Dynamic Allocation of Virtual Resources Based on Genetic Algorithm in the Cloud
Cloud computing provides dynamic resource allocation using virtualization technology to greatly improve resource efficiency. However, current resource reallocation solution seldom considers the stability of VM placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated occurrence of hot nodes. In this paper, a multi-objective genetic algorithm (MOGA) is presented to significantly improve the stability of VM placement pattern with less migration overhead. The group encoding scheme is employed in MOGA to express the mapping of physical nodes and virtual machines (VMs). Fitness function is designed based on the stability and migration overhead of group. Our simulation results demonstrate that, our MOGA is much more efficient than other algorithms for resource reallocation with good stability.
KeywordsCloud computing Resource allocation Genetic algorithm
This research was funded by Natural Science Foundation of Hubei Province (No. 2014CFB817), China.
- 1.Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R.H., Konwinski, A., Lee, G., Patterson, D.A., Rabkin, A., Stoica, I., Zaharia, M.: Above the Clouds: A Berkeley View of Cloud Computing. Technical report (2009)Google Scholar
- 2.Rai, A., Bhagwan, R., Guha, S.: Generalized resource allocation for the cloud. In: Proceedings of the 3rd Symposium on Cloud Computing (SOCC 2012). ACM, San Jose (2012)Google Scholar
- 3.Hermenier, F., Lorca, X., Menaud, J.M., Muller, G., Lawall, J.: Entropy: a consolidation manager for clusters. In: Proceedings of the ACM/Usenix International Conference on Virtual Execution Environments (VEE 2009), pp. 41–50 (2009)Google Scholar
- 4.Chen, L., Shen, H.: Consolidating complementary VMs with spatial/temporal-awareness in cloud datacenters. In: IEEE Conference on Computer Communications (INFOCOM 2014), pp. 1033–1041 (2014)Google Scholar
- 5.Zhang, L., Li, Z., Wu, C.: Dynamic resource provisioning in cloud computing: a randomized auction approach. In: IEEE Conference on Computer Communications (INFOCOM 2014), pp. 433–441 (2014)Google Scholar
- 6.Zhou, Z., Liu, F., Li, Z., Jin, H.: When smart grid meets geo-distributed cloud: an auction approach to datacenter demand response. In: IEEE Conference on Computer Communications (INFOCOM 2015) (2015)Google Scholar
- 8.Wang, W., Li, B., Liang, B.: Dominant resource fairness in cloud computing systems with heterogeneous servers. In: IEEE Conference on Computer Communications (INFOCOM 2014), pp. 583–591 (2014)Google Scholar
- 9.Guo, J., Liu, F., Lui, J.C.S., Jin, H.: Fair network bandwidth allocation in IaaS datacenters via a cooperative game approach. IEEE/ACM Trans. Netw. (2015)Google Scholar
- 10.Falkenauer, E., Delchambre, A.: A genetic algorithm for bin packing and line balancing. In: IEEE International Conference on Robotics and Automation, pp. 1186–1192 (1992)Google Scholar
- 12.CloudSim: A framework for modeling and simulation of cloud computing infrastructures and services (2015). http://www.cloudbus.org/cloudsim/