Presenter: Rizos Sakellariou Date: 07 May 2018
Traditionally, the objective of parallel computing has been to minimize execution time. As the complexity and the costs associated with modern execution platforms and infrastructures grow, parallel execution time cannot be viewed as a single objective to achieve at any cost. Instead, with such execution platforms consuming large amounts of energy, one needs to assess improvements in execution time against other types of cost. Cloud computing platforms, which are often used to execute parallel applications, typically follow a resource-on-demand paradigm, where users can pay for what resources they need. However, the underlying infrastructures suffer from increasing complexity which is partly masked by having users pay, sometimes for more than they need.
In this respect, the talk will motivate the need to address efficiently the issues related to the concurrent use of multiple (and often heterogeneous) resources offered by Cloud providers by capturing these issues as some form of a multi-objective optimization problem, which requires a good understanding and appreciation of different trade-offs. The talk will make this argument by presenting experience and research on planning the (parallel) execution of scientific workflow applications on the Cloud in a way that tries to strike a balance between different trade-offs such as execution time, energy consumption and cost. Algorithms and techniques, experimental results and ongoing research will be presented.
Rizos Sakellariou obtained his PhD from the University of Manchester in 1997 for a thesis on compile-time parallel loop partitioning and scheduling. Since then, he has held posts with Rice University and the University of Cyprus and for the last 18 years with the University of Manchester where he is leading a laboratory carrying our research in High-Performance, Parallel and Distributed systems, which over the last ten years has hosted more than 30 doctoral students, researchers and long-term visitors. He has carried out research on a number of topics related to parallel and distributed computing (including Grid and Cloud computing), with an emphasis on problems stemming from efficient/effective resource utilization and workload allocation issues. Further information about his research can be found on Google Scholar.