Mingzhe Tan
ABSTRACT
In order to reduce the energy waste in the data center, while taking into account the violation rate of user service level and the resource utilization rate, this paper studies how to adopt effective strategy to get the reasonable suboptimal solution by combining above three indexes. This paper designs a nonlinear energy consumption model based on polynomials and exponents to measure the energy consumption of different deployment schemes. It is the basis of the deployment strategy. At the heart of this paper, the probability transfer function and the fitness function are designed to optimize the ant colony algorithm. Finally a multi-objective optimization ant colony algorithm based on threshold-dependent pheromone updating was proposed. The algorithm is a kind of distributed optimization method, which is beneficial to parallel computation and has a positive feedback mechanism. The optimal solution can be efficiently obtained by continuous updating of pheromone. The experimental results show that the ant colony algorithm of multi-objective virtual machine placement can achieve the optimal trade-off between multiple conflicting targets, so that the system's energy consumption is less, the violation rate of user service level and the resource utilization rate is also small.
- Liu, P. and Chen, W. W. 2015. Cloud Computing (Third Edition). House. Electronics Industry.Google Scholar
- Ding, X. B., Ma, Z. and Dai, X. F. 2015. Dynamic time slice scheduling algorithm of virtual machine for response delay. Computer Engineering. 41, 7 (July. 2015), 11--16.Google Scholar
- Alicherry, M. and Lakshman, T. V. 2013. Optimizing data access latencies in cloud systems by intelligent virtual machine placement. In Proceedings of the 32nd IEEE International Conference on Computer Communications. (Turin, Italy, April, 14--19, 2013) INFOCOM '13. IEEE, 647--655.Google Scholar
- Tan, Y. M., Zeng, G. S. and Wang, W. 2012. Policy of energy optimal management for cloud computing platform with stochastic tasks. Journal of Software. 23, 2 (February. 2012), 266--278.Google ScholarCross Ref
- Ye, K. J., Wu, C. H., Jiang, X. H. and He, Q. M. 2012. Power management of virtualized cloud computing platform. Chinese Journal. Computers. 35, 6 (June. 2012), 1262--1285.Google ScholarCross Ref
- Luo, L., Wu, W. J. and Zhang, F. 2014. Energy modeling based on cloud data center. Journal of Software. 25, 7 (July. 2014), 1371--1387.Google Scholar
- Ge, J. W., Wang, Q. L. and Fang, Y. Q. 2016. A satisfaction-based task scheduling in cloud computing. Microelectronics & Computer. 33, 10 (2016), 111--114.Google Scholar
- Xiang, X. D., Lin, C. and Chen, X. 2015. EcoPlan: Energy-efficient downlink and uplink data transmission in mobile cloud computing. Wireless Networks. 21, 2 (February, 2015), 453--466. Google ScholarDigital Library
- Chen, C., Cai, L.C. and Gao, X. 2014. Cloud computing tasks scheduling algorithm based on mutagenic factors. Journal of Sichuan University. Science & Engineering (Natural Science Edition). 1 (2014), 010.Google Scholar
- Li, L. R. and Zheng, J. H. 2004. Multi objective genetic algorithm based on Pareto Front. Nature Science Journal of Xiangtan University. 26, 1 (January, 2004), 39--41.Google Scholar
- Duy, T. V. T., Yukinori, S. and Inoguchi, Y. 2011. A prediction-based green scheduler for datacenters in clouds. IEICE TRANS. Information of System. 94, 9 (2011), 1731--1741.Google ScholarCross Ref
- Li, Q., Hao, Q. F., Xiao, L. M., et al. 2011. Adaptive management and multi-objective optimization for virtual machine placement in cloud computing. Chinese Journal of Computers. 34, 12. (December. 2011), 2253--2264.Google Scholar
- Verma, A., Ahuja, P. and Neogi, A. 2008. pMapper: Power and migration cost aware application placement in virtualized systems. Proceedings of the 9th ACM/IFIP/USENIX International Conference on Distributed System of Platforms and Open Distributed Processing (Leuven, Belgium, December, 01--05, 2008). DSPODP '08. ACM, New York, NY, Springer Berlin Heidelberg, 243--264. Google ScholarDigital Library
- Li, B., Li, J. Huai, J., et al. 2009. Enacloud: An energy-saving application live placement approach for cloud computing environments. Proceedings of the 6th IEEE International Conference on Cloud Computing (Bangalore, India, September, 21--25, 2009). CLOUD '09. IEEE, 17--24. Google ScholarDigital Library
- Stutzle, T. and Hoos, H. 1997. Improvement on the Ant System: Introducing Max-Min Ant System. In Proceedings of the IEEE International Conference on Artificial Neural Nets and Genetic Algorithms (Norwich, U.K., 1997). ICCANN '97. IEEE, Springer, Vienna, 245--249.Google Scholar
- Hu, L., Jin, H. Liao, X., et al. 2008. Magnet: A novel scheduling policy for power reduction in cluster with virtual machines. Proceedings of the 5th IEEE International Conference on Cluster Computing (Tsukuba, Japan, September 29--October 01, 2008). CLUSTER '08. IEEE, 13--22.Google Scholar
- Ma, F. 2013. Research of virtual machine placement and live migration in cloud data center. Doctoral Thesis. Beijing Jiaotong University.Google Scholar
- Fan, X., Weber, W. D. and Barroso, L. A. 2007. Power provisioning for a warehouse-sized computer. ACM SIGARCH Computer Architecture News. 35, 2 (March, 2007), 13--23. Google ScholarDigital Library
- Wang, X. J. 2014. A virtual machine allocation algorithm based on power-aware in cloud computing. Computer Technol Dev. 24, 10(2014), 88--92.Google Scholar
- Xu, L., Zeng, Z. B. and Yao, C. 2012. Study on virtual resource allocation optimizationin cloud computing environment. J Communs. 1(2012).Google Scholar
- Buyya, R., Yeo, C. S. Venugopal S., et al. 2009. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems. 25, 6(2009), 599--616. Google ScholarDigital Library
Index Terms
- An Energy-aware Virtual Machine Placement Algorithm in Cloud Data Center
Recommendations
Reduce Energy Consumption through Virtual Machine Placement in Cloud Data Centre
MIKE 2013: Proceedings of the First International Conference on Mining Intelligence and Knowledge Exploration - Volume 8284In this paper, energy consumption in the data centre was studied where thousands of servers and other devices runs, energy are utilized to run the server and cooling the environment. Energy consumption can be reduced by switching off the idle server by ...
Energy Saving Virtual Machine Allocation in Cloud Computing
ICDCSW '13: Proceedings of the 2013 IEEE 33rd International Conference on Distributed Computing Systems WorkshopsIn the data center, a server can work in either active state or power-saving state. The power consumption in the power-saving state is almost 0, thus it is always desirable to allocate as many VMs as possible to some active servers and leave the rest to ...
Energy-efficient strategy for virtual machine consolidation in cloud environment
AbstractAn important issue of energy efficiency in cloud environment is to perform more jobs while consuming less amount of power. Virtual machine consolidation remains the most deployed strategy to manage both performance and energy consumption. Most of ...
Comments