A VIRTUAL MACHINES PLACEMENT MODEL FOR ENERGY AWARE CLOUD COMPUTING

Paolo Campegiani

2012

Abstract

We present an energy aware model for virtual machines placement in cloud computing systems. Our model manages resources of different kind (like CPU and memory) and energy costs that are depending on the kind and amount of deployed resources, incorporating capital expenses (costs of infrastructure and amortizations), operational expenses (electricity costs) and data center energy parameters as PUE, also with possibly different service levels for virtual machines. We show that the resulting model could be solved via a genetic algorithm, and we perform some sensitivity analysis on the model energy parameters.

References

  1. Beloglazov, A. and Buyya, R. (2010). Energy efficient allocation of virtual machines in cloud data centers.
  2. Campegiani, P. (2009). A genetic algorithm to solve the virtual machines resources allocation problem in multitier distributed systems. In 2nd International Workshop on Virtualization Performance: Analysis, Characterization and Tools (VPACT'09) .
  3. Campegiani, P. and LoPresti, F. (2009). A general model for virtual machines resources allocation in multi-tier distributed systems. In 5th International Conference on Autonomic and Autonomous Systems (ICAS 7809) . IARIA.
  4. Chang, F., Ren, J., and Viswanathan, R. (2010). Optimal resource allocation in clouds. In 3rd IEEE International Conference on Cloud Computing.
  5. Economou, D., S. Rivoire, C. K., and Ranganatham, P. (2006). Full-system power analysis and modeling for server environments. In Workshop on Modeling Benchmarking and Simulation (MOBS).
  6. Gandhi, A., Harchol-Balter, M., Das, R., and Lefurgy, C. (2009). Optimal power allocation in server farms. In SIGMETRICS/Performance 7809 .
  7. Lu, X. and Gu, Z. (2011). A load-adaptive cloud resource scheduling algorithm model based on ant colony algorithm. In IEEE Cloud Computing and Intelligent Systems.
  8. Mazzucco, M. and Dumas, M. (2011). Reserved or ondemand instances? a revenue maximization model for cloud providers. In IEEE 4th International Conference on Cloud Computing.
  9. McCullogh, J., Agarwal, Y., and Chandrashekar, J. (2010). Evaluating the effectiveness of model-based power characterization. In USENIX Annual Technical Conference.
  10. Rivoire, S., Ranganathan, P., and Kozyrakis, C. (2008). A comparison of high-level full-system power models. In Conference on Power aware computing and systems (HOTPOWER'08) . USENIX.
  11. Singh, K., Bhadauria, M., and McKee, S. A. (2009). Real time power estimation and thread scheduling via performance counters. ACM SIGARCH Computer Architecture News, 37(2):46-55.
  12. Srikanthaiah, S., Kansal, A., and Zhao, F. (2008). Energy aware consolidatin for cloud computing. In Conference on Power aware computing and systems (HOTPOWER'08) . USENIX.
  13. Urgaonkar, R., Kozat, U. C., Igarashi, K., and Neely, M. J. (2010). Dynamic resource allocation and power management in virtualized data centers. In IEEE Network Operations and Management Symposium (NOMS'10) .
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Paper Citation


in Harvard Style

Campegiani P. (2012). A VIRTUAL MACHINES PLACEMENT MODEL FOR ENERGY AWARE CLOUD COMPUTING . In Proceedings of the 1st International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS, ISBN 978-989-8565-09-9, pages 247-253. DOI: 10.5220/0003950402470253


in Bibtex Style

@conference{smartgreens12,
author={Paolo Campegiani},
title={A VIRTUAL MACHINES PLACEMENT MODEL FOR ENERGY AWARE CLOUD COMPUTING},
booktitle={Proceedings of the 1st International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,},
year={2012},
pages={247-253},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003950402470253},
isbn={978-989-8565-09-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,
TI - A VIRTUAL MACHINES PLACEMENT MODEL FOR ENERGY AWARE CLOUD COMPUTING
SN - 978-989-8565-09-9
AU - Campegiani P.
PY - 2012
SP - 247
EP - 253
DO - 10.5220/0003950402470253