Dr. Tao Chen

Research Fellow at the School of Computer Science, University of Birmingham, UK

I am currently a Research Fellow, working with Prof. Xin Yao, at the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA) in the School of Computer Science at the University of Birmingham. I obtained my Ph.D. form the University of Birmingham, on the topic of Self-Aware and Self-Adaptive Autoscaling for Cloud-Based Services, and under the supervision of Dr. Rami Bahsoon. Prior to that, I worked as a Software Engnieer in London. I also hold a M.Sc. (Distinction) on Internet Software Systems from University of Birmingham, and before that, I read for a B.Sc. (First Class) in Computer Science at Birmingham City University. My CV can be found at here.


  • 01/07/2017: I will serve as a PC member for the Cooperative Systems Track, ACM SIGAPP Symposium On Applied Computing, 2017.
  • 28/03/2017: I will serve as a Track Chair and PC member for IEEE World Congress on Services, 2017.
  • 17/11/2016: I gave a talk on our work on embedding computational intelligence into self-adaptive software systems at the MetaLab, School of Computing and Communication, Lancaster University. Thanks for the invitation and all the fruitful discussions!
  • 30/09/2016: Our paper entitled "Bridging Ecology and Cloud: Transposing Ecological Perspective to Enable Better Cloud Autoscaling" has been accepted as a chapter in the book of Software Architecture for Big Data and the Cloud (SABDC). Congratulations!
  • 06/09/2016: Our paper entitled "Self-Adaptive and Online QoS Modeling for Cloud-Based Software Services" has been accepted as a Regular Paper in an upcoming issue of the Transactions on Software Engineering. Congratulations!
  • 08/06/2016: Our new book, "Self-Aware Computing Systems", which is one of the significant outcomes of the EPiCS project, is now released. Congratulations!
  • 20/11/2015: I will join Prof. Xin Yao's group, the Centre of Excellence for Research in Computational Intelligence and Applications, at the School of Computer Science, University of Birmingham.


My research has been on Software Engineering, including but not limited to, performance engineering, search-based software engineering, cloud computing and services computing. I am particularly interested in engineering Self-Adaptive Software Systems, understanding their runtime behaviours and applying them in cloud-based, service-oriented and distributed environment. Most of my work leverages Natural Computation, Machine Learning and Economic Theory to tackle the problems (please see my publications for details). Specifically, my research has been related to the following aspects (especially at runtime):

System and Software Modelling

  • How to model the correlation between non-functional attributes/QoS and the underlying control variables (i.e., software and hardware configurations) and the environment.
  • How to study and mitigate the performance interference (in virtualized environment) caused by contentions.
  • How to model the pay-off after adaptations under certain circumstance.


  • What is the optimal level and number of feedback loops.
  • What is the best degree of centrality.
  • What is the most appropriate granularity of control.
  • Which architectural pattern to use.

Decision Making and Planning

  • How to conduct runtime reasoning about the effects of decision/adaptations on goals.
  • How to resolve the possible trade-offs between conflicted non-functional attributes.
  • How to tame environmental and requirement uncertainty when making adaptation decision.
  • When to change and adapt the software system.


Journal papers

T. Chen and R. Bahsoon. Self-Adaptive and Online QoS Modeling for Cloud-Based Software Services, IEEE Transactions on Software Engineering, vol. 43, no. 5, 2017. [PDF]

T. Chen and R. Bahsoon , Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services. IEEE Transactions on Services Computing, 2015, doi:10.1109/TSC.2015.2499770. [PDF]

T. Chen and R. Bahsoon. Towards A Smarter Cloud: Self-Aware Autoscaling of Cloud Configurations and Resources. IEEE Computer, vol. 48, no. 9, 2015. [PDF]

P.R. Lewis, A. Chandra, F. Faniyi, K. Glette, T. Chen, R. Bahsoon, J. Torresen and X. Yao. Architectural Aspects of Self-Aware and Self-Expressive Computing Systems: From Psychology to Engineering IEEE Computer, vol. 48, no. 8, 2015

T. Chen, R. Bahsoon and A. Tawil. Scalable Service Oriented Replication with Flexible Consistency Guarantee in the Cloud. Information Science, Elsevier, 2014. [PDF]

Book chapters

T. Chen, F. Faniyi, and R. Bahsoon. Design Patterns and Primitives: Introduction of Components and Patterns for Self-Aware Computing Systems, in the book of Self-Aware Computing Systems, Springer, 2016.

Conference papers

T. Chen, R. Bahsoon and X. Yao. Online QoS Modeling in the Cloud: A Hybrid and Adaptive Multi-Learners Approach. The 7th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2014), London, UK. 2014. [PDF]

T. Chen and R. Bahsoon. Symbiotic and Sensitivity-Aware Architecture for Globally-Optimal Benefit in Self-Adaptive Cloud. The 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS in conjunction with the 36th International Conference on Software Engineering (ICSE), India, 2014. [PDF]

T. Chen and R. Bahsoon. Self-Adaptive and Sensitivity-Aware QoS Modelling for the Cloud. The 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS in conjunction with the 35th International Conference on Software Engineering (ICSE), San Francisco, CA, 2013. [PDF]

T. Chen, R. Bahsoon and G. Theodoropoulos. A Decentralized Architecture for Dynamic QoS Optimization in Cloud-based DDDAS. 2013 International Conference on Computational Science, Procedia of Computer Science, Elsevier Science, 2013. [PDF]

T. Chen and R. Bahsoon. Scalable Service-Oriented Replication in the Cloud. In the Proceedings of 4th IEEE Cloud Conference 2011, Washington D.C., USA, 2011.

T. Chen, F. Faniyi, R. Bahsoon, P.R. Lewis, X. Yao, L.L. Minku, and L. Esterle. The Handbook of Engineering Self-Aware and Self-Expressive Systems. Technical Report, arXiv:1409.1793 [cs.SE], 2014.

Ph.D. Thesis

T. Chen. Self-Aware and Self-Adaptive Autoscaling for Cloud Based Services. School of Computer Science, University of Birmingham, 2016. [PDF]


I have been involved in teaching of the following modules:

  • (MSc) Software Engineering II, 2014-2015
  • (BSc) Fundamentals on Software Engineering, 2014-2015
  • (MSc) Software Engineering II, 2013-2014
  • (BSc) Fundamentals on Software Engineering, 2013-2014

Past and Current Research Projects

I have been involved in various research projects:

Dynamic Adaptive Automated Software Engineering (DAASE), funded by EPSRC, 2012-2018.

Current software development processes are expensive, laborious and error prone. They achieve adaptivity at only a glacial pace, largely through enormous human effort, forcing highly skilled engineers to waste significant time adapting many tedious implementation details. Often, the resulting software is equally inflexible, forcing users to also rely on their innate human adaptivity to find "workarounds". Yet software is one of the most inherently flexible engineering materials with which we have worked, DAASE seeks to use computational search as an overall approach to achieve the software's full potential for flexibility and adaptivity. In so-doing we will be creating new ways to develop and deploy software. This is the new approach to software engineering DAASE seeks to create. It places computational search at the heart of the processes and products it creates and embeds adaptivity into both. DAASE will also create an array of new processes, methods, techniques and tools for a new kind of software engineering, radically transforming the theory and practice of software engineering.

My role in the project lies in the theme of architecture optimization for self-adaptive software. The aim is to investigate advanced approach to optimise non-functional attributes of self-adaptive software at runtime, handling dynamics and uncertainty in the environment and given requirements, which in turn, leading to better and stable adaptations.

Self-Aware and Self-Adaptive Autoscaling for Cloud-Based Services, Ph.D. thesis, funded by School of Computer Science at University of Birmingham, 2012-2016.

My Ph.D. work focuses on designing self-aware and self-adaptive autoscaling system for cloud-based services where the aim is to dynamically optimise QoS and cost of all services running in the cloud, leading to better compliance of the requirements. The main contribution is an autoscaling framework that is capable to 'learn' about its own states and the environment, and adapt itself accordingly. Through using the principle of self-awareness and the related computational intelligence, autoscaling is achieved without heavy human analysis/intervention and require very limited design time knowledge.

The thesis has addressed specific research questions related to dynamic modeling of non-functional attributes, self- adaptive architecture and trade-off decision making under uncertainty, which can attract interests in the broad category of self-adaptive software systems.

Ecology Inspired Self-Aware Autoscaling Supporting Elastic Cloud-Based Services, funded by Ramsay Research Funding Schema from School of Computer Science at University of Birmingham, 2015.

The project studies the bridging between ecology and cloud computing, aiming to improve stability and sustainability of cloud-based services, using ecological theory and models. I was the named researcher for the project, in which I was responsible for identifying the benefits of ecological principles for cloud computing; formulating and modeled the services in cloud using ecological theory and analogue; and proposing architecture for ecological cloud computing.

Engineering Proprioception in Computing Systems (EPiCS), FP7-ICT-2009-5, funded by European Union 7th Framework Programme, 2010-2014.

EPiCS is a trans-national multi-disciplinary research project which aims at laying the foundation for engineering the novel class of proprioceptive computing systems.

Proprioceptive computing systems collect and maintain information about their state and progress, which enables self-awareness by reasoning about their behaviour, and self-expression by effectively and autonomously adapting their behaviour to changing conditions. Concepts of self-awareness and self-expression are new to the domains of computing and networking; the successful transfer and development of these concepts will help create future heterogeneous and distributed systems capable of efficiently responding to a multitude of requirements with respect to functionality and flexibility, performance, resource usage and costs, reliability and safety, and security.

My role in the project was to lead the task for architecting self-aware systems using self-aware patterns; categorised the architectural primitives for self-aware systems; and finally, constructed a comprehensive methodology to assist software engineers in developing self-aware systems.

Least Cost Fulfilment, funded by EPSRC KTS (Knowledge Transfer Secondments), 2013
A company is looking to deploy their optimization system into cloud with an aim to improve performance while reducing cost. I was involved in the task for modeling performance of the cloud-based application, the models can then assisted in determining the demand of cloud resources while minimising the cost.

Professional Service

I served/serving as an editorial board member/PC member/reviwer for the following international conferences and journals:

  • Journal of Systems and Software
  • Information Sciences
  • Services Transactions on Internet of Things (STIOT)
  • IEEE Services 2017
  • S2 International Conference on Internet of Things, 2016
  • IEEE Services 2016 Emerging Track: Big Data Software Engineering for Cloud, Edge Computing and Mobility
  • International Conference on Services Computing, 2015
  • European Conference on Software Architecture, 2015
  • International Conference on Services Computing, 2014

Contact Me

Room 244,
School of Computer Science,
University of Birmingham,
B15 2TT

Email: t.chen [at] cs [dot] bham [dot] ac [dot] uk
Website: www.cs.bham.ac.uk/~chentx
Telephone: +44 (0)121 414 3734

Dr. Tao Chen