SmartCom 2016

Bio: Chen Guoliang, born in 1938, a native of Yingshang County, Anhui Province, currently works as a professor and doctoral supervisor at the University of Science and Technology of China (USTC). Prof. Chen also serves as Dean of the School of Software Engineering of USTC, and Director of the National High Performance Computing Center at Hefei. Chen graduated from the Department of Radio and Electronics, Xi'an Jiaotong University in 1961. He joined the faculty of USTC in 1973. From 1981 to 1983, Prof. Chen undertook research and study at the Purdue University in the United States as a visiting scholar. In 2003, Prof. Chen was elected academician of the Chinese Academy of Sciences.
Prof. Chen has 15 national-level research projects, of which 11 have been completed. Besides, he has authored or co-authored more than 170 research papers and eight monographs, with the total number of citations exceeding over 540. Prof. Chen has won numerous awards or prizes at state, provincial and ministerial levels, including the second prize of the National Science and Technology Progress Award of China, the second prize of the National Teaching Achievement Award, and 12 science and technology progress awards at provincial or ministerial levels. Up to now, Prof. Chen has graduated a plethora of competent talents engaged in research on parallel algorithm including 26 doctorates. Prof. Chen is well-known for his rigorous style of learning and is venerated as a paragon of virtue for others.

Topic: Parallel and Interactive Computing of Big Data

Abstract: In the computation theory, the computational complexity problem classes is mainly studied in the two classes problems of P and NP. In Big data era, in order to raise the solving speed of P class problem, parallel method is one of the key solution. For instance, the NC parallel computing. Meanwhile, in order to improve the quality of solving NP class problem, interactive method can be adopted. For instance, the IP class interaction solving. In this report, we briefly introduce the some basic knowledge, including computational model and computational complexity theory, deterministic and non deterministic problem, the P class and NP class problem. Then we discuss the parallel solution on P problem, and the interaction solution of the NP problem. In the conclusion, according to our target, design strategy, implementation plan are discussed, and we proposed a framework of big data computing.

Bio: Qing Yang is Distinguished Engineering Professor in the Department of Electrical, Computer, and Biomedical Engineering at University of Rhode Island where he has been a faculty member since 1988. He is a director of High Performance Computing Lab (HPCL) of URI and is a recipient of 8 accomplishment awards while serving at URI such as Faculty Excellence Award, Distinguished Engineering Professor Award, Outstanding Intellectual Property Award. His research interests include computer architectures, memory and storage systems, computer networks, embedded computer systems and applications in neural-machine interface and biomedical engineering. He has published over 100 high quality technical articles in these research fields and held over a dozen issued patents and over a dozen pending applications. Majority of his patents have been licensed to computer industry with significant practical impact. Four high tech startup companies have been formed based on his patents. His latest startup, VeloBit, was based on his newly proposed concept: Content Locality, and was successfully acquired by Western Digital in July 2013. He has graduated 11 PhD students, of whom 4 are faculty members at major universities and others are leading researchers in computer companies such as Intel, Xerox, and EMC.
Yang is a Fellow of IEEE. He has served in the professional society in various capacities including general chair of the ACM/IEEE International Symposium on Computer Architecture (ISCA2011), IEEE international Conference on Network, Architecture, and Storage (NAS), IEEE Workshop on Storage Network Architecture and Parallel I/Os (SNAPI); IEEE Distinguished Speaker; Editor of IEEE Transactions; and Program Committee member of numerous international conferences. Besides being a principal investigator of many academic research projects, Yang has also done collaborative research with IBM, Intel, EMC, Freescale, and several startup companies in the Boston area. He received his B.Sc. in computer science from Huazhong University of Science and Technology, Wuhan, China, in 1982, M.A.Sc. in electrical engineering from University of Toronto, Canada, in 1985, and Ph.D degree in computer Engineering from The Center for Advanced Computer Studies, University of Louisiana, Lafayette, in 1988.

Topic: Introducing DPU----Data-storage Processing Unit---Placing Intelligence in Storage

Abstract (PDF): Cloud computing and big data applications require data storage systems that deliver high performance reliably and securely. The central piece, the brain, of a storage system is the central controller that manages the storage. However, all existing storage controllers have their limitations such as for flash memory only, for interface control only, for fault-tolerance only, and so forth. As the data become larger, more storage technologies emerge, and applications spread wider, the existing controllers cannot keep pace with the rapid growth of big data.
We introduce and are currently building a storage controller with built in intelligence, referred to as DPU for Data-storage Processing Unit, to manage, control, analyze, and classify big data at the place where they are stored. The idea is to place sufficient intelligence closest to the storage devices that are experiencing revolutionary changes with the emergence of storage class memories such as flash, PCM, MRAM, Memristor and so forth. Machine learning logics are a major part of DPU that learn I/O behaviors inside the storage to optimize performance, reliability, and availability. Advanced security techniques are implemented inside a storage device. Deep learning techniques train and analyze big data inside a storage device and reinforcement learning optimizes storage hierarchy. Parallel and pipelining techniques are utilized to process stored data exploiting the inherent parallelism inside SSD. Our preliminary experiment data showed promising results that could potentially change the landscape of storage market.

Bio: Meikang Qiu received the BE and ME degrees from Shanghai Jiao Tong University and received Ph.D. degree of Computer Science from University of Texas at Dallas. Currently, he is an Adjunct Professor at Columbia University and Associate Professor of Computer Science at Pace University. He is an IEEE Senior member and ACM Senior member. He is the Chair of IEEE Smart Computing Technical Committee. His research interests include cyber security, cloud computing, big data storage, hybrid memory, heterogeneous systems, embedded systems, operating systems, optimization, intelligent systems, sensor networks, etc. A lot of novel results have been produced and most of them have already been reported to research community through high-quality journal and conference papers. He has published 5 books, 330 peer-reviewed journal and conference papers (including 150+ journal articles, 180+ conference papers, 50+ IEEE/ACM Transactions papers), and 3 patents. He has won ACM Transactions on Design Automation of Electrical Systems (TODAES) 2011 Best Paper Award. His paper about cloud computing has been published in JPDC (Journal of Parallel and Distributed Computing, Elsevier) and ranked #1 in Top Hottest 25 Papers of JPDC 2012. He has won another 8 Conference Best Paper Awards in recent years. Currently he is an associate editor of 10+ international journals, including IEEE Transactions on Computer and IEEE Transactions on Cloud Computing. He is the General Chair/Program Chair of a dozen of IEEE/ACM international conferences, such as IEEE HPCC, IEEE CSCloud, IEEE BigDataSecurity. He has given 100+ talks all over the world, including Oxford, Princeton, Stanford, and New York University. He has won Navy Summer Faculty Award in 2012 and Air Force Summer Faculty Award in 2009. His research is supported by US government such as NSF, Air Force, Navy and companies such as GE, Nokia, TCL, and Cavium.

Topic: Privacy Protection for Mobile Cloud to Prevent Data Over-collection

Abstract: In smart city, all kinds of users’ data are stored in electronic devices to make everything intelligent. A smartphone is the most widely used electronic device and it is the pivot of all smart systems. However, current smartphones are not competent to manage users’ sensitive data, and they are facing the privacy leakage caused by data over-collection. Data over-collection, which means smartphones apps collect users’ data more than its original function while within the permission scope, is rapidly becoming one of the most serious potential security hazards in smart city. We study the current state of data over-collection and study some most frequent data over-collected cases. We present a mobile-cloud framework, which is an active approach to eradicate the data over-collection. By putting all users’ data into a cloud, the security of users’ data can be greatly improved. We have done extensive experiments and the experimental results have demonstrated the effectiveness of our approach. This research has been published in IEEE Transactions on Computers.

Bio: Shui Yu is currently a Senior Lecturer of School of Information Technology, Deakin University. He is a member of Deakin University Academic Board (2015-2016), a Senior Member of IEEE, and a member of AAAS and ACM, the Vice Chair of Technical Subcommittee on Big Data Processing, Analytics, and Networking of IEEE Communication Society, and a member of IEEE Big Data Standardization Committee.
Dr Yu’s research interest includes Security and Privacy in Networking, Big Data, and Cyberspace, and mathematical modelling. He has published two monographs and edited two books, more than 150 technical papers, including top journals and top conferences, such as IEEE TPDS, IEEE TC, IEEE TIFS, IEEE TMC, IEEE TKDE, IEEE TETC, and IEEE INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 22.
Dr Yu actively serves his research communities in various roles. He is currently serving the editorial boards of IEEE Communications Surveys and Tutorials, IEEE Access, IEEE Journal of Internet of Things, IEEE Communications Magazine, and a number of other international journals. He has served more than 70 international conferences as a member of organizing committee, such as publication chair for IEEE Globecom 2015 and 2017, IEEE INFOCOM 2016 and 2017, TPC co-chair for IEEE BigDataService 2015, IEEE ATNAC 2014, IEEE ITNAC 2015; Executive general chair for ACSW2017.
More information of Dr Yu can be found at http://www.deakin.edu.au/~syu/


Topic: Networking for Big Data: Challenges and Opportunities

Abstract: (PDF) Big Data is one of the hottest topics in our communities, and networking is an indispensable corner stone for the fancy big data applications. As a result, there is an emerging research branch, Networking for Big Data (NBD), in networking and communication fields. In this talk, we will firstly overview the current landscape of this energetic area, and then present the unprecedented challenges in this new domain, and finally discuss the current research directions in the main topics in networking for big data. We humbly hope this talk will shed light for forthcoming researchers to further explore the uncharted part of this promising land.