A Survey on Various Problems and Techniques for Optimizing Energy Efficiency in Cloud Architecture

Authors

  • Sanjeevi PANDIYAN School of Information Technology and Engineering, VIT University, Vellore 632014
  • Viswanathan PERUMAL School of Information Technology and Engineering, VIT University, Vellore 632014

Keywords:

Energy consumption, data centers, DVFS, cloud computing

Abstract

Cloud computing offers variety of resources and provides flexible services to users. The major issue ominous cloud computing is that it consumes servile amount of energy for providing a valuable computing services. Many attempts were taken to decrease the energy consumption of the data center yet the endeavors make less satisfaction. In this paper, a survey of energy consumption in the cloud computing is described a) Problems in the existing methods and energy reduction constraint used by various algorithms b) Comparison of various techniques with their findings emphasizing their advantages and disadvantages. Resource allocation in cloud is another issue in which energy can be reduced, and if a server is in idle it menaces enormous amount of energy and many algorithms were attempted to make the idle server to use in an efficient manner. Cooling of data center is also another enticing issue, because during heat the server consumes more energy and the stability of the system is reduced. Finally, the objective is to decrease the amount of energy consumed in data center leading to enhancement of Quality of Services (QoS).

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

K Kant. Data center evolution: A tutorial on state of the art, issues, and challenges. J. Comput. Netw. 2009; 53, 2939-65.

Anusuya and Krishnapriya. Green cloud: A pocket-level simulator with on-demand protocol for energy-aware cloud data centers. Int. J. Sci. Res. 2014; 3, 10-6.

A Beloglazov, R Buyya, YC Lee and A Zomaya. A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv. Comput. 2010; 82, 47-111.

I Petri, H Li, Y Rezgui, Y Chunfeng, B Yuce and B Jayan. A HPC based cloud model for real-time energy optimization. Enterp. Inform. Syst. 2016; 10, 108-28.

PT Jaeger, J Lin and JM Grimes. Cloud computing and information policy: Computing in a policy cloud. J. Inform. Tech. Polit. 2008; 5, 269-83.

A Beloglazov, J Abawajy and R Buyya. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generat. Comput. Syst. 2012; 28, 755-68.

CM Wu, RS Chang and HY Chan. A green energy-efficient scheduling algorithm using the DVFS technique for cloud data centers. Future Generat. Comput. Syst. 2014; 37, 141-7.

AM Sampaioa and JG Barbosa. Towards high-available and energy-efficient virtual computing environments in the cloud. Future Generat. Comput. Syst. 2014; 40, 30-43.

P Raycroft, R Jansen, M Jarus and PR Brenner. Performance bounded energy efficient virtual machine allocation in the global cloud. Sustain. Comput. Inform. Syst. 2014; 4, 1-9.

Q Zhang, G Metri, S Raghavan and W Shi. RESCUE: An energy-aware scheduler for cloud environments. Sustain. Comput. Inform. Syst. 2014; 4, 215-24.

J Cao, K Li and I Stojmenovic. Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. IEEE Trans. Comput. 2014; 63, 45-58.

A Greenberg, J Hamilton, A David and P Patel. The cost of a cloud: Research problems in data center networks. ACM SIGCOMM Comput. Commun. 2009; 39, 68-73.

N Kim, J Cho and E Seo. Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems. Future Generat. Comput. Syst. 2014; 32, 128-37.

Enhanced Power Monitoring for Dell PowerEdge Servers, Available at: www.dell.com/downloads/ global/power/ps3q08-20080174-Bhadri.pdf, accessed July 2015.

Architecture (DESA) for 11G Rack and Tower Servers, Available at: www.dell.com/downloads/ global/products/pedge/en/poweredge-11g-desa-white-paper.pdf, accessed July 2015.

SE Dashti and AM Rahmani. Dynamic VMs placement for energy efficiency by PSO in cloud computing. J. Exp. Theor. Artif. Intell. 2016; 28, 97-112.

A Tchana, ST Giang and L Broto. Two levels autonomic resource management in virtualized IaaS. Future Generat. Comput. Syst. 2013; 29, 1319-32.

RN Calheiros, AN Toosi, C Vecchiola and R Buyya. A coordinator for scaling elastic applications across multiple clouds. Future Generat. Comput. Syst. 2012; 28, 1350-62.

X Wang, Y Wang and Y Cui. A new multi-objective bi-level programming model for energy and locality aware multi-job scheduling in cloud computing. Future Generat. Comput. Syst. 2014; 36, 91-101.

H Chen, X Zhu, H Guob, J Zhu, X Qin and J Wud. Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment. J. Syst. Softw. 2015; 99, 20-35.

A Kansal, F Zhao, J Liu, N Kothari and AA Bhattacharya. Virtual machine power metering and provisioning. In: Proceedings of the 1st ACM Symposium on Cloud Computing, New York, USA, 2010, p. 39-50.

S Singh and I Chana. QRSF: QoS-aware resource scheduling framework in cloud computing. J. Supercomput. 2015; 71, 241-92.

V Pandey, S Singh and S Tapaswi. Energy and time efficient algorithm for cloud offloading using dynamic profiling. Wirel. Pers. Commun. 2014; 80, 1687-701.

X Xiang, C Lin and X Chen. EcoPlan: Energy-efficient downlink and uplink data transmission in mobile cloud computing. Wirel. Netw. 2014; 21, 453-66.

S Hosseinimotlagh, F Khunjush and R Samadzadeh. SEATS: Smart energy-aware task scheduling in real-time cloud computing. J. Supercomput. 2015; 71, 45-66.

A Ravi and SK Peddoju. Handoff strategy for improving energy efficiency and cloud service availability for mobile devices. Wirel. Pers. Commun. 2014; 81, 101-32.

Z Deng, G Zeng, Q He, Y Zhong and W Wang. Using priced timed automaton to analyse the energy consumption in cloud computing environment. Cluster Comput. 2014; 17, 1295-307.

AA Chandio, K Bilal, N Tziritas, Z Yu, Q Jiang, SU Khan and CZ Xu. A comparative study on resource allocation and energy efficient job scheduling strategies in large-scale parallel computing systems. Cluster Comput. 2014; 17, 1349-67.

R Dautov, I Paraskakis and M Stannett. Towards a framework for monitoring cloud application platforms as sensor networks. Cluster Comput. 2014; 17, 1203-13.

Z Cao and S Dong. An energy-aware heuristic framework for virtual machine consolidation in Cloud computing. J. Supercomput. 2014; 69, 429-51.

A Horri, MS Mozafari and G Dastghaibyfard. Novel resource allocation algorithms to performance and energy efficiency in cloud computing. J. Supercomput. 2014; 69, 1445-61.

C Cheng, J Li and Y Wang. An energy-saving task scheduling strategy based on vacation queuing theory in cloud computing. Tsinghua Sci. Tech. 2015; 20, 28-39.

X Xiang, C Lin and X Chen. Energy-efficient link selection and transmission scheduling in mobile cloud computing. IEEE Wirel. Commun. Lett. 2014; 3, 153-6.

L Yang, X Zhu, H Chen, J Wang, S Yin and X Liu. Real-time tasks oriented energy-aware scheduling in virtualized clouds. IEEE Trans. Cloud Comput. 2014; 2, 168-80.

P Chauhan and M Gupta. Energy aware cloud computing using dynamic voltage frequency scaling. Int. J. Comput. Sci. Tech. 2014; 5, 195-9.

T Guerout, T Monteil, GD Costa, RN Calheiros, R Buyya and M Alexandru. Energy-aware simulation with DVFS. Simulat. Model. Pract. Theor. 2013; 39, 76-91.

R Kumar and G Sahoo. Cloud computing simulation using cloudSim. Int. J. Eng. Trends Tech. 2014; 8, 82-6.

P Sanjeevi, V Perumal, MR Babu and PV Krishna. Study and analysis of energy issues in cloud computing. Int. J. Appl. Eng. Res. 2015; 10, 16961-9.

D Bhatt. A revolution in information technology: Cloud computing. Walailak J. Sci. & Tech. 2012; 9, 107-13.

P Sanjeevi and P Viswanathan. A green energy optimized scheduling algorithm for cloud data centers. In: Proceedings of the IEEE International Conference on Computing and Network Communications, Trivandrum, India, 2015, p. 941-5.

Downloads

Published

2016-08-01

How to Cite

PANDIYAN, S., & PERUMAL, V. (2016). A Survey on Various Problems and Techniques for Optimizing Energy Efficiency in Cloud Architecture. Walailak Journal of Science and Technology (WJST), 14(10), 749–758. Retrieved from https://wjst.wu.ac.th/index.php/wjst/article/view/1905