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Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi

Year 2020, Volume: 1 Issue: 1, 37 - 44, 27.12.2020

Sinem Akyol Bilal Alataş

Abstract

Bulut bilişim veri merkezlerinde kaynak yönetimini geliştirmek ve güç tüketimini azaltmak için etkili görev planlaması çok önemlidir. Bununla birlikte, sayısız görev, sanal makineler ve dikkate alınması gereken çok fazla amaç dikkate alındığında, planlamanın oldukça zor bir problem olduğu bilinmektedir. Bu görevlerin çizelgelenmesi için birçok yaklaşım ve vaka çalışması geliştirilmiştir. Çok amaçlı optimizasyon, görev planlama problemlerini çözmede çok sayıda çakışan hedefle başa çıkmak için ilginç bir tekniktir. Bu makale, çeşitli bulut bilişim ortamları için tasarlanmış meta-sezgisel optimizasyon yöntemleri tabanlı çok amaçlı planlama yaklaşımlarına ilişkin inceleme ve genel bakış sunmaktadır. Ayrıca gelecekte bu alandaki potansiyel araştırma alanları için, çok amaçlı planlama şemalarının karşılaştırmasını sağlamaktadır.

Keywords

Görev Planlama Mekanizmaları Çok Amaçlı Optimizasyon Bulut Bilişim

References

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  • Amini Motlagh, A., Movaghar, A., & Rahmani, A. M. Task scheduling mechanisms in cloud computing: A systematic review, International Journal of Communication Systems, 33(6) e4302, 2020.
  • Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. A hybrid model of internet of things and cloud computing to manage big data in health services applications, Future generation computer systems, 86, 1383-1394, 2018.
  • Bandaru, S., & Deb, K. Metaheuristic techniques, Decision Sciences (pp. 693-750), CRC Press, 2016.
  • Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhang, Q. Multiobjective evolutionary algorithms: A survey of the state of the art, Swarm and Evolutionary Computation, 1(1) 32-49, 2011.
  • Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization, Expert Systems with Applications, 47 106-119, 2016.
  • Zavala, G. R., Nebro, A. J., Luna, F., & Coello, C. A. C. A survey of multi-objective metaheuristics applied to structural optimization, Structural and Multidisciplinary Optimization, 49(4) 537-558, 2014.
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  • Zitzler, E., Laumanns, M., & Thiele, L. SPEA2: Improving the strength Pareto evolutionary algorithm, TIK-report, 103, 2001.
  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2) 182-197, 2002.
  • Coello, C. C., & Lechuga, M. S. MOPSO: A proposal for multiple objective particle swarm optimization, In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1051-1056). IEEE, May, 2002.
  • Zhang, Q., & Li, H. MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 11(6) 712-731, 2007.
  • Panda, S. K., & Jana, P. K. A multi-objective task scheduling algorithm for heterogeneous multi-cloud environment, In 2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV) (pp. 82-87), IEEE, January, 2015.
  • Li, C., Song, M., Zhang, M., & Luo, Y. Effective replica management for improving reliability and availability in edge-cloud computing environment, Journal of Parallel and Distributed Computing, 2020.
  • Gupta, S., Agarwal, I., & Singh, R. S. Workflow scheduling using Jaya algorithm in cloud, Concurrency and Computation: Practice and Experience, 31(17) e5251, 2019.
  • Li, S., & Sun, W. Utility maximisation for resource allocation of migrating enterprise applications into the cloud, Enterprise Information Systems, 1-33, 2020.
  • Taktak H, Moussa F. A service-oriented application creation process in ubiquitous environments: travel assistant mobile application, Int J Perva Comput Comm, 13 300-330, 2017.
  • Sofia, A. S., & GaneshKumar, P. Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II, Journal of Network and Systems Management, 26(2) 463-485, 2018.
  • Jena, R. K. Multi objective task scheduling in cloud environment using nested PSO framework, Procedia Computer Science, 57 1219-1227, 2015.
  • Zhu X, Chen C, Yang LT, Xiang Y. ANGEL: agent-based scheduling for real-time tasks in virtualized clouds, IEEE Trans Comput., 64 3389-3403, 2015.
  • Ramezani, F., Lu, J., & Hussain, F. Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization, In International Conference on Service-oriented computing (pp. 237-251), Springer, Berlin, Heidelberg, December, 2013.
  • Ramezani F, Lu J, Taheri J, Hussain FK. Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments, World Wide Web., 18 1737-1757, 2015.
  • Moschakis I. A, Karatza H.D. Multi-criteria scheduling of Bag-of-Tasks applications on heterogeneous interlinked clouds with simulated annealing, J Syst Soft., 101 1-14, 2015.
  • Wang W-J, Chang Y-S, Lo W-T, Lee Y-K. Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments, J Supercomp., 66 783-811, 2013.
  • Frincu, M. E., & Craciun, C. Multi-objective meta-heuristics for scheduling applications with high availability requirements and cost constraints in multi-cloud environments, In 2011 fourth IEEE international conference on utility and cloud computing (pp. 267-274), IEEE, December, 2011.
  • Ma, H., da Silva, A. S., & Kuang, W. NSGA-II with local search for multi-objective application deployment in multi-cloud, In 2019 IEEE Congress on Evolutionary Computation (CEC) (pp. 2800-2807), IEEE, June, 2019.
  • Pang, S., Li, W., He, H., Shan, Z., & Wang, X. An EDA-GA hybrid algorithm for multi-objective task scheduling in cloud computing, IEEE Access, 7 146379-146389, 2019.

Year 2020, Volume: 1 Issue: 1, 37 - 44, 27.12.2020

Sinem Akyol Bilal Alataş

Abstract

References

  • Mishra, S. K., Sahoo, B., & Parida, P. P. Load balancing in cloud computing: a big Picture, Journal of King Saud University-Computer and Information Sciences, 32(2) 149-158, 2020.
  • Amini Motlagh, A., Movaghar, A., & Rahmani, A. M. Task scheduling mechanisms in cloud computing: A systematic review, International Journal of Communication Systems, 33(6) e4302, 2020.
  • Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. A hybrid model of internet of things and cloud computing to manage big data in health services applications, Future generation computer systems, 86, 1383-1394, 2018.
  • Bandaru, S., & Deb, K. Metaheuristic techniques, Decision Sciences (pp. 693-750), CRC Press, 2016.
  • Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhang, Q. Multiobjective evolutionary algorithms: A survey of the state of the art, Swarm and Evolutionary Computation, 1(1) 32-49, 2011.
  • Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization, Expert Systems with Applications, 47 106-119, 2016.
  • Zavala, G. R., Nebro, A. J., Luna, F., & Coello, C. A. C. A survey of multi-objective metaheuristics applied to structural optimization, Structural and Multidisciplinary Optimization, 49(4) 537-558, 2014.
  • Hosseinzadeh, M., Ghafour, M. Y., Hama, H. K., Vo, B., & Khoshnevis, A. Multi-objective task and workflow scheduling approaches in cloud computing: a comprehensive review, Journal of Grid Computing, 1-30, 2020.
  • Knowles, J. D., & Corne, D. W. Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary computation, 8(2) 149-172, 2000.
  • Corne, D. W., Jerram, N. R., Knowles, J. D., & Oates, M. J. PESA-II: Region-based selection in evolutionary multiobjective optimization. In Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (pp. 283-290), July, 2001.
  • Zitzler, E., Laumanns, M., & Thiele, L. SPEA2: Improving the strength Pareto evolutionary algorithm, TIK-report, 103, 2001.
  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2) 182-197, 2002.
  • Coello, C. C., & Lechuga, M. S. MOPSO: A proposal for multiple objective particle swarm optimization, In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1051-1056). IEEE, May, 2002.
  • Zhang, Q., & Li, H. MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 11(6) 712-731, 2007.
  • Panda, S. K., & Jana, P. K. A multi-objective task scheduling algorithm for heterogeneous multi-cloud environment, In 2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV) (pp. 82-87), IEEE, January, 2015.
  • Li, C., Song, M., Zhang, M., & Luo, Y. Effective replica management for improving reliability and availability in edge-cloud computing environment, Journal of Parallel and Distributed Computing, 2020.
  • Gupta, S., Agarwal, I., & Singh, R. S. Workflow scheduling using Jaya algorithm in cloud, Concurrency and Computation: Practice and Experience, 31(17) e5251, 2019.
  • Li, S., & Sun, W. Utility maximisation for resource allocation of migrating enterprise applications into the cloud, Enterprise Information Systems, 1-33, 2020.
  • Taktak H, Moussa F. A service-oriented application creation process in ubiquitous environments: travel assistant mobile application, Int J Perva Comput Comm, 13 300-330, 2017.
  • Sofia, A. S., & GaneshKumar, P. Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II, Journal of Network and Systems Management, 26(2) 463-485, 2018.
  • Jena, R. K. Multi objective task scheduling in cloud environment using nested PSO framework, Procedia Computer Science, 57 1219-1227, 2015.
  • Zhu X, Chen C, Yang LT, Xiang Y. ANGEL: agent-based scheduling for real-time tasks in virtualized clouds, IEEE Trans Comput., 64 3389-3403, 2015.
  • Ramezani, F., Lu, J., & Hussain, F. Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization, In International Conference on Service-oriented computing (pp. 237-251), Springer, Berlin, Heidelberg, December, 2013.
  • Ramezani F, Lu J, Taheri J, Hussain FK. Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments, World Wide Web., 18 1737-1757, 2015.
  • Moschakis I. A, Karatza H.D. Multi-criteria scheduling of Bag-of-Tasks applications on heterogeneous interlinked clouds with simulated annealing, J Syst Soft., 101 1-14, 2015.
  • Wang W-J, Chang Y-S, Lo W-T, Lee Y-K. Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments, J Supercomp., 66 783-811, 2013.
  • Frincu, M. E., & Craciun, C. Multi-objective meta-heuristics for scheduling applications with high availability requirements and cost constraints in multi-cloud environments, In 2011 fourth IEEE international conference on utility and cloud computing (pp. 267-274), IEEE, December, 2011.
  • Ma, H., da Silva, A. S., & Kuang, W. NSGA-II with local search for multi-objective application deployment in multi-cloud, In 2019 IEEE Congress on Evolutionary Computation (CEC) (pp. 2800-2807), IEEE, June, 2019.
  • Pang, S., Li, W., He, H., Shan, Z., & Wang, X. An EDA-GA hybrid algorithm for multi-objective task scheduling in cloud computing, IEEE Access, 7 146379-146389, 2019.
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Software Engineering
Journal Section Research Articles
Authors

Sinem Akyol This is me

Bilal Alataş This is me

Publication Date December 27, 2020
Submission Date December 14, 2020
Published in Issue Year 2020 Volume: 1 Issue: 1

Cite

APA Akyol, S., & Alataş, B. (2020). Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 1(1), 37-44.
AMA Akyol S, Alataş B. Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi. December 2020;1(1):37-44.
Chicago Akyol, Sinem, and Bilal Alataş. “Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi”. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 1, no. 1 (December 2020): 37-44.
EndNote Akyol S, Alataş B (December 1, 2020) Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 1 1 37–44.
IEEE S. Akyol and B. Alataş, “Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi”, Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 1, no. 1, pp. 37–44, 2020.
ISNAD Akyol, Sinem - Alataş, Bilal. “Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi”. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 1/1 (December 2020), 37-44.
JAMA Akyol S, Alataş B. Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi. 2020;1:37–44.
MLA Akyol, Sinem and Bilal Alataş. “Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi”. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 1, no. 1, 2020, pp. 37-44.
Vancouver Akyol S, Alataş B. Bulut Bilişim’de Çok Amaçlı Optimizasyon Tabanlı Görev Planlama Mekanizmalarının İncelenmesi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi. 2020;1(1):37-44.