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

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.

The project has both academic and industrial beneficiaries.

Academic beneficiaries

- This research will directly lead to production of knowledge in the areas of service-oriented computing, performance evaluation, optimisation and applied probability. These would benefit the academic communities working in these fields.
- The project will also contribute to the training of the RA on service-oriented systems and BPMN modelling. These technologies are widely popular on the job market and could help the RA acquire a research position in industry after the end of the project. We plan to involve the RA in both research and dissemination activities, particularly through presentations in conferences or seminars.
- We will also organise 3 invited seminars at Imperial College London with invited experts from EU on service-oriented systems and matrix geometric methods. We will advertise the seminars internally and to targeted colleagues in UK and Europe, particularly in across the performance engineering community where the PI has an established network.

Industrial beneficiaries

- Our research could impact on the optimisation of service-oriented architectures and business processes in industry, thus promoting in the medium term heightened business efficiency and performance. There is a strong demand for such business process management solutions in the UK and internationally.
- Project partner is BOC, a company leader in service-oriented architectures, business process management and related software modelling technologies. BOC's main product is ADONIS, a business process analysis tool for analysis, simulation and evaluation of BPMN models. The tool is adopted in several companies in UK and EU for service-oriented system modelling. As stated in the support letter, BOC is willing to examine the project output for possible integration of our open source algorithms (as external royalty-free libraries) in ADONIS.
- Since we plan to release our results as open source, any company could integrate our results in a business process management or service engineering solution. Contributing to business process management would impact on operational and organisational change. BPMN models can describe various forms of human activities and therefore tools to optimise process structure would immediately foster better organisations. Conversely, contributing to service engineering would mainly impact on technological advancement of services computing.
- Beyond the area of service-oriented systems, the project could benefit the design of component-based software. This is because Line is integrated with the Palladio framework developed by KIT, a popular environment in the model-driven engineering community. This would benefit software architects and process engineers.

Further details, including dissemination and communication plans, are given in the Pathways to impact document.