My research has been on Internet-Based Distributed Computing Systems but not limited to, Cloud Computing, Service Computing and Self-Adaptive Systems. I am particularly interested in engineering Software-Intensive (Autonomous ) Distributed Systems, understanding, improving and assessing their run-time behaviour from performance (QoS) and economic-driven perspectives, and applying them in Cloud-Based IoT Systems, Cyber-Physical Systems and Service-Oriented Systems. Most of my work leverages Computational Intelligence, Machine Learning and Economic Theory to tackle these problems..Apart from that my PhD research work has been published in the top conferences such as IEEE ICWS, IEEE/ACM SEAMS and IEEE ICPADS etc.

Service Composition in SaaS Cloud using Technical Debt Analysis

Service composition is a technique for building software application by composing web services in SaaS Cloud. However, a software application running in SaaS cloud would inevitably operate under dynamic changes on the workload from the tenants, and thus it is not uncommon for the composition to encounter under-utilisation and over-utilisation on the component services. The former reduces the service revenue and the latter, in contrast to the over-utilisation that leads to QoS constraints violation. In fact, both cases are undesirable, and bring a challenging task: when to (re)compose the component services such that the utility over time is maximised ?.

In this project, we take advantage of "Technical Debt" metaphor for making economic-driven decisions on the selection and composition of component services in the composite service execution. In particular, the ultimate goal of this research work is to create economic-driven approaches for understanding, improving and assessing the run-time behaviour of composed software application and maximise the service revenue and satisfaction of tenants in SaaS cloud. For more information, please read C2-IEEE ICWS-2019 and C3-IEEE/ACM SEAMS-2020 research papers.