- Dr Maolin Tang
- Senior Lecturer
Science and Engineering Faculty,
Electrical Engineering, Computer Science,
- Discipline *
- Artificial Intelligence and Image Processing, Distributed Computing
- +61 7 3138 5225
- View location details (QUT staff and student access only)
- Identifiers and profiles
PhD (Engineering) (Edith Cowan University), Master of Engineering (Computer Science) (Chongqing University), Bachelor of Engineering (Computer Software) (Huazhong University of Science and Engineering)
Evolutionary Computation, Cloud Computing, Genetic Algorithm, Computational Intelligence, Web Service Composition, Software as a Service (SaaS)
Research discipline:Data Science
(Prospective PhD students who are interested in any of the following research topics may contact me at firstname.lastname@example.org)
- Big Data Cloud Computing
Big data analysis and processing usually requires a large pool of computation resources. Cloud computing is a powerful computation technology to perform massive-scale and complex computing. It eliminates the need for maintaining expensive computing hardware, dedicated space, and software. Big Data Cloud Computing investigates how to make use of the powerful computation resources of cloud computing to store, analyses and process big data. Dr. Tang’s current research on Big Data Cloud Computing is focusing on Cloud-based MapReduce computation. MapReduce is a parallel programming model for processing large data sets, and it has been widely applied in many commercial and scientific applications, such as data mining, bioinformatics, machine learning, and web indexing. MapReduce was originally proposed for parallel computation in a cluster which consists of a set of loosely or tightly connected computers. However, when MapReduce is implemented as a service in cloud computing there are many new issues that need to be addressed. The first and most important one is the Quality of Service (QoS) of the cloud-based MapReduce service. The ultimate objective of the research is to develop a cloud-based MapReduce computation service that can be used by multiple users simultaneously and can dynamically scale to adapt the dynamically changing computation environment of cloud computing to guarantee its QoS.
- Energy-efficient Data Centres
The energy consumption of IT equipment in data centres is doubled while the number of transistors that can be integrated in an IC chip is doubled every 18-24 months. As a result, the energy consumption of the IT equipment, including computers and communication devices, in data centres are increasing dramatically. Moreover, when the energy consumption of the IT equipment in a data centre increases by one kilowatt, the energy consumed by the non-IT equipment in the data centre will increase by 2.3 kilowatts. Thus, the energy consumption of data centres has been increasing dramatically in the past years. According to Koomey, data centres comprised 1.3% of the global energy usage in 2010. Australian data centres were responsible for 2-3 billion kWh of electricity or 1.5% of Australia’s total annual energy consumption in 2009. At this scale, even relatively modest energy consumption reduction in the IT equipment in data centres will yield significant savings in operational costs and will reduce millions of tons of carbon emissions. Thus, this research proposal has not only economic impact, but also environmental impact. According to Amazon’s estimations, the energy-related costs at its data centres account for 42% of the total operational costs. The energy consumption in data centres can be broken down into two components: the energy consumed by IT equipment, such as computers and communication devices, and the energy consumed by non-IT equipment, such as cooling equipment, UPS, and power distribution units (PDUs). The energy consumed by the IT equipment in data centres is responsible for about 30% of the total energy consumption in data centres. Thus, minimising the total energy consumptions of the IT equipment in data centres is of paramount importance. This research aims to develop and demonstrate a new framework that can minimise the total energy consumption of the IT equipment in data centres.
- QoS-Aware Web Service Composition and Optimisation
This project is a part of a CRC project, ‘Service Aggregation’, which is funded by CRC for Smart Services.
In this project we investigate how to make use of existing Web services to build a new value-added Web service that satisfies QoS requirements and constraints. We also study new techniques for composite Web service optimisation.
- Optimisation of SaaS Delivery in the Cloud
This project investigates new methods and techniques for optimising SaaS (Software as a Service) delivery in the cloud.
The research problems include, but are not limited to, how to improve SaaS delivery in the cloud:
- by optimising the deployment of SaaS
- by optimising the allocation of SaaS
- by dynamically managing SaaS.
Teaching discipline:Data Science
- I am currently teaching CAB301 Algorithms and Complexity in Semester 1 and CAB403 Systems Programming in Semester 2.
- Xu X, Tang M, Tian GY, (2018) Theoretical results of QoS-guaranteed resource scaling for cloud-based MapReduce, IEEE Transactions on Cloud Computing p879-889
- Vasudevan M, Tian GY, Tang M, Kozan E, Zhang X, (2018) Energy-efficient application assignment in profile-based data center management through a Repairing Genetic Algorithm, Applied Soft Computing p399-408
- Xu X, Tang M, Tian GY, (2018) QoS-guaranteed resource provisioning for cloud-based MapReduce in dynamical environments, Future Generation Computer Systems p18-30
- Vasudevan M, Tian GY, Tang M, Kozan E, (2017) Profile-based application assignment for greener and more energy-efficient data centers, Future Generation Computer Systems p94-108
- Xu X, Tang M, (2016) A new approach to the cloud-based heterogeneous MapReduce placement problem, IEEE Transactions on Services Computing p862-871
- Tang M, (2015) A memetic algorithm for the location-based continuously operating reference stations placement problem in network real-time kinematic, IEEE Transactions on Cybernetics p2214-2223
- Ansari K, Feng Y, Tang M, (2015) A runtime integrity monitoring framework for real-time relative positioning systems based on GPS and DSRC, IEEE Transactions on Intelligent Transportation Systems p980-992
- Ai L, Tang M, Fidge CJ, (2011) Partitioning composite web services for decentralized execution using a genetic algorithm, Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications p157-172
- Tang M, Yao X, (2007) A Memetic Algorithm for VLSI Floorplanning, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics p62-69
- Lau RY, Tang M, Wong O, Milliner SW, Chen P, (2006) An Evolutionary Learning Approach for Adaptive Negotiation Agents, International Journal of Intelligent Systems p41-72
For more publications by this staff member, visit QUT ePrints, the University's research repository.
Completed supervisions (Doctorate)
- Cost-Efficient Virtual Machine Management in Data Centers (2016)
- Profile-based Application Management For Green Data Centres (2016)
- QoS-guaranteed Resource Provisioning for Cloud-based MapReduce (2016)
- Reliability Control of GNSS Carrier-phase Integer Ambiguity Resolution (2015)
- Development of an Inter-Vehicle Communications & Positioning Platform for Transport Safety Applications (2014)
- Composite SaaS Resource Management in Cloud Computing using Evolutionary Computation (2013)
- Achieving High Reliability for Ambiguity Resolutions with Multiple GNSS Constellations (2012)
- Efficient Safety Message Dissemination for Cooperative Collision Warning via Context Modelling (2011)
- QoS-Aware Web Service Composition Using Genetic Algorithms (2011)
- An Adaptive Framework for Internet-based Distributed Genetic Algorithms (2006)