KEYNOTE 1: Coupling Cloud Computing and IT Landscapes Estate Optimizations to Reduce Total Cost of Ownership

Keynote Speaker: Mazin Yousif, PhD
CTO & VP of Architecture of the Royal Dutch Shell Global Account, T-Systems International
Chair of Advisory Board, The European Research Consortium for Informatics and Mathematics

About the Keynote Speaker

Mazin YousifDr. Yousif is the Chief Technology Officer and Vice President of Architecture for the Royal Dutch Shell Global account at T-Systems, International. Before joining T-Systems, he was with IBM Canada, Global Technology services, as CTO for its Cloud Computing Services. He was a chief architect at Numonyx with focus on the role of Phase Change Memory (PCM) in servers' architectures and datacenters optimizations. He was also a principal engineer at Intel leading many projects on energy optimizations, virtualization and autonomic computing.

Dr. Yousif is the chair of the Advisory Board of the European Research Consortium for Informatics and Mathematics (ERCIM). He is the Editor-In-Chief of the IEEE Cloud Computing Magazine (2014-2017). He founded the NSF Industry/University Cooperative research Center for Autonomic Computing (in 2006) and then delegated to Professors in three universities to develop all the necessary documentation and paperwork to establish the center. Dr. Yousif was an Adjunct Professor at several universities like Duke, North Carolina State and Arizona. He was an IEEE Distinguished Visitors Program speaker from 2008 - 2013. He was a principal leader defining the InfiniBand Architecture and he co-chaired the management working group in the InfiniBand Trade Association - responsible for defining the InfiniBand Architecture. He has served as the General Chair or Program Chair for many conferences and serves in the editorial board of many journals.

Dr. Yousif is a frequent speaker in academic and industry conferences on various topics related to Cloud, Autonomic and Green Computing. He has also published extensively.

He finished his Master’s and Ph.D. degrees from the Pennsylvania State University in 1987 and 1992, respectively.


Datacenters are usually heterogeneous, become increasingly more complex, and continuously change due to introduction of new infrastructure; removing zombies and consolidating, to name a few. This makes establishing full visibility of all the infrastructure – both hardware and software – extremely difficult. Establishing full knowledge about datacenters’ infrastructure is the basis for IT landscape Estate Optimization. It is also required to fully adopt Cloud Computing and in a manner cheaper and more efficient than having internal IT.

This talk will include two major parts. In the first part, I’ll present an approach that helps build full knowledge about datacenters’ infrastructures. The approach includes scanning, reconciliation, analytics and visualization. In the second part of the talk and after establishing full knowledge about IT landscapes and making sense of all the collected data, I’ll then look at how the information is used to achieve efficiencies through IT Landscapes estate optimization and cloud computing adoption.

KEYNOTE 2: Context-aware Computing in the Era of Crowd Sensing and Big Data

Keynote Speaker: Daqing Zhang, PhD
Chair Professor at Institute of Software, Peking University, China
Professor at Institut Mines-Telecom/Telecom SudParis and CNRS, France

About the Keynote Speaker

Daqing ZhangDaqing Zhang is a Chair Professor at Institute of Software, Peking University, China. He has been a full professor at Institut Mines-Telecom/Telecom SudParis and CNRS, France, since 2007. His research interests include context-aware computing, urban computing, mobile social networking, big data analytics, pervasive elderly care, etc.. Dr. Zhang has served as the general or program chair for more than 10 international conferences, giving keynote or invited speeches at more than 15 international events. He is the associate editor for 4 journals including ACM Transactions on Intelligent Systems and Technology, etc.. In recent years, he has been exploring a new research direction called "social and community intelligence", making use of taxi GPS traces, social media data and mobile phone data to extract human and community intelligence and enable innovative services. He is the winner of the Ten-years CoMoRea impact paper award at IEEE PerCom 2013, the Best Paper award at IEEE UIC 2012 and the Best Paper Runner Up award at Mobiquitous 2011. Daqing Zhang obtained his Ph.D. from University of Rome "La Sapienza" in 1996.


Since the seminal work of Schilit and Theimer on context-awareness in 1994, great research progress has been made in context-aware computing field. Due to limited deployment scale of sensors and devices, in early years context-aware computing focused mainly on understanding and exploiting personal context in single smart spaces. As a result of the recent explosion of sensor-equipped mobile phones, the phenomenal growth of Internet and social network services, the broader use of the Global Positioning System (GPS) in all types of public transportation, and the extensive deployment of sensor network and WiFi in both indoor and outdoor environments, the digital footprints left by people while interacting with cyber-physical spaces are accumulating with an unprecedented speed and scale, resulting in "Big Data". The technology trend towards crowd sensing is creating new challenges and opportunities for context-aware computing - with huge amount, large scale, multi-modal, different granularity, diverse quality of data from various data sources. In this talk, I will start by examining the status quo of context-aware computing research in 2004 and then present the research direction called "social and community intelligence (SCI)" as a natural extension of context-aware computing in the era of crowd sensing and big data, with emphasis on extracting community and society level context. In particular I will introduce our recent work in crowd-sensed data analytics, including mining large scale taxi GPS data, mobile phone data and social media data for enabling innovative applications in smart cities. Finally I will briefly summarize the progress made in context-aware computing in the past ten years, in terms of data acquisition, modeling, inference, storage and context inferred.

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