RT Journal Article
JF IEEE Transactions on Cloud Computing
YR 2016
VO 4
IS 2
SP 138
TI On Achieving Energy Efficiency and Reducing CO2 Footprint in Cloud Computing
A1 Usman Wajid,
A1 Cinzia Cappiello,
A1 Pierluigi Plebani,
A1 Barbara Pernici,
A1 Nikolay Mehandjiev,
A1 Monica Vitali,
A1 Michael Gienger,
A1 Kostas Kavoussanakis,
A1 David Margery,
A1 David Garcia Perez,
A1 Pedro Sampaio,
A1 undefined,
A1 undefined,
A1 undefined,
A1 undefined,
K1 Measurement
K1 Energy consumption
K1 Cloud computing
K1 Energy efficiency
K1 Monitoring
K1 Green products
K1 Computational modeling
K1 Evaluation
K1 Energy-aware systems
K1 Scheduling and task partitioning
K1 evaluation
K1 Energy-aware systems
K1 scheduling and task partitioning
AB With the increasing popularity of the cloud computing model and rapid proliferation of cloud infrastructures there are increasing concerns about energy consumption and consequent impact of cloud computing as a contributor to global CO2 emissions. To date, little is known about how to incorporate energy consumption and CO2 concerns into cloud application development and deployment decision models. In this respect, this paper describes an eco-aware approach that relies on the definition, monitoring and utilization of energy and CO2 metrics combined with the use of innovative application scheduling and runtime adaptation techniques to optimize energy consumption and CO2 footprint of cloud applications as well as the underlying infrastructure. The eco-aware approach involves measuring or quantifying the energy consumption and CO2 at different levels of cloud computing, using that information to create scheduling and adaptation techniques that contribute towards reducing the energy consumption and CO2 emissions, and finally testing and validating the developed solutions in a multi-site cloud environment with the help of challenging case study applications. The experimental and validation results show the potential of the eco-aware approach to significantly reduce the CO2 footprint and consequent environmental impact of cloud applications.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 2168-7161
LA English
DO 10.1109/TCC.2015.2453988
LK http://doi.ieeecomputersociety.org/10.1109/TCC.2015.2453988