Keywords
Effective factors
Factor Analysis Approach
Eastern Azerbaijan and Ardebil Branches
How to Cite
Abstract
Task scheduling and energy efficiency seem to be the necessary design requirements for current computing systems in recent years. It extends from single servers to data centers and clouds, as they consume large amounts of electrical power. For this reason, an effective energy management for cloud data centers is essential. At present, many researchers have focused and implemented biologically-based calculations as a desirable paradigm for addressing heterogeneity and the growth of energy crisis with skill and no added complications. Similarly, for our work, we selected biological behavior of Korean insects and chosen FFO-based migration method. The benchmark for choosing it is the rapid convergence and global optimization. In addition, the notion of limiting the overall increase in power increases with respect to new VM migration and never before used for the VM migration method. In the energy consumption scenario by VM migration, a FFO-based linear model is formulated that executes an FFO algorithm that is able to solve the power consumption problem with the firefly attraction feature. In other words, this paper proposes a virtual energy virtualization migration technique that emits live VMs from an active node to another active node. The proposed technique uses the biography-inspired worn-out optimization technique to find the best node for over-migrating VMs to achieve energy efficiency in cloud data centers. This optimizes energy efficiency through the optimal migration of VMs, thereby improving the level of resource utilization.
References
Abed, I., Koh, S. P., Sahari, K., Jagadeesh, P., & Tiong, S. K. (2014). Optimization of the
Time of Task Scheduling for Dual Manipulators using a Modified Electromagnetism-Like Algorithm and Genetic Algorithm. Arabian Journal for Science and Engineering, 39(8), 6269-6285.
Xu, Y., Li, K., Hu, J., & Li, K. (2014). A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Information Sciences, 270(0), 255-287.
Sinnen, O., To, A., & Kaur, M. (2011). Contention-aware scheduling with task duplication. Journal of Parallel and Distributed Computing, 71(1), 77-86.
Taborda, D. M. G., & Zdravkovic, L. (2012). Application of a Hill-Climbing technique to the formulation of a new cyclic nonlinear elastic constitutive model. Computers and Geotechnics, 43(0), 80-91
F. Luo, Z. Y. Dong, Y. Chen, Y. Xu, K. Meng, and K. P. Wong, "Hybrid cloud computing platform: The next generation IT backbone for smart grid," in 2012 IEEE Power and Energy Society General Meeting, 2012, pp. 1-7.
Q. Li and Y. Guo, "Optimization of resource scheduling in cloud computing," in 2010 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2010.
D. M. Dakshayini and D. H. Guruprasad, "An optimal model for priority based service scheduling policy for cloud computing environment," International Journal of Computer Applications, vol. 32, pp. 23-29, 2011.
Z. Lee, Y. Wang, and W. Zhou, "A dynamic priority scheduling algorithm on service request scheduling in cloud computing," in Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on, 2011, pp. 4665-4669.
A. G. Delavar, M. Rahmany, A. Halaakouie, and R. Sookhtsaraei, "DSQGG: An optimized Genetic-based algorithm for Scheduling in Distributed Grid," in Computer Technology and Development (ICCTD), 2010 2nd International Conference on, 2010, pp. 365-369.
L. Cherkasova, D. Gupta, and A. Vahdat, "When virtual is harder than real: Resource allocation challenges in virtual machine based it environments," Hewlett Packard Laboratories, Tech. Rep. HPL-2007-25, 2007.
S. Pandey, L. Wu, S. M. Guru, and R. Buyya, "A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments," in 2010 24th IEEE international conference on advanced information networking and applications, 2010, pp. 400-407.
A. Bensmaine, M. Dahane, and L. Benyoucef, "A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment," Computers & Industrial Engineering, vol. 66, pp. 519-524, 2013.
S. T. Maguluri, R. Srikant, and L. Ying, "Stochastic models of load balancing and scheduling in cloud computing clusters," in INFOCOM, 2012 Proceedings IEEE, 2012, pp. 702-710.
H. N. Van, F. D. Tran, and J.-M. Menaud, "SLA-aware virtual resource management for cloud infrastructures," in Computer and Information Technology, 2009. CIT'09. Ninth IEEE International Conference on, 2009, pp. 357-362.
Y. Hu, J. Wong, G. Iszlai, and M. Litoiu, "Resource provisioning for cloud computing," in Proceedings of the 2009 Conference of the Center for Advanced Studies on Collaborative Research, 2009, pp. 101-111.
G. Wei, A. V. Vasilakos, Y. Zheng, and N. Xiong, "A game-theoretic method of fair resource allocation for cloud computing services," The journal of supercomputing, vol. 54, pp. 252-269, 2010.
Y. O. Yazir, C. Matthews, R. Farahbod, S. Neville, A. Guitouni, S. Ganti, et al., "Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis," in Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on, 2010, pp. 91-98.
L. Wu, S. K. Garg, and R. Buyya, "SLA-based resource allocation for software as a service provider (SaaS) in cloud computing environments," in Cluster, Cloud and Grid Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on, 2011, pp. 195-204.
M. A. Arfeen, K. Pawlikowski, and A. Willig, "A framework for resource allocation strategies in cloud computing environment," in Computer Software and Applications Conference Workshops (COMPSACW), 2011 IEEE 35th Annual, 2011, pp. 261-266.
S. Abirami and S. Ramanathan, "Linear scheduling strategy for resource allocation in cloud environment,"International Journal on Cloud Computing: Services and Architecture(IJCCSA), vol. 2, pp.9-17, 2012.
Z. Xiao, W. Song, and Q. Chen, "Dynamic resource allocation using virtual machines for cloud computing environment," Parallel and Distributed Systems, IEEE Transactions on, vol. 24, pp. 1107-1117, 2013.
S. T. Maguluri, R. Srikant, and L. Ying, "Heavy traffic optimal resource allocation algorithms for cloud computing clusters," Performance Evaluation, vol. 81, pp. 20-39, 2014.
W. Tian, Y. Zhao, M. Xu, Y. Zhong, and X. Sun, "A toolkit for modeling and simulation of real-time virtual machine allocation in a cloud data center," Automation Science and Engineering, IEEE Transactions on, vol. 12, pp. 153-161, 2016.
S. Marrone and R. Nardone, "Automatic resource allocation for high availability cloud services," Procedia Computer Science, vol. 52, pp. 980-987, 2017.
ESU González, JVV Antúnez (2016). Bioética como marco de la responsabilidad social en hospitales públicos. Opción, Año 32, Especial No.12 (2016): 830-856.
5JV Villalobos (2016). DE NUEVO AL DEBATE SOBRE LA CUESTIí“N DE LOS PARADIGMAS CIENT í FICOS. Opción 32 (81).
Tahavieva F.R., Nigmatullina I.A., Speech-communicative function in the structure of predictive competence of young schoolchildren with musculoskeletal disorders, Astra Salvensis, Supplement No. 10, 2017, p. 315-322.
KHASHEVA Z. M., GOLIK V.I., SHULGATY L.P., ERMISHINA E.V., Economic justification of technological diversification for the metal mining and production complex, Astra Salvensis - review of history and culture, No. 10, 2017, p. 361-367.