A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

David Margery, David Guyon, Anne-Cecile Orgerie, Christine Morin, Gareth Francis, Charaka Palansuriya, Kostas Kavoussanakis

Abstract

CO2 emissions related to Cloud computing reach nowadays worrying levels, without any reduction in sight. Often, Cloud users, asking for virtual machines, are not aware of such emissions which concern the entire Cloud infrastructures and are thus difficult to split into the actual resources utilization, such as virtual machines. We propose a CO2 emissions accounting framework giving flexibility to the Cloud providers, predictability to the users and allocating all the carbon costs to the users. This paper shows the architecture of our accounting framework and ideas on how to practically implement it.

References

  1. Bosse, S., Jamous, N., Kramer, F., and Turowski, K. (2016). Introducing Greenhouse Emissions in Cost Optimization of Fault-Tolerant Data Center Design. In IEEE Conference on Business Informatics (CBI), volume 01, pages 163-172.
  2. Gu, C., Shi, P., Shi, S., Huang, H., and Jia, X. (2015). A Tree Regression-Based Approach for VM Power Metering. IEEE Access, 3:610-621.
  3. Ismaeel, S. and Miri, A. (2016). Multivariate Time Series ELM for Cloud Data Centre Workload Prediction. In International Conference on Human-Computer Interaction (HCI), pages 565-576.
  4. Khosravi, A., Garg, S. K., and Buyya, R. (2013). Energy and Carbon-Efficient Placement of Virtual Machines in Distributed Cloud DataCenters. In International Conference on Parallel Processing (EuroPar).
  5. Kim, N., Cho, J., and Seo, E. (2011). Energy-Based Accounting and Scheduling of Virtual Machines in a Cloud System. In IEEE/ACM International Conference on Green Computing and Communications (GreenCom), pages 176-181.
  6. Kurpicz, M., Orgerie, A.-C., and Sobe, A. (2016). How much does a VM cost? Energy-proportional Accounting in VM-based Environments. In Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), pages 651-658.
  7. Natural Resources Defense Council (2014). Data Center Efficiency Assessment. NRDC Issue Paper, https://www.nrdc.org/sites/default/files/data-centerefficiency-assessment-IP.pdf.
  8. Nordhaus, W. D. (2012). Carbon Taxes to Move Toward Fiscal Sustainability, pages 208-214. Columbia University Press.
  9. Sharma, N., Gummeson, J., Irwin, D., and Shenoy, P. (2010). Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems. In IEEE Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pages 1-9.
  10. Sharma, N., Sharma, P., Irwin, D., and Shenoy, P. (2011). Predicting Solar Generation from Weather Forecasts Using Machine Learning. In IEEE International Conference on Smart Grid Communications (SmartGridComm), pages 528-533.
  11. Swan, M. (2015). Blockchain: Blueprint for a new economy. ” O'Reilly Media, Inc.”.
  12. Wajid, U., Cappiello, C., Plebani, P., Pernici, B., Mehandjiev, N., Vitali, M., Gienger, M., Kavoussanakis, K., Margery, D., García-P érez, D., and Sampaio, P. (2015). On Achieving Energy Efficiency and Reducing CO2 Footprint in Cloud Computing. IEEE Transactions on Cloud Computing, PP(99):14.
  13. Wu, W., Lin, W., and Peng, Z. (2016). An Intelligent Power Consumption Model for Virtual Machines Under CPU-intensive Workload in Cloud Environment. Soft Computing, pages 1-10.
  14. Xiao, P., Hu, Z., Liu, D., Yan, G., and Qu, X. (2013). Virtual Machine Power Measuring Technique with Bounded Error in Cloud Environments. Journal of Network and Computer Applications, 36(2):818-828.
  15. Yang, H., Zhao, Q., Luan, Z., and Qian, D. (2014). iMeter: An integrated VM power model based on performance profiling. Future Generation Computer Systems, 36:267 - 286.
Download


Paper Citation


in Harvard Style

Margery D., Guyon D., Orgerie A., Morin C., Francis G., Palansuriya C. and Kavoussanakis K. (2017). A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures . In Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-241-7, pages 299-304. DOI: 10.5220/0006356502990304


in Bibtex Style

@conference{smartgreens17,
author={David Margery and David Guyon and Anne-Cecile Orgerie and Christine Morin and Gareth Francis and Charaka Palansuriya and Kostas Kavoussanakis},
title={A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures},
booktitle={Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2017},
pages={299-304},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006356502990304},
isbn={978-989-758-241-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures
SN - 978-989-758-241-7
AU - Margery D.
AU - Guyon D.
AU - Orgerie A.
AU - Morin C.
AU - Francis G.
AU - Palansuriya C.
AU - Kavoussanakis K.
PY - 2017
SP - 299
EP - 304
DO - 10.5220/0006356502990304