Research Collaboration and Knowledge Transfer
As outlined in our Research page, we have extensive experience in collaboration with industrial, academic and research institutions worldwide. We have a successful track record with EPSRC, TSB, EU and JISC; and directly with the research arm of institutions.
Therefore, we welcome researchers joining us via a variety of routes – as a research partner, a visiting researcher, a postgraduate student, or an intern.
Postgraduate Research Students
Specific funding or career opportunities will be announced on the school’s main funding opportunities page with accompanying information. Current PhD supervisors are: Professor Jie Xu and Professor Karim Djemame.
There are many opportunities for PhD research within our group. A selection of examples can be seen below, please also feel free to contact the group to discuss your own interests.
- Cloud Data Analysis
To date, there has been no widely-used analysis of real Cloud usage. This project seeks to use distributed data processing technology (such as Hadoop) – performing on a large cluster of machines at the University of Leeds – to mine and analyse multiple sets of cloud datacentre data (such as that released by Google) in order to generate meaningful statistics and conclusions that will direct future research into real-world Cloud performance, dependability, and energy usage. These conclusions will then be further tested and analysed on a test cloud developed at the University of Leeds.
- Risk Management in Clouds
The objective of the research is to identify the need for risk management at various stages of the cloud service lifecycle (construction, deployment, and operation). It emphasizes on the design of and implementation of various risk models used by different cloud architectural components throughout the service lifecycle. It aims towards optimized service construction, deployment, and execution for Cloud Infrastructures by offering risk management tools to efficiently manage the full life cycle of services. These tools provide simplified construction of services, help make informed deployment and runtime management decisions based on risk assessment models for evaluation of providers, and permit the appropriate establishment of fault tolerance mechanisms.
- Energy Efficient Cloud Computing
There has been considerable effort in the Cloud computing industry to reduce costs through the use of energy efficient hardware (cooling, power-saving, etc.) However, an area that has been mostly overlooked is how energy can also be saved through intelligent scheduling of Cloud jobs. This project aims to analyse existing cloud infrastructures and scheduling algorithms in order to improve their energy efficiency; simplex and intercloud environments will be considered, and real hardware monitors will be used to evaluate the effectiveness of the generated schemes.
- Elastic Real-Time Applications in Cloud Environments
There has been an increased effort in developing and integrating Real-Time applications into the Cloud environment (streaming, hosted servers for online gaming, etc.) However, designing and constructing Real-Time applications that leverage Cloud elasticity that are capable of enforcing timing constraints in the presence of a turbulent and unpredictable Cloud system environment is not trivial. This project seeks to formalise such a design in order to enable the development of Real-Time Cloud applications that are able to cope and leverage the characteristics of the Cloud computing environment.
- Definition of a Methodology to Develop High Scalable Applications Using Commercial Cloud Frameworks
One of the biggest advantages of Cloud Computing over other computational models is that it provides high scalability on demand. However, architecting applications to use this Cloud characteristic is not trivial. It requires a deep understanding of the Cloud computing model and also of the framework supplied by Cloud providers. There is a lack of well-defined methodologies to support system architects in order to exploit the available framework tools to achieve high scalability with the minimum effort. This project seeks to develop and formalise such a method in order to allow consumers to make a more efficient use of resources and improve required performance under growing demand.
- Making Sense of User Generated Content in a Collaborative Environment
Social media and cloud platforms are increasingly being used for collaboration. The amount of content and usage data generated in these environments is huge. This poses both challenges and opportunities. These user generated content provided a rich set of captured knowledge for behaviour analyses. Due to the size of data, accuracy and scalability of analysis are some of the issues and require further research. At Leeds, we have embarked on a model-driven approach which utilises semantics as a means of representation for further reasoning to make sense of the underlying patterns. Our research projects have utilised models from Activity Theory, individual and collaborative sense making models, and provenance models depending on the context. Algorithms are being experimented to streamline the pipeline from data/knowledge capture to representation within a collaborative setting.
- Byzantine Fault-Tolerance in Clouds
Computing Clouds are typically composed of a large number of physical and virtual machines, capable of supporting a large number of often highly elastic applications from a great number of users. In this environment, it is important to guard against the occurrence of Byzantine faults – arbitrary faults introduced into the system by malicious entities. This project aims to develop technologies to detect Byzantine faults and allow cloud jobs to continue to provide correct service in the presence of such faults. It will involve a rigorous examination of the existing Byzantine Fault-Tolerant (BFT) techniques used in traditional distributed systems and development, deployment and evaluation of a new algorithm to support BFT in real Clouds.
General advice for research degrees applications can be found on the school of computing’s main pages.
- Visiting Professor Chaug Guo – NUDT, China
- Visiting Professor Kaigui Wu – Chongqing University, China
- Visiting student Shengdong Zhang – NUDT, China
Past Research (Alumni’s Theses)
Our group has had many successful alumni, who have produced their theses here at Leeds. We highlight their success here, while other theses produced in the school can be found under the main school listing.
- Bernhard Josef Primas (PhD) Modeling and Algorithmic Development for Selected Real-World Optimization Problems with Hard-to-Model Features
- Ahmad Kamal Bin Ramli (PhD) Service Level Agreement-based adaptation management for Internet Service Provider (ISP) using Fuzzy Q-learning
- Ouyang, Xue (PhD) – Intelligent Straggler Mitigation in Massive-Scale Computing Systems
- Abdulaziz Albatli (PhD) – Provenance-Driven Diagnostic Framework for Task Evictions Mitigating Strategy in Cloud Computing
- Ibrahim Alzamil (PhD) – Energy-Aware Profiling and Prediction Modelling of Virtual Machines in Cloud Computing Environments
- David McKee (PhD) – n-Dimensional Prediction of RT-SOA QoS
- Peter Garraghan (PhD) – Holistic Cloud Computing Environmental Quantification and Behavioural Analysis
- Silvana De Gyves Avila (PhD) – QoS Awareness and Adaptation in Service Composition
- Ismael Solis Moreno (PhD) – Characterizing and exploiting heterogeneity for enhancing energy-efficiency of cloud datacenters
- Anthony John Sargeant (PhD) – Testing the Dependability of Dynamic Binding in Service-Oriented Computing
- Richard Edward Kavanagh (PhD) – Negotiated Resource Brokering for Quality of Service Provision of Grid Applications
- Tahir Farooq (PhD) – Protection and Sharing of Semantically-Enabled ELN in a Co-Laboratory Research Environment Focused on the EUROCHAMP-2 Community
- Django Armstrong (PhD) – Enhancing Quality of Service in Cloud Computing Through Novel Resource Management
- Sanaa Sharaf (PhD) – Extending WS-agreement to Support Dynamic Service Level Agreements in Grids
- Asif Sangrasi (PhD) – Component Level Risk Assessment Modelling for Grid Resources
- Shahzad Ahmed Nizamani (PhD) – A quality-aware cloud selection service for computational modellers
- Zulkifly Mohd Zaki (PhD) – A User-Orientated Electronic Laboratory Notebook for the Retrieval of Scientific Provenance Grounded in EUROCHAMP-2 Community
- Siraya Sitthisarn (PhD) – Semantically-enabled Keyword Search for Expert Witness Discovery Applied to a Legal Professional Network
- Raid Abdullah Alsoghayer (PhD) – Risk assessment models for resource failure in grid computing
- Junaid Arshad (PhD) – Context-aware intrusion severity analysis for clouds
- Muhammad Mubashir Khan (PhD) – High error-rate quantum key distribution: novel protocols with improved eavesdropping detection
- Sania Bhatti (PhD) – Clustering and fault tolerance for target tracking using wireless sensor networks
- Imran Ali Jokhio (PhD) – A Scalable Scheme for Enhancing EPC Network Security
- Simon Mark Davy (PhD) – Decentralised Economic Resource Allocation For Computational Grids
- James Joseph Padgett (PhD) – A Management System for Service level Agreements in Grid based Systems
- Tran Vu Pham (PhD) – A Collaborative e-Science Architecture for Distributed Scientific Communities
- Mohammed Hassan Haji (PhD) – SNAP-based Grid Resource Broker using the Three Phase Commit Protocol
- Paul Michael Townend (PhD) – Topology-aware fault tolerance in grids