Abstract
The electric power is one of the major operating expenses in data centers. Rising and varying energy costs induce the need of further solutions to use energy efficiently. The first steps to improve efficiency have already been accomplished by applying virtualization technologies. However, a practical approach for data center power control mechanisms is still missing.
In this paper, we address the problem of energy efficiency in data centers. Efficient and scalable power usage for data centers is needed. We present different approaches to improve efficiency and carbon footprint as background information. We propose an in-progress idea to extend the possibilities of power control in data centers and to improve efficiency. Our approach is based on virtualization technologies and live-migration to improve resource utilization by comparing different effects on virtual machine permutation on physical servers. It delivers an efficiency-aware VM placement by assessing different virtual machine permutation. In our approach, the applications are untouched and the technology is non-invasive regarding the applications. This is a crucial requirement in the context of Infrastructure-as-a-Service (IaaS) environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beloglazov, A. and Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurrency and Computation: Practice and Experience, 2012.
Beloglazov, A. and Buyya, R.: OpenStack Neat: a framework for dynamic and energy-efficient consolidation of virtual machines in OpenStack clouds, Concurrency and Computation: Practice and Experience, 2014.
Borgerding, A. and Schomaker, G.: Extending Energetic Potentials of Data Centers by Resource Optimization to Improve Carbon Footprint. EnviroInfo 2014 - 28th International Conference on Informatics for Environmental Protection, pages 661–668, 2014.
Chen, C., et al.: Green-aware workload scheduling in geographically distributed data centers. 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pages 82–89, 2012.
Chen, H., Caramanis, M.C. and Coskun, A.K.: The data center as a grid load stabilizer, Design Automation Conference (ASP-DAC), 2014 19th Asia and South Pacific, pages. 105-112, 20–23, Jan. 2014.
Corradi, A., Fanelli, M. and Foschini, L.: VM consolidation: A real case based on OpenStack Cloud, Future Generation Comp. Syst., 32, pages 118–127, 2014.
Dalvandi, A., et al.: Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers. In Proceedings of the 2013 I.E. International Conference on Cloud Computing Technology and Science - Volume 01 (CLOUDCOM ’13), Vol. 1. IEEE Computer Society, Washington, DC, USA, pages 212–217, 2013.
Hoyer, M.: Resource management in virtualized data centers regarding performance and energy aspects, Doctoral dissertation, Oldenburg, Univ., Diss., 2011.
Krioukov, A., et al.: Integrating renewable energy using data analytics systems: Challenges and opportunities. IEEE Data Eng. Bull., 34(1), pages 3–11, 2011.
Liu, Z., et al.: Greening geographical load balancing. In Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS ’11, pages 233–244, New York, NY, USA, 2011.
Meisner, D., et al.: PowerNap: eliminating server idle power. In Proceedings of the 14th international conference on Architectural support for programming languages and operating systems (ASPLOS XIV). ACM, New York, NY, USA, pages 205–216, 2009.
Meisner, D., et al.: Does low-power design imply energy efficiency for data centers? In Naehyuck Chang, Hiroshi Nakamura, Koji Inoue, Kenichi Osada and Massimo Poncino, editors, ISLPED, pages 109–114. IEEE/ACM, 2011.
Pelley, S., et al.: Understanding and abstracting total data center power. Proc. of the 2009 Workshop on Energy Efficient Design (WEED), Jun. 2009.
Qureshi, A., et al.: Cutting the electric bill for internet-scale systems. SIGCOMM Comput. Commun. Rev., 39(4), pages 123–134, August 2009.
Schröder, K., Schlitt, D., Hoyer, M. and Nebel, W.: Power and cost aware distributed load management. In Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking (e-Energy ’10). ACM, New York, NY, USA, pages 123–126, 2010.
Smith, J. W. and Sommerville, I.: Workload Classification & Software Energy Measurement for Efficient Scheduling on Private Cloud Platforms, CoRR abs/1105.2584, 2011.
Tang, Q., et al.: Thermal-Aware Task Scheduling for Data centers through Minimizing Heat Recirculation, The IMPACT Laboratory School of Computing and Informatics Arizona State University Tempe, AZ 85287, 2008.
Vu, H. and Hwang, S.: A Traffic and Power-aware Algorithm for Virtual Machine Placement in Cloud Data Center. International Journal of Grid & Distributed Computing 7.1, 2014.
Wood, T., Tarasuk-Levin, G., Shenoy, P., Desnoyers, P., Cecchet, E. and Corner, M. D.: Memory buddies: exploiting page sharing for smart colocation in virtualized data centers. In Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments (VEE ’09). ACM, New York, NY, USA, pages 31–40, 2009.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Borgerding, A., Schomaker, G. (2016). Extending Energetic Potentials of Data Centers by Resource Optimization to Improve Carbon Footprint. In: Marx Gomez, J., Sonnenschein, M., Vogel, U., Winter, A., Rapp, B., Giesen, N. (eds) Advances and New Trends in Environmental and Energy Informatics. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-23455-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-23455-7_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23454-0
Online ISBN: 978-3-319-23455-7
eBook Packages: Business and ManagementBusiness and Management (R0)