Retracted : Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm


Tarun Kumar Ghosh
Krishna Gopal Dhal
Sanjoy Das


The cloud computing has emerged as a novel distributed computing system in past few years. It provides computation and resources over the Internet via dynamic provisioning of services. There are quite a few challenges and issues connected with implementation of cloud computing. This paper considers one of its major problems, i.e. task scheduling. The function of an efficient task scheduling algorithm is that it concentrates not only on attaining the requirements of the user but also in enhancing the efficiency of the cloud computing system. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. This paper proposes a modified Penguins Search Optimization Algorithm (MPeSOA) for efficient cloud task scheduling. The main contribution of our work is to schedule all tasks to available virtual machines so that the makespan is minimized, resource utilization is increased and the degree of imbalance is reduced. The proposed scheduling algorithm was simulated using the CloudSim 4.0 simulator. Experimental results showed that the proposed MPeSOA outperformed three existing meta-heuristics, namely Penguins Search Optimization Algorithm (PeSOA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).


How to Cite
Tarun Kumar Ghosh, Krishna Gopal Dhal, & Sanjoy Das. (2023). Retracted : Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm. International Journal of Next-Generation Computing, 14(2).


  1. Belalem, G., Tayeb, F. Z., and Zaoui, W. 2010. Approaches to improve the resources management in the simulator cloudsim. In Information Computing and Applications: First International Conference, ICICA 2010, Tangshan, China, October 15-18, 2010. Proceedings 1. Springer, 189–196. DOI:
  2. Buyya, R., Ranjan, R., and Calheiros, R. N. 2009. Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: Challenges and opportunities. In 2009 international conference on high performance computing & simulation. IEEE, 1–11. DOI:
  3. Calheiros, R. N.; rajiv ranjan; anton beloglazov; de rose, cesar, af; buyya, rajku- mar.(2011).“cloudsim: A toolkit for modeling and simulation of cloud computing envi- ronments and evaluation of resource provisioning algorithms. Software: Practice and Ex- perience (SPE) 41, 1, 23–50. DOI:
  4. Gheraibia, Y. and Moussaoui, A. 2013. Penguins search optimization algorithm (pesoa). In Recent Trends in Applied Artificial Intelligence: 26th International Conference on Indus- trial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, DOI:
  5. Amsterdam, The Netherlands, June 17-21, 2013. Proceedings 26. Springer, 222–231. DOI:
  6. Goyal, T., Singh, A., and Agrawal, A. 2012. Cloudsim: simulator for cloud computing infrastructure and modeling. Procedia Engineering 38, 3566–3572. DOI:
  7. Ibrahim, E., El-Bahnasawy, N. A., and Omara, F. A. 2016. Task scheduling algorithm in cloud computing environment based on cloud pricing models. In 2016 World Symposium on Computer Applications & Research (WSCAR). IEEE, 65–71. DOI:
  8. Jacob, L. 2014. Bat algorithm for resource scheduling in cloud computing. population 5, 18, 23. Lim, S.-H., Sharma, B., Nam, G., Kim, E. K., and Das, C. R. 2009. Mdcsim: A multi- tier data center simulation, platform. In 2009 IEEE International Conference on Cluster
  9. Computing and Workshops. IEEE, 1–9.
  10. Liu, Z. and Wang, X. 2012. A pso-based algorithm for load balancing in virtual machines of cloud computing environment. In Advances in Swarm Intelligence: Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part I 3. Springer,
  11. –147.
  12. Masdari, M., Salehi, F., Jalali, M., and Bidaki, M. 2017. A survey of pso-based scheduling algorithms in cloud computing. Journal of Network and Systems Management 25, 1, 122– 158. DOI:
  13. Mathew, T., Sekaran, K. C., and Jose, J. 2014. Study and analysis of various task schedul- ing algorithms in the cloud computing environment. In 2014 International conference on advances in computing, communications and informatics (ICACCI). IEEE, 658–664. DOI:
  14. Ramezani, F., Lu, J., and Hussain, F. 2013. Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization. In Service-Oriented Computing: 11th DOI:
  15. International Conference, ICSOC 2013, Berlin, Germany, December 2-5, 2013, Proceedings
  16. Springer, 237–251.
  17. Rathore, J., Keswani, B., and Rathore, V. S. 2015. ‘review of various load balancing techniques in cloud computing. Comput. Sci. Electron. J 7, 1, 5.
  18. Senthil Kumar, A. and Venkatesan, M. 2019. Multi-objective task scheduling using hy- brid genetic-ant colony optimization algorithm in cloud environment. Wireless Personal Communications 107, 1835–1848. DOI:
  19. Sidhu, M. S., Thulasiraman, P., and Thulasiram, R. K. 2013. A load-rebalance pso heuristic for task matching in heterogeneous computing systems. In 2013 IEEE Symposium on Swarm Intelligence (SIS). IEEE, 180–187. DOI:
  20. Singh, H., Tyagi, S., Kumar, P., Gill, S. S., and Buyya, R. 2021. Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions. Simulation Modelling Practice and Theory 111, 102353. DOI:
  21. Singh, K., Alam, M., and Sharma, S. K. 2015. A survey of static scheduling algorithm for distributed computing system. International Journal of Computer Applications 129, 2, DOI:
  22. –30.
  23. Singh, S. and Kalra, M. 2014. Scheduling of independent tasks in cloud computing using modified genetic algorithm. In 2014 International Conference on Computational Intelligence and Communication Networks. IEEE, 565–569. DOI:
  24. Sudha, K. and Sukumaran, S. 2015. Coherent genetic algorithm for task scheduling in cloud computing environment. Aust. J. Basic Appl. Sci 9, 2, 1–8.
  25. Tsai, C.-W. and Rodrigues, J. J. 2013. Metaheuristic scheduling for cloud: A survey. IEEE Systems Journal 8, 1, 279–291. DOI:
  26. Wen, X., Huang, M., and Shi, J. 2012. Study on resources scheduling based on aco all- gorithm and pso algorithm in cloud computing. In 2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science. IEEE, 219–222. DOI:
  27. Wickremasinghe, B., Calheiros, R. N., and Buyya, R. 2010. Cloudanalyst: A cloudsim- based visual modeller for analysing cloud computing environments and applications. In 2010 24th IEEE international conference on advanced information networking and applications. IEEE, 446–452. DOI:
  28. Xu, Z., Xu, X., and Zhao, X. 2015. Task scheduling based on multi-objective genetic algo- rithm in cloud computing. JOURNAL OF INFORMATION &COMPUTATIONAL SCI- ENCE 12, 4, 1429–1438. DOI:
  29. Zhan, S. and Huo, H. 2012. Improved pso-based task scheduling algorithm in cloud computing. Journal of Information & Computational Science 9, 13, 3821–3829.