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Keynote Lectures

PERFORMANCE OF CLOUDS - Issues and Research Directions
Eleni Karatza, Aristotle University of Thessaloniki, Greece

EXPERIMENTAL COMPUTER SCIENCE - Approaches and Instruments
Frédéric Desprez, Antenne Inria Giant, Minatec Campus, France

DATA INTENSIVE SCIENCE AND CLOUD COMPUTING
Fabrizio Gagliardi, Barcelona Supercomputing Centre, Spain

Cloud Standards
Arlindo Dias, IBM Portugal, Portugal

 

PERFORMANCE OF CLOUDS - Issues and Research Directions

Eleni Karatza
Aristotle University of Thessaloniki
Greece
 

Brief Bio
Eleni Karatza is a Professor in the Department of Informatics at the Aristotle University of Thessaloniki, Greece. Dr. Karatza's research interests include Computer Systems Modeling and Simulation, Performance Evaluation, Grid and Cloud Computing, Energy Efficiency in Large Scale Distributed Systems, Resource Allocation and Scheduling and Real-time Distributed Systems.

Professor Karatza has authored or co-authored over 180 technical papers and book chapters including four papers that earned best paper awards at international conferences. She is senior member of IEEE, ACM and SCS, and she served as an elected member of the Board of Directors at Large of the Society for Modeling and Simulation International (2009-2011). She has served as Program Chair and Keynote Speaker in International Conferences.

Professor Karatza is the Editor-in-Chief of the Elsevier Journal "Simulation Modeling Practice and Theory", Area Editor for Computer Systems and Networks of the "Journal of Systems and Software" of Elsevier, and she has been Guest Editor of Special Issues in multiple International Journals.http://agent.csd.auth.gr/~karatza/


Abstract
Computational and data clouds are large-scale distributed systems used for serving demanding jobs. Their performance became more important due to the tremendous increase of users, applications and services. Cloud computing is expected to change the way computer applications and services are developed and delivered. Therefore, there are important issues that must be addressed, such as: efficient scheduling, resource management, energy efficiency, reliability, security and trust, cost, availability, quality. Cloud computing provides users the ability to lease computational resources from its virtually infinite pool for use in HPC. Users for HPC applications can exploit the capabilities of the large number of resources and perform compute intensive jobs over clouds without having to deploy a physical infrastructure. Resource allocation and scheduling is a difficult task in clouds where there are many alternative computers with different capacities. If cloud computing is going to be used for HPC, appropriate methods must be considered for both parallel job scheduling and VM scalability. The scheduling algorithms must seek a way to maintain a good response time to leasing cost ratio. Furthermore, data security and availability and also energy conservation are critical issues that have to be considered as well. The performance evaluation of clouds is often possible only by simulation rather than by analytical techniques, due to the complexity of the systems. Simulation can provide important insights into the efficiency and tradeoffs of scheduling in large-scale complex distributed systems, such as clouds.



 

 

EXPERIMENTAL COMPUTER SCIENCE - Approaches and Instruments

Frédéric Desprez
Antenne Inria Giant, Minatec Campus
France
 

Brief Bio
Frédéric Desprez is a Chief Senior Research Scientist at Inria and holds a position at the LIP laboratory (ENS Lyon, France). He co-founded the SysFera company where he holds a position as scientific advisor. He received his PhD in C.S. from Institut National Polytechnique de Grenoble, France, in 1994 and his MS in C.S. from ENS Lyon in 1990. His research interests include parallel algorithms, scheduling for large scale distributed platforms, data management, and grid and cloud computing. He leads the Grid'5000 project, which offers a platform to evaluate large scale algorithms, applications, and middleware systems. See http://graal.ens-lyon.fr/~desprez/ for further information.


Abstract
The increasing complexity of available infrastructures with specific features (caches, hyper-threading, dual core, etc.) or with complex architectures (hierarchical, parallel, distributed, etc.) makes it extremely difficult to build analytical models that allow for a satisfying prediction. Hence, it raises the question on how to validate algorithms and software systems if a realistic analytic analysis is not possible. As for some many other sciences, the one answer is experimental validation. Nevertheless, experimentation in Computer Science is a difficult subject that today still opens more questions than it solves: What may an experiment validate? What is a "good experiment"? How to build an experimental environment that allows for "good experiments"? etc. In this keynote, we will provide an overview of experimental computer science, both around the way these experiments are made and with the description of some existing platforms in the US and Europe.



 

 

DATA INTENSIVE SCIENCE AND CLOUD COMPUTING

Fabrizio Gagliardi
Barcelona Supercomputing Centre
Spain
 

Brief Bio
In the present position since July 2008. In this position leading an EMEA team responsible for MSR Connections in EMEA. Based in Geneva with main office at the MSR research centre in Cambridge UK. As part of this job supporting and contributing to MSR Cloud computing strategy in Europe, including the incubation of a major EU project (www.venus-c.eu), and with 3 direct engagements with major national funding agencies (EPSRC in the UK, INRIA in France and HLRS in Germany). Responsible for the strategic planning of the Cloud Computing team at EMIC in Germany. He joined Microsoft in November 2005, when after the last EGEE conference in his home town of Pisa, Fabrizio Gagliardi took responsibility for the company Technical Computing Initiative in Europe, Middle East, Africa and Latin America. Before then and starting at the end of the 90’ he was among the pioneers in developing and introducing Grid computing in Europe with early collaboration with Ian Foster and Carl Kesselman in the US, this led to projects like EU-DataGrid and EGEE, of which he was Principal Investigator and Director from 2000 till 2005. In 2004-2005 while still Director of EGEE (www.eu-egee.org) he contributed to the incubation and launch of more than 10 other Grid EU projects all inspired and supported by the EU EGEE flag-ship. Appointments: In 1985-1987 he was advisor to the Director of the Electra Synchrotron in Trieste Italy. From 1985 till 2005 he was scientific advisor to the Director of the Scuola Normale in Pisa, one of the most prestigious universities in Italy. From 2000 till 2005, advisor to the President of the Italian National Academy. Since 1995, member of the scientific advisory board to Trento local government agency, FBK (http://www.fbk.eu/) . Member of the Board of Directors of CoSBi (www.cosbi.eu) since 2008. Member of the Board of Directors of INRIA-MSR joint institute (http://www.msr-inria.inria.fr/). Director of the joint MSR institute on parallel computing at the Barcelona Supercomputing Centre (http://www.bscmsrc.eu/ ). Since 2009 he is the chair of the ACM European Council (http://europe.acm.org/) and also sits in the ACM Distinguished Speakers Programme International Committee.


Abstract
Science is becoming predominantly data driven. The amount of data scientists have to process is becoming overwhelming and the amount of time and effort which go into accessing, curating, managing the data is taking an ever increasing share of the scientists activity. This has produced a shift in the traditional computational science paradigm towards what is now often called the “IV Paradigm” (http://research.microsoft.com/en-us/collaboration/fourthparadigm/). The talk will cover some of the consequences of this trend on computing infrastructures and the impact that some new emerging technologies such as Cloud Computing could have.



 

 

Cloud Standards

Arlindo Dias
IBM Portugal
Portugal
 

Brief Bio
Not available


Abstract
Not available



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