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

该项目专注于服务计算的性能分析和优化算法。服务计算是IT和企业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。在过去的十年中，企业通过面向服务的概念接受了服务计算，这是一种将业务功能组织到独立服务中的设计模式。由于业务流程建模(如BPMN)和执行(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的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层次化的增长，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其具有挑战性。由于资源竞争、参数设计时的不确定性以及潜在的人工参与，解决方案对执行时间和成本变化的敏感性也很难理解。该项目将首先开发一个从高级工作流规范(如BPMN)到分层排队网络(LQN)模型的模型到模型的转换，该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。一旦形成LQN形式，将使用在项目内开发的新的随机分析技术来分析设计计划。这些技术将是该项目的主要科学创新，将首次将矩阵分析方法(MAM)应用于LQN分析。MAM是排队分析技术，允许描述复杂的排队系统，其中资源处的服务可以分阶段发展，类似于支撑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。非常令人惊讶的是，就我们所知，MAM技术从未被应用于服务计算中的性能分析问题。将MAM应用于LQN的目的是提高面向服务的系统设计的单个设计方案评估的效率和准确性，提供一种高效而准确的分析方法，该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.