Amazon / Gartner / Agency for Science / Google / Cloud Providers / Distributed Systems Laboratory / /
Continent
Asia / Europe / /
Country
Germany / United States / Australia / Singapore / / /
Event
Reorganization / Environmental Issue / /
Facility
Institute of High Performance Computing / Karlsruhe Institute of Technology / The University of Melbourne / Institute of Information Systems / /
IndustryTerm
server-specific energy-saving policies / proposed energy-saving policies / energy-efficient computing / supercomputer applications / research work address energy-efficient computing / High energy consumption / overall energy efficiency / energy saving scheme / shared server / energy sustainability / energy reduction / energy savings / energy usage / energy / cluster systems / energy costs / a computing infrastructure / parallel applications / electricity / energy-efficient scheduling / energy consumption / near-optimal energy-efficient scheduling policies / bag-of-tasks applications / energy cost / generic energy-efficient scheduling policies / energy-efficient solutions / web serving / power-aware scheduling algorithms / virtualization technologies / web workloads / consumer applications / energy sustainability analysis / web workload / high energy / storage technologies / energy efficiency / computing / high energy cost / prediction algorithm / server management / energy efficiency factors / carbon/energy / High energy usage / focused on achieving energy efficiency / /
Organization
University of Melbourne / Institute of High Performance Computing / Institute of Technology / Agency for Science / Technology and Research / Institute of Information Systems and Management / /
Person
Arun Anandasivam / Shin Yeo / /
Position
local scheduler / Corresponding author / meta-scheduler / /
Energy-Efficient Scheduling of HPC Applications in Cloud Computing Environments Saurabh Kumar Garg a,∗ , Chee Shin Yeo b , Arun Anandasivam c , Rajkumar Buyya a a