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

OptiMAM：通过随机分析方法优化模型驱动的服务设计

基本信息

批准号：
EP/M009211/1
负责人：
Giuliano Casale
金额：
$ 12.52万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FM009211%2F1
关键词：
OptiMAM Optimising Model Driven Service

项目摘要

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.

该项目着重于服务计算的性能分析和优化算法。服务计算是IT与业务管理之间界面的跨学科领域，旨在最大程度地提高IT服务技术的业务效率。在过去的十年中，企业通过服务定位概念接受了服务计算，这是一种设计模式，将业务功能组织到独立服务中。现在，由于Web服务技术和语言的进步业务流程建模（例如BPMN）及其执行（例如WS-BPEL）。随着服务的复杂性和分层的增长，建立正确的操作时间表以及资源，活动和服务之间的最佳依赖性变得极为挑战。也很难理解发现解决方案对执行时间和成本变化的敏感性，由于资源争议，该参数的设计时间不确定性以及人类在过程中的潜在参与而不可避免。分析。 LQN允许数学分析资源，服务和流程之间的关系。一旦以LQN形式，将使用新的随机分析技术在项目中开发的新设计计划进行分析。这种技术将是该项目的主要科学创新，将首次适用于矩阵分析方法（MAM）进行LQN分析。 MAM是排队分析技术，允许描述复杂的排队系统，其中资源的服务可以分阶段发展，与工作流程中使用的业务流程和面向服务的体系结构中使用的活动顺序相似。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的目的是提高对面向服务的系统设计评估单个设计方案的效率和准确性，从而提供了一种有效而准确的分析方法，可以与搜索目的进行优化。