间的一个跨学科领域，旨在最大限度地提高IT服务技术的业务效率。在过去十年中，企业通过面向服务的概念（一种将业务功能组织为自包含服务的设计模式）接受了服务计算。由于用于业务流程建模（例如，BPMN）及其执行（例如，WS-BPEL）的web服务技术和语言的进步，面向服务现在很常见。尽管面向服务的体系结构和业务流程管理已被广泛采用，但优化底层活动工作流设计的能力在计算上仍然具有挑战性。随着服务的复杂性和分层的增长，建立正确的操作计划以及资源、活动和服务之间的最佳依赖关系变得极具挑战性。很难理解解决方案对执行时间和成本变化的敏感性，这是不可避免的，因为资源争用、参数的设计时不确定性以及过程中潜在的人类参与。该项目将首先开发从高级工作流规范（例如BPMN）到分层排队网络（LQN）模型的模型到模型的转换，分层排队网络（LQN）模型是一类用于性能分析的随机模型。lqn允许对资源、服务和流程之间的关系进行数学分析。一旦进入LQN形式，设计计划将使用在项目中开发的新的随机分析技术进行分析。这些技术将首次应用于LQN分析，是该项目的主要科学创新。MAM是允许描述复杂排队系统的排队分析技术，其中资源服务可以分阶段发展，类似于在支持业务流程和面向服务的体系结构的工作流中使用的活动序列。相当令人惊讶的是，据我们所知，MAM技术从未应用于服务计算中的性能分析问题。将MAM应用于lqn的目标是提高面向服务的系统设计的单个设计场景评估的效率和准确性，提供一种高效和准确的分析方法，可以与搜索目的的优化相结合。

The project focuses on performance analysis and optimisation algorithms for Services Computing. Services Computing is a inter-disciplinary area at the interface between IT and business management that aims at maximising the business efficiency of IT service technologies. Over the last decade, enterprises have embraced services computing through the notion of service-orientation, a design pattern where business functions are organised into self-contained services. Service-orientation is now common, thanks to advancements in web services technologies and languages for business process modelling (e.g., BPMN) and their execution (e.g., WS-BPEL).Although service-oriented architectures and business process management have been extensively adopted, the ability to optimise the design of the underlying activity workflows remains computationally challenging. As the complexity and layering of services grows, it becomes extremely challenging to establish the correct schedule of operations and the optimal dependencies between resources, activities and services. It is also difficult to understand the sensitivity of the solutions found to variability in execution times and costs, which are unavoidable due to resource contention, design-time uncertainty over the parameters, and potential involvement of humans in the processes.The project will first develop a model-to-model transformation from a high-level workflow specification (e.g., BPMN) to layered queueing network (LQN) models, a class of stochastic models used for performance analysis. LQNs allow to mathematically analyse the relationships between resources, services and processes. Once in LQN form, the design plan will be analyzed using new stochastic analysis techniques to be developed within the project. Such techniques, which will be the main scientific innovation of the project, will apply for the first time matrix-analytic methods (MAM) to LQN analysis. MAM are queueing analysis techniques that allow to describe complex queueing systems, where service at resources can evolve in phases, similarly to the sequence of activities that are used in the workflows underpinning business processes and service-oriented architectures. Quite surprisingly, MAM techniques have never been applied to performance analysis problems in services computing, to the best of our knowledge. The goal of applying MAM to LQNs is to increase both the efficiency and accuracy of the evaluation of a single design scenario for a service-oriented system design, providing an efficient and accurate analysis method that can be coupled with optimisation for search purposes.