The huge amount of data which can be stored and processed with the use of the internet has resulted in the capacity of data centers get bigger. This has resulted in the increase of the consumption of the power in data centers significantly and the management of power consumption in data centers has become essential. There are many techniques that have been proposed in that prospective. This paper highlights the need of managing the power in data centers, in order to reach the target of the efficiency of the power and observe several architectural techniques designed for its management of power. The detail of techniques is also given based on their behavior. The purpose of this paper is to provide intuition into the techniques to improve energy efficiency of data centers by using self-organizing architecture proposed and also to request the designers to continue producing solutions which can help for managing the dissipation of the power of data centers.

Self-Organizing, virtualization, server consolidation, power consumption

[1] Bohrer, P., Elnozahy, E., Keller, T., Kistler, M., Lefurgy, C., McDowell, C., & Rajamony, R. (2014). The case for power management in Web servers. Norwell, MA, USA: Kluwer Academic Publishers.
[2] Bollen, J., & Heylighen, F. (2015). Algorithms for the Self-organization of Distributed, Multiuser Networks. Austrian: Cybernetics and Systems.
[3] Bonabeau, E., Dorigo, M., & Theraulaz, G. (2015). swarm Intelligence. USA: Oxford University Press.
[4] Elnozahy, E., Kistler, M., & Rajamony, R. (2015). Energy- Efficient server clusters. Power-Aware Computer Systems.
[5] Heylighen, F. (2015). The science of self-organization and Adaptivity. USA: Encyclopedia of life support. .
[6] Heylighen, F., & Gershenson, C. (2015). The meaning of self-organization in computing. IEE Intelligent Systems.
[7] Khargharia, B., Hariri, S., & Yousif, M. (2015). Autonomic power and performance management for computing systems. USA: Cluster Computing.
[8] Lefurgy, C., Rajamani, K., Rawson, F., Felter, W., Kistler, M., & Keller, T. (2015). Energy Management for commercial servers. USA: Computer 36 (12).
[9] Pinheiro, E., Bianchini, R., Carrera, E., & Heath, T. (2015). Load Balancing and unbalancing for power and performance in cluster-based systems. In proceedings of the Workshop on Compilers and Operating Systems for Low Power.
[10] White, R., & Abels, T. (2014). Energy Resorce management in the virtual data center. Washington, DC, USA: Proceedings of the International Symposium on Electronics and the Environment.
[11] Jun, C., Yunchuan , Q., Yu Ye, & Zhuo, T. (2015). A Live Migration Algorithm for Virtual Machine in a Cloud Computing Environment. Chine: UIC-ATC-ScalCom-CBDCom-IoP.
[12] Megha, Desai R.; Hiren, Patel B.;. (2015). Efficient Virtual Machine Migration in Cloud Computing. Fifth International Conference on Communication Systems and Networking Technologies.
[13] Mofijul, I. M. (2015). A Genetic Algorithm for Virtual Machine Migration in Heterogeneous Mobile Cloud Computing. Bangladesh.
[14] Pankajdeep , K., & Rani, A. (2015). Virtual Machine Migration in Cloud Computing. Internationale Journal of Grid Distribution Computing Vol.8.
[15] Rabiatul , A., Ruhani, R. A., Norliza, Z., & Mustaffa, S. (2015). Virtual Machine Migration Implementation in Load Balancing for cloud computing. Mara (UiTM).