My research is mainly in the domain of real-time embedded systems (RTES), cyber-physical systems (CPS) and autonomous systems (AS). The vision of my research is to work towards adaptive, safe and predictable engineering systems. My main research topics are:
- Real-time systems, including scheduling and allocation on single- and multi-cores, and hardware that is timing predictable;
- Flexible and feedback-based methods for task scheduling in CPS and AS;
- Control-scheduling co-design and design automation of CPS;
- Using Digital Twins for design, verification and optimization of RTES and CPS;
- Network scheduling, from network-on-chip to transportation traffic scheduling;
- Assuring timing and safety of robotic and autonomous systems.
[Collaborations] I am working with academics and companies from inside the UK, US, France, Germany, Portugal and China (mainland, Taiwan and HongKong), and I am open to collaborations related to these topics and fields, addressing open problems and grand challenges both from industry and academia, with a focus on timing and scheduling, and applications in real-time embedded systems, cyber-physical systems and autonomous systems.
➤ HICLASS: High-Integrity, Complex, Large, Software And Electronic Systems
(2023 - ) Research Associate, University of York
*Funded by UKRI
HICLASS is a project to enable the delivery of the most complex software-intensive, safe and cyber-secure systems in the world.
The project is a strategic initiative to drive new technologies and best-practice throughout the UK aerospace supply chain, enabling the UK to affordably develop systems for the growing aircraft and avionics market expected over the next decades. It includes key primes, system suppliers, software companies and universities working together to meet the challenge of growing system complexity and size.
HICLASS will allow development of new, complex, intelligent and internet-connected electronic products, safe and secure from cyber-attack that can be affordably certified.
➤ MOCHA: Modelling and Optimising Complex Heterogenous Architectures
(2019 - 2022) Research Associate, University of York
Funded by Huawei Technologies Co. Ltd, £985,927
The applications and resources (processors, networks and memory) for real-time systems are becoming ever more complex to understand, control and maintain. This has led to research into building statistical models of systems and adaptive policies based on these statistical models.
The key challenges that emerge are whether the models reflect how the system would behave during operation, how systems should deal with unexpected or rarely occurring scenarios, and then how to optimise systems based on the behaviours of the systems. It is specifically to address the high overheads of current systems and the low cache hit rates that are currently achieved.
For more information: [Project Website]
➤ DEIS: Dependability Engineering Innovation for CPS
(2018 - 2019) Research Associate, University of York
Funded by EU Horizon 2020, €4.9M
Cyber-Physical-Systems (CPS) provide the potential for vast economic and societal impact in domains such as automotive, health care and home automation. The open and cooperative nature of CPS poses a significant new challenge in assuring dependability. The DEIS project addresses this important and unsolved challenge by developing technologies that enable a science of dependable system integration. Such technologies facilitate the efficient synthesis of components and systems based on their dependability information. The key innovation in the approach of the DEIS project is the concept of Digital Dependability Identity (DDI). A DDI contains all the information that uniquely describes the dependability characteristics of a CPS component. DDIs are used for the integration of components into systems during development as well as for the dynamic integration of systems into systems of systems in the field.
➤ ATAS: Adaptive Task Scheduling Framework for CPS
(2015 - 2018) PhD Research Student, University of York
In a Cyber-Physical Control System (CPCS), there is often a hybrid of hard real-time tasks which have stringent timing requirements and soft real-time tasks that are computationally intensive. The task scheduling of such systems is challenging and requires flexible schemes that can meet the timing requirements without being over-conservative.
In this study, an adaptive real-time scheduling framework for CPCS is presented. The adaptive scheduler has a hierarchical structure and it is built on top of a traditional FPS scheduler. The idea of dynamic worst-case execution time is introduced and its cause and methods to identify the existence of a trend are discussed. An adaptation method that uses monitored statistical information to update control task periods is then introduced. Finally, this method is extended by proposing a dual-period model that can switch between multiple operational modes at run-time. The proposed framework can be potentially extended in many aspects and some of these are discussed in the future work. All proposals of this thesis are supported by extensive analysis and evaluations.
Research projects that I am informally involved:
- dag-gen-rnd: A Randomized Multi-DAG Task Generator for Scheduling and Allocation Research
- DAG Scheduling Simulator on Multiprocessor Systems
- Line-Circle-Square (LCS): A Multilayered Geometric Filter for Edge-Based Detection