Keynote I: Making Right Business Decision on Cloud Computing Resource Planning and Management

Dr. Yanzhen Qu

Dean of Computer Science and Information Technology, Colorado Technical University - Southern Colorado, USA

Professor of Colorado Technical University

About the keynote speaker

    Han-Chieh Chao Dr. Yanzhen Qu currently is the dean and a professor in Computer Science and Information Technology at Colorado Technical University – Southern Colorado, USA. Dr. Qu holds a B.Eng. in Electronic Engineering, a M. Eng. in Electrical Engineering, and a Ph.D. in Computer Science. Over his industrial career characterized by many “the world first innovations”, he has served at various senior or executive level Product R&D and IT management positions at several multinational corporations. He was also the chief system architect and the development director of several world first very large real-time commercial software systems.

    At Colorado Technical University, Dr. Qu is the dissertation supervisor of over ten computer science doctoral students, and his recent research interests include cloud computing security and architecture, cyber security risk detection and mitigation, data engineering, software engineering process and methods, soft computing, data mining over non-structured data, human-oriented computer interface, scalable enterprise information management system, as well as embedded and mobile computing. He has been served as general/program/session chair or keynote speaker in several professional conferences or workshops. He has published many research papers in the peer reviewed conferences and professional journals, and is currently serving as a member of editorial board of several professional journals.


Cloud computing has gained a lot of popularity in recent years with the promise of providing all kinds of solutions through reliable networked services associated with enormous benefits that such as lower cost, scalability, and responsiveness. Cloud computing has been creating many attractive opportunities for business of all sizes. “These opportunities, however, did not come without challenges.” In fact, both consumers and vendors of cloud computing service are facing challenges on when and how to plan and manage the computing resources that are needed for their business. For consumers, depends on the nature of application, using cloud computing may not always be a lower cost. For vendors, it is very difficult to accurately predict customers’ elastic demands on the computing resources. In this talk we will discuss the models that we have developed to help both consumers and vendors of cloud computing to deal with their challenges in this aspect.

Keynote II: Multimedia Traffic Modelling and Quality-of-Service Assurance

Dr. Geyong Min

Chair in Computer Science in the Department of Computing at the University of Bradford, UK

About the keynote speaker

    Victor C.M. Leung Professor Geyong Min is a Chair in Computer Science in the Department of Computing at the University of Bradford, UK. He received the PhD degree in Computing Science from the University of Glasgow, UK, and the BSc degree in Computer Science from Huazhong University of Science and Technology, China. His research interests include Next Generation Internet, Wireless Communications, Multimedia Systems, Information Security, Ubiquitous Computing, Modelling and Performance Engineering.

    His recent research has been supported by European FP, UK EPSRC and industrial partners. He has published over 200 research papers in prestigious international journals, including IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Computers, IEEE Transactions on Multimedia, IEEE Transactions on Parallel and Distributed Systems, and IEEE Network, and in reputable international conferences, such as ICDCS and IPDPS.

    Prof. Min is an Editorial Board member of 9 international journals and serves as the Guest Editor for 18 international journals. He has chaired/co-chaired 30 international conferences/workshops. He received the Outstanding Leadership Awards from IEEE International conferences HPCC’2012, TrustCom’2012, CIT’2010, ScalCom’2009, and HPCC’2008.


Recent advances in wireless communication technologies are making it possible for automobiles to be integrated into the global network. Intelligent Transportation Systems with vehicles in the loop are expected to significantly improve road safety, reduce traffic congestion and cut greenhouse gas emissions. This is made possible in the USA by Dedicated Short Range Communications (DSRC), which employs the IEEE 802.11p standard over the 75MHz of spectrum in the 5.9 GHz band allocated by the FCC for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. DSRC is expected to revolutionize road transportation by making possible many real-time safety applications. However, global deployment of DSRC is not expected to materialize in the near term due to regulatory and financial challenges. In the meantime, vehicles and their passengers are increasingly equipped with different forms of wireless networking capabilities, e.g., cellular, WiFi and WiMAX. Thus there is also a growing interest in supporting applications like infotainment, travel advisory, route planning, etc., using heterogeneous wireless networks. In this presentation, I shall describe several applications that leverage the wireless communications to put vehicles in the loop. Different applicants impose different requirements on the wireless network for data routing, transfer latency, etc. I shall review the technical challenges that need to be overcome to meet some of these requirements, and describe solutions developed in our recent research to meet these challenges. I shall conclude the presentation by discussing some future research directions.

Keynote III: Network traffic classification for security applications

Dr. Yang Xiang

Director of the Network Security and Computing Lab (NSCLab), Deakin University, Melbourne Burwood Campus, Australia

Senior Member of the IEEE

About the keynote speaker

    Jeffrey Voas Dr. Yang Xiang received his PhD in Computer Science from Deakin University, Australia. He is currently with School of Information Technology, Deakin University. He is the Director of the Network Security and Computing Lab (NSCLab). His research interests include network and system security, distributed systems, and networking. In particular, he is currently leading his team developing active defense systems against large-scale distributed network attacks.

    He is the Chief Investigator of several projects in network and system security, funded by the Australian Research Council (ARC). He has published more than 120 research papers in many international journals and conferences, such as IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Information Security and Forensics, and IEEE Journal on Selected Areas in Communications. One of his papers was selected as the featured article in the April 2009 issue of IEEE Transactions on Parallel and Distributed Systems. He has published two books, Software Similarity and Classification (Springer) and Dynamic and Advanced Data Mining for Progressing Technological Development (IGI-Global).

    He has served as the Program/General Chair for many international conferences such as ICA3PP 12/11, IEEE/IFIP EUC 11, IEEE TrustCom 11, IEEE HPCC 10/09, IEEE ICPADS 08, NSS 11/10/09/08/07. He has been the PC member for more than 60 international conferences in distributed systems, networking, and security. He serves as the Associate Editor of IEEE Transactions on Parallel and Distributed Systems and the Editor of Journal of Network and Computer Applications. He is a Senior Member of the IEEE.


Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest-neighbor (NN) based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel non-parametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic datasets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples. This work has significant impact on security applications.

Keynote IV: Privacy preserving in Cloud Computing

Dr. Jinjun Chen

Associate Professor, Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia

Director of Lab of Cloud Computing and Distributed Systems at UTS

About the keynote speaker

    Ivan Stojmenovic Dr Jinjun Chen is an Associate Professor from Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia. He is the Director of Lab of Cloud Computing and Distributed Systems at UTS. He holds a PhD in Computer Science and Software Engineering (2007) from Swinburne University of Technology, a Master of Engineering (1999) and a Bachelor of Applied Mathematics (1996) from Xidian University, China. Dr Chen’s research interests include cloud computing, big data, workflow management, privacy and security, and related various research topics. His research results have been published in more than 100 papers in high quality journals and at conferences, including IEEE Transactions on Service Computing, ACM Transactions on Autonomous and Adaptive Systems, ACM Transactions on Software Engineering and Methodology (TOSEM), IEEE Transactions on Software Engineering (TSE), and IEEE Transactions on Parallel and Distributed Systems.

    He received Swinburne Vice-Chancellor’s Research Award for early career researchers (2008), IEEE Computer Society Outstanding Leadership Award (2008-2009) and (2010-2011), IEEE Computer Society Service Award (2007), Swinburne Faculty of ICT Research Thesis Excellence Award (2007). He is the Vice Chair of IEEE Computer Society’s Technical Committee on Scalable Computing (TCSC), Vice Chair of Steering Committee of Australasian Symposium on Parallel and Distributed Computing, Founder and Coordinator of IEEE TCSC Technical Area on Workflow Management in Scalable Computing Environments, Founder and steering committee co-chair of International Conference on Cloud and Green Computing.


Cloud computing promises an open environment where customers can deploy IT services in pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness, privacy preserving becomes critical because otherwise customers may eventually lose the confidence of deploying cloud computing in practice. In this talk, we will discuss privacy preserving in general and then propose our solution to address a particular type of privacy preserving in cloud.

Keynote V: Multimedia Signal Modelling for Future Big Data Social Systems

Dr. Xingang Liu

University of Electronic Science and Technology of China, China

About the keynote speaker

    Jeffrey Voas Dr Xingang Liu is current an associate professor and PhD supervisor in the school of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), China. He was a BK21 research fellow and adjunct professor in the school of Electrical and Electronic Engineering in Yonsei University, and the department of Multimedia Engineering in Dongguk University, Korea, respectively. His research interests are multimedia signal communication related topics, such as heterogeneous and homogenous video transcoding, video quality measurement (QoE-related), video signal error concealment in the destination, mode decision algorithm, 3-D video codec and so on. He has published around more than 60 academic papers in refereed journals, conference proceedings as the first or corresponding author.

    Dr. Liu a member of IEEE and KICS, and he has been invited to serve as an organization committee, technical program committee and session chair of around 20 IEEE International conferences/workshops/symposiums, such as IEEE IPC2007, IEEE ICESS2008, IEEE PICOM2009 and so on. He received the “Outstanding Service Award” and “Outstanding Leadership Award” for IEEE IUCC2012 and IEEE CIT2012 in Jun. 2012 and Oct. 2012, respectively. Dr. Liu severed as the leading guest editors for several international journals, such as JWCN, MTAP, and so on.


Advances in multimedia data acquisition and storage technology have led to the growth of very large multimedia databases. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging problem. This challenge has opened the opportunity for research in Multimedia Signal Modelling (MSM). Multimedia Signal Modelling for Future Big Data Social Systems can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. The motivation for doing MSM is to use the discovered patterns to improve decision making. MSM will attract significant research efforts in developing methods and tools to organize, manage, search and perform domain specific tasks for data from domains such as surveillance, meetings, broadcast news, sports, archives, movies, medical data, as well as personal and online media collections. As an active and inter-disciplinary research field, multimedia signal modelling also presents a great opportunity for multimedia computing in the big data field.