Two-Phase Virtual Machine Placement in Cloud Computing Data Centers Using Extreme Learning Machine Prediction-Based Triggering Method
Keywords:Virtual machine placement, Cloud computing, Incremental VMP, VMP reconfiguration
Background: Two-phase Optimization of Virtual Machine Placement (VMP) Problem considers both the Online Incremental VMP (iVMP) phase in which the new arrival of dynamic requests of Virtual Machines VMs are attended to and the Offline VMP reconfiguration (VMPr) phase that performs placement recalculation. In the two-phase scheme, the first part of the two-phase approach is the iVMP, where virtual machines (VMs) can be built, changed, or destroyed at runtime. While the second phase focuses on raising the standard of solutions produced by the iVMP, several studies have been done in different literature to solve the VMP problem. However, the methods used tend to be over-forecast and have long runs of a linear trend. This affects the prediction and produces a less optimal solution. Objective: The following four objective functions are optimized using the proposed Extreme Learning Machine Prediction-Based Triggering Method for Virtual Machine Placement in Cloud Computing Datacenters in Two-Phases, which combines the advantages of both online (dynamic) and static (offline) VMP formulations: the length of the reconfiguration process, the amount of energy used, the way resources are used, and the financial expenses. This study suggests a brand-new strategy for deciding when to start the VMP reconfiguration phase. Results: The Method provides more accuracy to the predicted requests as well as reduces the total economic penalties for Service Level Agreement (SLA) violations. An experimental comparison with the existing approach is conducted utilizing 400 cases. Conclusion: The results demonstrated that, in comparison to the benchmark approach, the proposed work obtained a minimum cost function with a 10.5% improvement.
A.Baloglazov, Abawajy, J., &Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud Computing. Future Gener. Comp. Syst., 755-768.
Amarilla, A., Benıtez, L., Zalimben, S., Lopez-Pires, F., &Baran, B. (2017). Evaluating a Two-Phase Virtual Machine Placement Optimization Scheme for Cloud Computing Datacenters. MIC/MAEB 2017, (pp. 4-7). Barcelona.
Angeles, S. (2014). Virtualization Vs Cloud Computing: What's the Difference? Business News Daily.
Biran, O., Corradi, A., Fanelli, M., Foschini, L., Nus, A., Raz, D., et al. (2012). A stable network-aware VM placement for Cloud Systems. in proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012) (pp. 498-506). IEEE Computer Society.
Calcavecchia, N. M., Biran, O., Hadad, E., &Moatti, Y. (2012). VM placement strategies for cloud scenarios in cloud computing (CLOUD). IEEE 5th International Conference (pp. 852-859). IEEE.
Chamas, N., Lopez-Pires, F., &Baran, B. (2017). Two Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty. IEEE.
Ding, Zhao &Ru nie, (2013). Extreme Learning Machine: Algorithm, Theory and Applications ArtifIntell Rev (2015) 44:103–115DOI 10.1007/s10462-013-9405-z
Dong, J., Wang, H., Jin, X., Li, Y., Zhang, P., & Cheng, S. (2013). Virtual machine placement for improving energy efficiency and network performance in its cloud. in Distributed Computing Systems Workshops (ICDSW), 2013 IEEE 33rd International Conference (pp. 238-243). IEEE.
Fang, S., Kanagavelu, R., Lee, B.-S., Foh, C., & Aung, K. (2013). Power-Efficient virtual machine placement and migration in data centers. IEEE International Conference (pp. 1408-1413). in Green Computing and Communications, GreenCom, 2013 IEEE and Internet of Things, IThings/CSPCom.
Fang, W., Liang, X., Li, S., Chiaraviglio, L., &Xiong, N. (2013). VMPlanner: Optimising virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers. Computer Networks, vol.57 (1), 179-196.
Ferreto, T., Netto, M., Calheiros, R., & Rose, C. D. (2011). server consolidation with migration control for virtualized data centers. Future Generation Computer Systems, vol.27, 1027-1034.
Goudarzi, H., & Pedram, M. (2012). Energy-efficient virtual machine replication and placement in a cloud computing system in cloud computing (CLOUDS). IEEE 5th International Conference, (pp. 750-757).
Gupta, A., Milojicic, D., & Kal'e, L. V. (2012). Optimizing VM placement for hpc in cloud. in proceedings of the 2012 workshop on Cloud Services, federation and 8th open cirrus summit, ACM, (pp. 1-6).
H. Goudarzi, &M. Pedram. (2012). Energy-efficiency virtual machine replication and placement in a cloud computing system. in cloud computing (CLOUD), IEEE 5th International Conference, (pp. 750-757).
Hong, H.-J., Chen, D.-Y., Huang, C.-Y., Chen, K.-T., & Hsu, C.-H. (2013). Qoe-aware virtual machine placement for cloud games .in Network and Systems Support for Games (NetGames), 2013 12th Annual Workshop on IEEE, (pp. 1-2).
Huang, G. B., Zhu, Q. Y, &Siew, C., K. (2006) Extreme Learning Machine theory and application Neuro computing 70 (2006) 489–501
Hyndman, R., &Athanasopoulos, G. (2018). Forecasting: Principles and Practice. melbourne, Australia: oTexts.
Ihara, D., Pires, F. L., &Baran, B. (2015). Many-objective virtual machine placement for Dynamic Environments.
J.J.Prevost, K.Nagothu, B.Kelly, &M.Jamshidi. (2013). Optimal update frequency model for physical machine state change state and virtual machine placement in cloud. System of Systems Engineering (SoSE), 8th International Conference on IEEE, (pp. 159-164).
Kaur, G., & Bhardwaj, V. (2016). A review on VM Placement Strategies. International Journal of Advanced Research in Computer Science and Software Engineering, Vol.6 (5), 521-526.
Le, K., Bianchini, R., Zhang, J., Jaluria, Y., Meng, J., & Nguyen, T. D. (2011). Reducing Electricity cost through virtual machine placement in high performance computing cloud. in Proceedings of the 2011 International Conference for High-Performance Computing, Networking, Storage and Analysis (p. 22). ACM.
Li, K., Wu, J., &Blaisse, A. (2013). Elasticity-aware virtual machine placement for cloud data centers. in Cloud Networking (CloudNet), 2013 IEEE 2nd International conference (pp. 99-107). IEEE.
Li, K., Zheng, H., Wu, J., & Du, X. (2015). Virtual machine placement in cloud systems through the migration process. International Journal of Parallel, Emergent and Distributed Systems, 393-410.
Masdari, M., Nabavi, S. S., &Ahmadi, V. (2016). An Overview of virtual machine placement schemes In Cloud Computing. Journal of Network and Computer Applications.
McKenziea, Everette& Gardner. (2010). Damped Trend Exponential Smoothing: A modeling viewpoint International Journal of Forecasting 26 (2010) 661–665.
Mell, P., &Grance, T. (2009). The NIST Definition of Cloud Computing. National Institute of Standards and Technology.
Ortigoza, J., Pires, F., &B.Baran. (2016). Workload generation for virtual machine placement in cloud computing environments. XLII Latin American Computing Conference, CLEI, (pp. 1-9).
Paliwal, S. (n.d.). Performance challenges in cloud computing.
Pires, F. L., &Baran, B. (2015). A Virtual Machine Placement Taxonomy. International Symposium on Cluster, Cloud and Grid Computing, 15th IEEE/ACM, 159-168.
Pires, F. L., &Baran, B. (2013). Multi-objective virtual machine placement with service level agreement: A memetic Algorithm approach .in proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing (pp. 203-210). IEEE Computer Society.
Pires, F. L., &Baran, B. (2014). virtual machine placement literature review (data). Polytechnic School, National University of Asuncion on, Tech. Rep.
Pires, F. l., Baran, B., Benitez, L., Zalinmben, S., &Amarilla, A. (2017). Virtual machine placement for elastic infrastructures in overbooked cloud computing data centers under uncertainty. Future Generation Computer Systems.
Pires, F. L., Baran, B., Benitez, L., Zalinmben, S., &Amarilla, A. (2017). Virtual machine placement for elastic infrastructures in overbooked cloud computing data centers under uncertainty. Future Generation Computer Systems.
Rochwerger, B., Breitgand, D., Levy, E., Galis, A., Nagin, K., Llorente, I., et al. (2009). The reservior model and architechture for open federated cloud computing. IBM Journal of Research and Development, vol. 53, no. 4, 1-4.
Speitkamp, B., &Bichler, M. (2010). A mathematical programming approach for server. IEEE Trans. Serv. Comput, 266–278.
Tchernykh, A., Schwiegelsohn, U., Alexandrov, V., &Talbi, E. (2015). towards understanding uncertainty in cloud computing resource provisioning. Procedia Comp. Sci., 1772-1781.
Tomas, L., &Tordsson, J. (2014). An autonomic approach to risk-aware data center overbooking. IEEE Trans. Aloud Comput. , 292-305.
Wu, G., Tang, M., Tian, Y.-C., & Li, W. (2012). Energy Efficient virtual machine placement in data centers by Genetic Algorithm. in Neural Information Processing. Springer, 315-323.
Wu, J.-J., Liu, P., & Yang, J.-S. (2012). Workload characteristics-aware virtual machine consolidation algorithms. in Proceedings of the 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 42-49). IEEE Computer Society.
Yahaya, R., H., Ambursa, F., U., &Galadanci, B., S. (2020). A two-phase Optimization Scheme with Efficient Prediction-based Triggering for Virtual Machine Placement in Cloud Datacenters. Ilorin Journal of Computer Science and Information Technology (ILJCSIT)https://www.iljcsit.com.ngISSN: 2550-7214 (print)Vol. 3, No. 1 (2020)