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A review of task scheduling in cloud computing based on nature-inspired optimization algorithm

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Abstract

The advent of the cloud computing paradigm allowed multiple organizations to move, compute, and host their applications in the cloud environment, enabling seamless access to a wide range of services with minimal effort. An efficient and dynamic task scheduler is required to handle concurrent user requests for cloud services using various heterogeneous and diversified resources. Improper scheduling can lead to challenges with under or over-utilization of resources, which could waste cloud resources or degrade service performance. Nature-inspired optimization techniques have been proven effective at solving scheduling problems. This paper accomplishes a review of nature-inspired optimization techniques for scheduling tasks in cloud computing. A novel classification taxonomy and comparative review of these techniques in cloud computing are presented in this research. The taxonomy of nature-inspired scheduling techniques is categorized as per the scheduling algorithms, nature of the scheduling problem, type of tasks, the primary objective of scheduling, task-resource mapping scheme, scheduling constraint, and testing environment. Additionally, guidelines for future research issues are also provided, which should undoubtedly benefit researchers and practitioners as well as open the door for newcomers eager to pursue their glory in the field of cloud task scheduling.

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References

  1. Kaur, R., Laxmi, V.: Performance evaluation of task scheduling algorithms in virtual cloud environment to minimize makespan. Int. J. Inf. Technol. (2022). https://doi.org/10.1007/s41870-021-00753-4

    Article  Google Scholar 

  2. Gawali, M.B., Shinde, S.K.: Task scheduling and resource allocation in cloud computing using a heuristic approach. J. Cloud Comput. 7(1), 1–16 (2018)

    Article  Google Scholar 

  3. Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput. 14, 217–264 (2016)

    Article  Google Scholar 

  4. Mathew, T., Sekaran, K.C. and Jose, J., 2014, September. Study and analysis of various task scheduling algorithms in the cloud computing environment. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 658–664). IEEE.

  5. Xu, L., Qiao, J., Lin, S., Zhang, W.: Dynamic task scheduling algorithm with deadline constraint in heterogeneous volunteer computing platforms. Future Internet 11(6), 121 (2019)

    Article  Google Scholar 

  6. Damodaran, P., Chang, P.Y.: Heuristics to minimize makespan of parallel batch processing machines. Int. J. Adv. Manuf. Technol. 37, 1005–1013 (2008)

    Article  Google Scholar 

  7. Kim, S.I., Kim, J.K.: A method to construct task scheduling algorithms for heterogeneous multi-core systems. IEEE Access 7, 142640–142651 (2019)

    Article  Google Scholar 

  8. Pinedo, M. and Hadavi, K., 1992. Scheduling: theory, algorithms and systems development. In Operations Research Proceedings 1991: Papers of the 20th Annual Meeting/Vorträge der 20. Jahrestagung (pp. 35–42). Springer, Berlin

  9. Houssein, E.H., Gad, A.G., Wazery, Y.M., Suganthan, P.N.: Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm Evol. Comput. 62, 100841 (2021)

    Article  Google Scholar 

  10. Singh, H., Tyagi, S., Kumar, P.: Scheduling in cloud computing environment using metaheuristic techniques: a survey. In: Shal, V. (ed.) Emerging technology in modelling and graphics: proceedings of IEM graph 2018, pp. 753–763. Springer Singapore, Singapore (2020)

    Chapter  Google Scholar 

  11. Liu, Y., Zhang, C., Li, B., Niu, J.: DeMS: A hybrid scheme of task scheduling and load balancing in computing clusters. J. Netw. Comput. Appl. 83, 213–220 (2017)

    Article  Google Scholar 

  12. Kumar, D.: Review on task scheduling in ubiquitous clouds. J. ISMAC 1(01), 72–80 (2019)

    Google Scholar 

  13. Allahverdi, A., Ng, C.T., Cheng, T.E., Kovalyov, M.Y.: A survey of scheduling problems with setup times or costs. Eur. J. Oper. Res. 187(3), 985–1032 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  14. Remesh Babu, K.R. and Samuel, P., 2016. Enhanced bee colony algorithm for efficient load balancing and scheduling in cloud. In Innovations in Bio-Inspired Computing and Applications: Proceedings of the 6th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2015) held in Kochi, India during December 16–18, 2015 (pp. 67–78). Springer International Publishing.

  15. Taillard, E.: Some efficient heuristic methods for the flow shop sequencing problem. Eur. J. Oper. Res. 47(1), 65–74 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  16. Morton, T., Pentico, D.W.: Heuristic scheduling systems: with applications to production systems and project management. John Wiley, Hoboken (1993)

    Google Scholar 

  17. Bissoli, D.C., Altoe, W.A., Mauri, G.R. and Amaral, A.R., 2018, August. A simulated annealing metaheuristic for the bi-objective flexible job shop scheduling problem. In 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE) (pp. 1–6). IEEE.

  18. Gong, G., Chiong, R., Deng, Q., Gong, X.: A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility. Int. J. Prod. Res. 58(14), 4406–4420 (2020)

    Article  Google Scholar 

  19. Zarrouk, R., Bennour, I.E., Jemai, A.: A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem. Swarm Intell. 13, 145–168 (2019)

    Article  Google Scholar 

  20. Sörensen, K., Glover, F.: Metaheuristics. Encycl. Operations Res. Manag. Sci. 62, 960–970 (2013)

    Article  Google Scholar 

  21. Garg, D. and Kumar, P., 2019. A survey on metaheuristic approaches and its evaluation for load balancing in cloud computing. In Advanced Informatics for Computing Research: Second International Conference, ICAICR 2018, Shimla, India, July 14–15, 2018, Revised Selected Papers, Part I 2 (pp. 585–599). Springer Singapore.

  22. Kaur, N. and Chhabra, A., 2016, March. Analytical review of three latest nature inspired algorithms for scheduling in clouds. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 3296–3300). IEEE.

  23. Garg, D. and Kumar, P., 2019. A survey on metaheuristic approaches and its evaluation for load balancing in cloud computing. In Advanced Informatics for Computing Research: Second International Conference, ICAICR 2018, Shimla, India, July 14–15, 2018, Revised Selected Papers, Part I 2 (pp. 585–599). Springer Singapore.

  24. Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inf. J. 16(3), 275–295 (2015)

    Google Scholar 

  25. Kaur, N. and Chhabra, A., 2016, March. Analytical review of three latest nature inspired algorithms for scheduling in clouds. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 3296–3300). IEEE.

  26. Tsai, C.W., Rodrigues, J.J.: Metaheuristic scheduling for cloud: a survey. IEEE Syst. J. 8(1), 279–291 (2013)

    Article  Google Scholar 

  27. Nandhakumar, C. and Ranjithprabhu, K., 2015, January. Heuristic and meta-heuristic workflow scheduling algorithms in multi-cloud environments—a survey. In 2015 International Conference on Advanced Computing and Communication Systems (pp. 1–5). IEEE.

  28. Hatchuel, A., Saidi-Kabeche, D., Sardas, J.C.: Towards a new planning and scheduling approach for multistage production systems. Int. J. Prod. Res. 35(3), 867–886 (1997)

    Article  MATH  Google Scholar 

  29. Lawler, E.L., Lenstra, J.K. and Rinnooy Kan, A.H.G., 1982. Recent developments in deterministic sequencing and scheduling: a survey. In Deterministic and Stochastic Scheduling: Proceedings of the NATO Advanced Study and Research Institute on Theoretical Approaches to Scheduling Problems held in Durham, England, July 6–17, 1981 (pp. 35–73). Springer Netherlands.

  30. Madni, S.H.H., Abd Latiff, M.S., Abdullahi, M., Abdulhamid, S.I.M., Usman, M.J.: Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE 12(5), e0176321 (2017)

    Article  Google Scholar 

  31. Mazumder, A.M.R., Uddin, K.A., Arbe, N., Jahan, L. and Whaiduzzaman, M., 2019, June. Dynamic task scheduling algorithms in cloud computing. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1280–1286). IEEE.

  32. Chowdhury, N., M Aslam Uddin, K., Afrin, S., Adhikary, A., Rabbi, F.: Performance evaluation of various scheduling algorithm based on cloud computing system. Asian J. Res. Comput. Sci. 2(1), 1–6 (2018)

    Article  Google Scholar 

  33. Balharith, T. and Alhaidari, F., 2019, May. Round robin scheduling algorithm in CPU and cloud computing: a review. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1–7). IEEE.

  34. Zhao, H. and Sakellariou, R., 2003. An experimental investigation into the rank function of the heterogeneous earliest finish time scheduling algorithm. In Euro-Par 2003 Parallel Processing: 9th International Euro-Par Conference Klagenfurt, Austria, August 26-29, 2003 Proceedings 9 (pp. 189-194). Springer, Berlin

  35. Li, B., Niu, L., Huang, X., Wu, H. and Pei, Y., 2018, December. Minimum completion time offloading algorithm for mobile edge computing. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC) (pp. 1929–1933). IEEE.

  36. Krishnaveni, H., Sinthujanitaprakash, V.: Execution time based sufferage algorithm for static task scheduling in cloud. In: Advances in big data and cloud computing: Proceedings of ICBDCC18, pp. 61–70. Springer Singapore, Singapore (2019)

    Chapter  Google Scholar 

  37. Chen, H., Wang, F., Helian, N. and Akanmu, G., 2013, February. User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing. In 2013 national conference on parallel computing technologies (PARCOMPTECH) (pp. 1–8). IEEE.

  38. George Amalarethinam, D.I. and Kavitha, S., 2019. Rescheduling enhanced Min-Min (REMM) algorithm for meta-task scheduling in cloud computing. In International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018 (pp. 895–902). Springer International Publishing.

  39. Mao, Y., Chen, X. and Li, X., 2014. Max–min task scheduling algorithm for load balance in cloud computing. In Proceedings of International Conference on Computer Science and Information Technology: CSAIT 2013, September 21–23, 2013, Kunming, China (pp. 457–465). Springer India.

  40. Sandana Karuppan, A., Meena Kumari, S.A. and Sruthi, S., 2019. A priority-based max-min scheduling algorithm for cloud environment using fuzzy approach. In International Conference on Computer Networks and Communication Technologies: ICCNCT 2018 (pp. 819–828). Springer Singapore.

  41. Zhou, X., Zhang, G., Sun, J., Zhou, J., Wei, T., Hu, S.: Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT. Futur. Gener. Comput. Syst. 93, 278–289 (2019)

    Article  Google Scholar 

  42. Tong, Z., Deng, X., Chen, H., Mei, J., Liu, H.: QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment. Neural Comput. Appl. 32, 5553–5570 (2020)

    Article  Google Scholar 

  43. Nazar, T., Javaid, N., Waheed, M., Fatima, A., Bano, H. and Ahmed, N., 2019. Modified shortest job first for load balancing in cloud-fog computing. In Advances on Broadband and Wireless Computing, Communication and Applications: Proceedings of the 13th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2018) (pp. 63–76). Springer International Publishing.

  44. Alworafi, M.A., Dhari, A., Al-Hashmi, A.A. and Darem, A.B., 2016, December. An improved SJF scheduling algorithm in cloud computing environment. In 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT) (pp. 208–212). IEEE.

  45. Seth, S., Singh, N.: Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems. Int. J. Inf. Technol. 11(4), 653–657 (2019)

    Google Scholar 

  46. Devi, D.C., Uthariaraj, V.R.: Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. Sci. World J. (2016). https://doi.org/10.1155/2016/3896065

    Article  Google Scholar 

  47. Venkataraman, N.: Threshold based multi-objective memetic optimized round robin scheduling for resource efficient load balancing in cloud. Mobile Netw. Appl. 24, 1214–1225 (2019)

    Article  Google Scholar 

  48. Krishnaveni, H., Janita, V.S.: Completion time based sufferage algorithm for static task scheduling in cloud environment. Int. J. Pure Appl. Math. 119(12), 13793–13797 (2018)

    Google Scholar 

  49. Dutta, M. and Aggarwal, N., 2016. Meta-heuristics based approach for workflow scheduling in cloud computing: a survey. In Artificial Intelligence and Evolutionary Computations in Engineering Systems: Proceedings of ICAIECES 2015 (pp. 1331–1345). Springer India.

  50. Wu, F., Wu, Q., Tan, Y.: Workflow scheduling in cloud: a survey. J. Supercomput. 71, 3373–3418 (2015)

    Article  Google Scholar 

  51. Alkhanak, E.N., Lee, S.P., Khan, S.U.R.: Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities. Futur. Gener. Comput. Syst. 50, 3–21 (2015)

    Article  Google Scholar 

  52. Masdari, M., ValiKardan, S., Shahi, Z., Azar, S.I.: Towards workflow scheduling in cloud computing: a comprehensive analysis. J. Netw. Comput. Appl. 66, 64–82 (2016)

    Article  Google Scholar 

  53. Fister, I., Jr., Yang, X.S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. Neural Evol. Comput. (2013). https://doi.org/10.48550/arXiv.1307.4186

    Article  MATH  Google Scholar 

  54. Yang, X.S., He, X.: Nature-inspired optimization algorithms in engineering: overview and applications. Nat. -Inspired Comput. Eng. (2016). https://doi.org/10.1007/978-3-319-30235-5_1

    Article  Google Scholar 

  55. Nanda, S.J., Panda, G.: A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol. Comput. 16, 1–18 (2014)

    Article  Google Scholar 

  56. Ss, V.C., Hs, A.: Nature inspired meta heuristic algorithms for optimization problems. Computing 104(2), 251–269 (2022)

    Article  MathSciNet  Google Scholar 

  57. Mirjalili, S., Mirjalili, S.: Genetic algorithm. Evol. Algorithms Neural Netw.: Theory Appl. 780, 43–55 (2019)

    Article  Google Scholar 

  58. Wang, Z., Tang, K., Yao, X.: A memetic algorithm for multi-level redundancy allocation. IEEE Trans. Reliab. 59(4), 754–765 (2010)

    Article  Google Scholar 

  59. Tilahun, S.L., Kassa, S.M. and Ong, H.C., 2012. A new algorithm for multilevel optimization problems using evolutionary strategy, inspired by natural adaptation. In PRICAI 2012: Trends in Artificial Intelligence: 12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia, September 3–7, 2012. Proceedings 12 (pp. 577–588). Springer Berlin Heidelberg.

  60. Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft. Comput. 9, 815–834 (2005)

    Article  MATH  Google Scholar 

  61. Gandomi, M., Kashani, A.R., Farhadi, A., Akhani, M., Gandomi, A.H.: Spectral acceleration prediction using genetic programming based approaches. Appl. Soft Comput. 106, 107326 (2021)

    Article  Google Scholar 

  62. Hussain, I., Ullah, I., Ali, W., Muhammad, G., Ali, Z.: Exploiting lion optimization algorithm for sustainable energy management system in industrial applications. Sustain. Energy Technol. Assess. 52, 102237 (2022)

    Google Scholar 

  63. Hosseini, S., Al Khaled, A.: A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research. Appl. Soft Comput. 24, 1078–1094 (2014)

    Article  Google Scholar 

  64. Gomes, G.F., da Cunha, S.S., Ancelotti, A.C.: A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Eng. Comput. 35, 619–626 (2019)

    Article  Google Scholar 

  65. Guo, W., Chen, M., Wang, L., Mao, Y., Wu, Q.: A survey of biogeography-based optimization. Neural Comput. Appl. 28, 1909–1926 (2017)

    Article  Google Scholar 

  66. Aguilar-Rivera, R., Valenzuela-Rendón, M., Rodríguez-Ortiz, J.J.: Genetic algorithms and Darwinian approaches in financial applications: a survey. Expert Syst. Appl. 42(21), 7684–7697 (2015)

    Article  Google Scholar 

  67. Zames, G.: Genetic algorithms in search, optimization and machine learning. Inf Tech J 3(1), 301 (1981)

    MATH  Google Scholar 

  68. Dasgupta, K., Mandal, B., Dutta, P., Mandal, J.K., Dam, S.: A genetic algorithm (ga) based load balancing strategy for cloud computing. Procedia Technol. 10, 340–347 (2013)

    Article  Google Scholar 

  69. Ge, Y. and Wei, G., 2010, October. GA-based task scheduler for the cloud computing systems. In 2010 International Conference on Web Information Systems and Mining (Vol. 2, pp. 181–186). IEEE.

  70. Zheng, Z., Wang, R., Zhong, H. and Zhang, X., 2011, March. An approach for cloud resource scheduling based on Parallel Genetic Algorithm. In 2011 3rd International Conference on Computer Research and Development (Vol. 2, pp. 444–447). IEEE.

  71. Wang, T., Liu, Z., Chen, Y., Xu, Y. and Dai, X., 2014, August. Load balancing task scheduling based on genetic algorithm in cloud computing. In 2014 IEEE 12th international conference on dependable, autonomic and secure computing (pp. 146–152). IEEE.

  72. Jang, S.H., Kim, T.Y., Kim, J.K., Lee, J.S.: The study of genetic algorithm-based task scheduling for cloud computing. Int. J. Cont. Autom. 5(4), 157–162 (2012)

    Google Scholar 

  73. Liu, J., Luo, X.G., Zhang, X.M., Zhang, F., Li, B.N.: Job scheduling model for cloud computing based on multi-objective genetic algorithm. Int. J. Comput. Sci. Issues (IJCSI) 10(1), 134 (2013)

    Google Scholar 

  74. Kaur, K., Chhabra, A., Singh, G.: Heuristics based genetic algorithm for scheduling static tasks in homogeneous parallel system. Int. J. Comput. Sci. Security (IJCSS) 4(2), 183–198 (2010)

    Google Scholar 

  75. Ghorbannia Delavar, A., Aryan, Y.: HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Clust. Comput. 17, 129–137 (2014)

    Article  Google Scholar 

  76. Yu, J., Buyya, R.: Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci. Program. 14(3–4), 217–230 (2006)

    Google Scholar 

  77. Khajemohammadi, H., Fanian, A. and Gulliver, T.A., 2013, August. Fast workflow scheduling for grid computing based on a multi-objective genetic algorithm. In 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) (pp. 96–101). IEEE.

  78. Gu, J., Hu, J., Zhao, T., Sun, G.: A new resource scheduling strategy based on genetic algorithm in cloud computing environment. J. Comput. 7(1), 42–52 (2012)

    Article  Google Scholar 

  79. Huang, J.: The workflow task scheduling algorithm based on the GA model in the cloud computing environment. J. Softw. 9(4), 873–880 (2014)

    Article  Google Scholar 

  80. Nasonov, D., Butakov, N., Balakhontseva, M., Knyazkov, K. and Boukhanovsky, A.V., 2014. Hybrid evolutionary workflow scheduling algorithm for dynamic heterogeneous distributed computational environment. In International Joint Conference SOCO’14-CISIS’14-ICEUTE’14: Bilbao, Spain, June 25th-27th, 2014, Proceedings (pp. 83–92). Springer International Publishing.

  81. Szabo, C., Sheng, Q.Z., Kroeger, T., Zhang, Y., Yu, J.: Science in the cloud: allocation and execution of data-intensive scientific workflows. J. Grid Comput. 12, 245–264 (2014)

    Article  Google Scholar 

  82. Shen, G. and Zhang, Y.Q., 2011. A shadow price guided genetic algorithm for energy aware task scheduling on cloud computers. In Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I 2 (pp. 522-529). Springer Berlin Heidelberg.

  83. Kolodziej, J., Khan, S.U. and Xhafa, F., 2011, October. Genetic algorithms for energy-aware scheduling in computational grids. In 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 17–24). IEEE.

  84. Zhu, K., Song, H., Liu, L., Gao, J. and Cheng, G., 2011, December. Hybrid genetic algorithm for cloud computing applications. In 2011 IEEE Asia-Pacific Services Computing Conference (pp. 182–187). IEEE.

  85. Sawant, S., 2011. A genetic algorithm scheduling approach for virtual machine resources in a cloud computing environment.

  86. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurr. Comput. Program 826, 37 (1989)

    Google Scholar 

  87. Jouglet, A., Oğuz, C., Sevaux, M.: Hybrid flow-shop: a memetic algorithm using constraint-based scheduling for efficient search. J. Mathe. Model. Algorithms 8, 271–292 (2009)

    Article  MATH  Google Scholar 

  88. Moscato, P., Norman, M.G.: A memetic approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passing systems. Parallel Comput. Trans. Appl. 1, 177–186 (1992)

    Google Scholar 

  89. Kashani, M.H., Jahanshahi, M.: A new method based on memetic algorithm for task scheduling in distributed systems. Int. J. Simul. Syst. Sci. Technol. 10(5), 26–32 (2009)

    Google Scholar 

  90. Padmavathi, S., Shalinie, S.M., Abhilaash, R.: A memetic algorithm based task scheduling considering communication cost on cluster of workstations. Int. J. Adv. Soft Comput. Appl. 2, 174–190 (2010)

    Google Scholar 

  91. Sutar, S., Sawant, J. and Jadhav, J., 2006. Task scheduling for multiprocessor systems using memetic algorithms. In 4th International Working Conference Performance Modeling and Evaluation of Heterogeneous Networks (HET-NETs ‘06).

  92. Zhao, F., Tang, J.: A memetic algorithm combined particle swarm optimization with simulated annealing and its application on multiprocessor scheduling problem. Prz Elektrotechniczny 88, 292–296 (2012)

    Google Scholar 

  93. Atashpaz-Gargari, E. and Lucas, C., 2007, September. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE Congress on Evolutionary Computation (pp. 4661–4667). Ieee.

  94. Behnamian, J., Zandieh, M.: A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties. Expert Syst. Appl. 38(12), 14490–14498 (2011)

    Article  Google Scholar 

  95. Attar, S.F., Mohammadi, M., Tavakkoli-Moghaddam, R.: A novel imperialist competitive algorithm to solve flexible flow shop scheduling problem in order to minimize maximum completion time. Int. J. Comput. Appl. 28(10), 27–32 (2011)

    Google Scholar 

  96. Madani-Isfahani, M., Ghobadian, E., Tekmehdash, H., Tavakkoli-Moghaddam, R., Naderi-Beni, M.: An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration. Int. J. Ind. Eng. Comput. 4(2), 191–202 (2013)

    Google Scholar 

  97. Yakhchi, S., Ghafari, S.M., Yakhchi, M., Fazeli, M. and Patooghy, A., 2015, March. ICA-MMT: A load balancing method in cloud computing environment. In 2015 2nd World Symposium on Web Applications and Networking (WSWAN) (pp. 1–7). IEEE.

  98. Yousefyan, S., Dastjerdi, A.V. and Salehnamadi, M.R., 2013, May. Cost effective cloud resource provisioning with imperialist competitive algorithm optimization. In The 5th Conference on Information and Knowledge Technology (pp. 55–60). IEEE.

  99. Pooranian, Z., Shojafar, M., Javadi, B., Abraham, A.: Using imperialist competition algorithm for independent task scheduling in grid computing. J. Intell. Fuzzy Syst. 27(1), 187–199 (2014)

    Article  Google Scholar 

  100. Piroozfard, H. and Wong, K.Y., 2014, December. An imperialist competitive algorithm for the job shop scheduling problems. In 2014 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 69–73). IEEE.

  101. Jula, A., Othman, Z. and Sundararajan, E., 2013, April. A hybrid imperialist competitive-gravitational attraction search algorithm to optimize cloud service composition. In 2013 IEEE workshop on memetic computing (MC) (pp. 37–43). IEEE.

  102. Jula, A., Othman, Z., Sundararajan, E.: Imperialist competitive algorithm with PROCLUS classifier for service time optimization in cloud computing service composition. Expert Syst. Appl. 42(1), 135–145 (2015)

    Article  Google Scholar 

  103. Fatemipour, F. and Fatemipour, F., 2012. Scheduling scientific workflows using imperialist competitive algorithm. In International conference on industrial intelligent information (ICIII 2012) (pp. 218–225).

  104. Faragardi, H.R., Rajabi, A., Shojaee, R. and Nolte, T., 2013, November. Towards energy-aware resource scheduling to maximize reliability in cloud computing systems. In 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (pp. 1469–1479). IEEE.

  105. Rajakumar, B.R.: The Lion’s Algorithm: a new nature-inspired search algorithm. Procedia Technol. 6, 126–135 (2012)

    Article  Google Scholar 

  106. Yazdani, M., Jolai, F.: Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Design Eng. 3(1), 24–36 (2016)

    Article  Google Scholar 

  107. Emami, H.: Cloud task scheduling using enhanced sunflower optimization algorithm. Ict Express 8(1), 97–100 (2022)

    Article  Google Scholar 

  108. Subhash, L.S., Udayakumar, R.: Sunflower whale optimization algorithm for resource allocation strategy in cloud computing platform. Wireless Pers. Commun. 116, 3061–3080 (2021)

    Article  Google Scholar 

  109. Chandrashekar, C., Krishnadoss, P.: Opposition based sunflower optimization algorithm using cloud computing environments. Mater. Today: Proc. 62, 4896–4902 (2022)

    Article  Google Scholar 

  110. Jena, U.K., Kumar Das, P., Kabat, M.R., Kuanar, S.K.: Dynamic load balancing in cloud network through sunflower optimization algorithm and sine-cosine algorithm. In: Next generation of internet of things: proceedings of ICNGIoT 2022, pp. 609–621. Springer Nature Singapore, Singapore (2022)

    Google Scholar 

  111. Mahale, R.A., Chavan, S.D.: A survey: evolutionary and swarm based bio-inspired optimization algorithms. Int. J. Sci. Res. Publ. 2(12), 1–6 (2012)

    Google Scholar 

  112. Juneja, M. and Nagar, S.K., 2016, October. Particle swarm optimization algorithm and its parameters: A review. In 2016 International Conference on Control, Computing, Communication and Materials (ICCCCM) (pp. 1–5). IEEE.

  113. Yuce, B., Packianather, M.S., Mastrocinque, E., Pham, D.T., Lambiase, A.: Honey bees inspired optimization method: the bees algorithm. Insects 4(4), 646–662 (2013)

    Article  Google Scholar 

  114. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  115. Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firefly algorithm: performance study. Swarm Evol. Comput. 1(3), 164–171 (2011)

    Article  Google Scholar 

  116. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)

    Article  Google Scholar 

  117. Blum, C.: Ant colony optimization: Introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005)

    Article  Google Scholar 

  118. Neshat, M., Sepidnam, G., Sargolzaei, M., Toosi, A.N.: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 42(4), 965–997 (2014)

    Article  Google Scholar 

  119. Yang, X.S., He, X.: Bat algorithm: literature review and applications. Int. J. Bio-inspired Comput. 5(3), 141–149 (2013)

    Article  Google Scholar 

  120. Ahmed, A.M., Rashid, T.A., Saeed, S.A.M.: Cat swarm optimization algorithm: a survey and performance evaluation. Comput. Intell. Neurosci. (2020). https://doi.org/10.1155/2016/3896065

    Article  Google Scholar 

  121. Ajith, A., Crina, G., Vitorino, R., Martin, R., Stephen, W.: Termite: a swarm intelligent routing algorithm for mobilewireless Ad-Hoc networks, pp. 155–184. Springer, Berlin (2006)

    Google Scholar 

  122. Pinto, P., Runkler, T.A. and Sousa, J.M., 2005. Wasp swarm optimization of logistic systems. In Adaptive and Natural Computing Algorithms: Proceedings of the International Conference in Coimbra, Portugal, 2005 (pp. 264–267). Springer Vienna.

  123. Chen, X., Zhou, Y., Luo, Q.: A hybrid monkey search algorithm for clustering analysis. Sci. World J. (2014). https://doi.org/10.1155/2014/938239

    Article  Google Scholar 

  124. YongBo, C., YueSong, M., JianQiao, Y., XiaoLong, S., Nuo, X.: Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm. Neurocomputing 266, 445–457 (2017)

    Article  Google Scholar 

  125. Lu, X. and Zhou, Y., 2008. A novel global convergence algorithm: bee collecting pollen algorithm. In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence: 4th International Conference on Intelligent Computing, ICIC 2008 Shanghai, China, September 15–18, 2008 Proceedings 4 (pp. 518–525). Springer Berlin Heidelberg.

  126. Kenan Dosoglu, M., Guvenc, U., Duman, S., Sonmez, Y., Tolga Kahraman, H.: Symbiotic organisms search optimization algorithm for economic/emission dispatch problem in power systems. Neural Comput. Appl. 29, 721–737 (2018)

    Article  Google Scholar 

  127. Meraihi, Y., Gabis, A.B., Ramdane-Cherif, A., Acheli, D.: A comprehensive survey of crow search algorithm and its applications. Artif. Intell. Rev. 54(4), 2669–2716 (2021)

    Article  Google Scholar 

  128. Dhanya, D., Arivudainambi, D.: Dolphin partner optimization based secure and qualified virtual machine for resource allocation with streamline security analysis. Peer-to-Peer Netw. Appl. 12, 1194–1213 (2019)

    Article  Google Scholar 

  129. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  130. Pilla, R., Botcha, N., Gorripotu, T.S. and Azar, A.T., 2020. Fuzzy PID controller for automatic generation control of interconnected power system tuned by glow-worm swarm optimization. In Applications of Robotics in Industry Using Advanced Mechanisms: Proceedings of International Conference on Robotics and Its Industrial Applications 2019 1 (pp. 140–149). Springer International Publishing.

  131. Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344(2–3), 243–278 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  132. Chiang, C.W., Lee, Y.C., Lee, C.N., Chou, T.Y.: Ant colony optimisation for task matching and scheduling. IEE Proc. –Comput. Digital Tech. 153(6), 373–380 (2006)

    Article  Google Scholar 

  133. Chen, W.N., Zhang, J. and Yu, Y., 2007, September. Workflow scheduling in grids: an ant colony optimization approach. In 2007 IEEE Congress on Evolutionary Computation (pp. 3308–3315). IEEE.

  134. Chen, W.N., Shi, Y. and Zhang, J., 2009, May. An ant colony optimization algorithm for the time-varying workflow scheduling problem in grids. In 2009 IEEE Congress on Evolutionary Computation (pp. 875–880). IEEE.

  135. Pacini, E., Mateos, C., Garino, C.G.: Balancing throughput and response time in online scientific clouds via ant colony optimization (SP2013/2013/00006). Adv. Eng. Softw. 84, 31–47 (2015)

    Article  Google Scholar 

  136. Liu, X.F., Zhan, Z.H., Du, K.J. and Chen, W.N., 2014, July. Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. In Proceedings of the 2014 annual conference on genetic and evolutionary computation (pp. 41–48).

  137. Sivaraju, S.S., Kumar, C.: Energy enhancement of WSN with deep learning based SOM scheduling algorithm. J. Inf. Technol. Digital World 4(3), 238–249 (2022)

    Article  Google Scholar 

  138. Mathiyalagan, P., Suriya, S., Sivanandam, S.N.: Modified ant colony algorithm for grid scheduling. Int. J. Comput. Sci. Eng. 2(02), 132–139 (2010)

    Google Scholar 

  139. Liu, A. and Wang, Z., 2008, October. Grid task scheduling based on adaptive ant colony algorithm. In 2008 International conference on management of e-commerce and e-government (pp. 415–418). IEEE.

  140. Bagherzadeh, J. and MadadyarAdeh, M., 2009, October. An improved ant algorithm for grid scheduling problem. In 2009 14th International CSI Computer Conference (pp. 323–328). IEEE.

  141. Chen, W.N., Zhang, J.: An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements. IEEE Trans. Syst. Man Cybernetics Part C 39(1), 29–43 (2008)

    Article  Google Scholar 

  142. Tawfeek, M.A., El-Sisi, A., Keshk, A.E. and Torkey, F.A., 2013, November. Cloud task scheduling based on ant colony optimization. In 2013 8th international conference on computer engineering & systems (ICCES) (pp. 64–69). IEEE.

  143. Khambre, P.D., Deshpande, A., Mehta, A., Sain, A.: Modified pheromone update rule to implement ant colony optimization algorithm for workflow scheduling algorithm problem in grids. Int. J. Adv. Res. Comput. Sci. Technol. 2(2), 424–429 (2014)

    Google Scholar 

  144. Singh, L., Singh, S.: Deadline and cost based ant colony optimization algorithm for scheduling workflow applications in hybrid cloud. J. Sci. Eng. Res. 5(10), 1417–1420 (2014)

    Google Scholar 

  145. Eberhart, R. and Kennedy, J., 1995, November. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (Vol. 4, pp. 1942–1948).

  146. Pandey, S., Wu, L., Guru, S.M. and Buyya, R., 2010, April. 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 (pp. 400–407). IEEE.

  147. Wu, Z., Ni, Z., Gu, L. and Liu, X., 2010, December. A revised discrete particle swarm optimization for cloud workflow scheduling. In 2010 international conference on computational intelligence and security (pp. 184–188). IEEE.

  148. Xue, S.J., Wu, W.: Scheduling workflow in cloud computing based on hybrid particle swarm algorithm. Indonesian J. Electr. Eng. Comput. Sci. 10(7), 1560–1566 (2012)

    Google Scholar 

  149. Tavakkoli-Moghaddam, R., Azarkish, M., Sadeghnejad-Barkousaraie, A.: A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Expert Syst. Appl. 38(9), 10812–10821 (2011)

    Article  MATH  Google Scholar 

  150. Beegom, A.A. and Rajasree, M.S., 2014. A particle swarm optimization based pareto optimal task scheduling in cloud computing. In Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17–20, 2014, Proceedings, Part II 5 (pp. 79–86). Springer International Publishing.

  151. Karimi, M., Motameni, H.: Tasks scheduling in computational grid using a hybrid discrete particle swarm optimization. Int. J. Grid Distrib. Comput. 6(2), 29–38 (2013)

    Google Scholar 

  152. Pooranian, Z., Shojafar, M., Abawajy, J.H., Abraham, A.: An efficient meta-heuristic algorithm for grid computing. J. Comb. Optim. 30, 413–434 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  153. Krishnasamy, K.: Task scheduling algorithm based on hybrid particle swarm optimization in cloud computing environment. J. Theor. Appl. Inf. Technol. 55(1), 1–3 (2013)

    Google Scholar 

  154. Sridhar, M. and Babu, G.R.M., 2015, June. Hybrid particle swarm optimization scheduling for cloud computing. In 2015 IEEE International Advance Computing Conference (IACC) (pp. 1196–1200). IEEE.

  155. Al-maamari, A. and Omara, F.A., 2015. Task scheduling using hybrid algorithm in cloud computing environments. Journal of Computer Engineering (IOSR-JCE), 17(3), pp.96–106.

  156. Zhang, L., Chen, Y., Sun, R., Jing, S., Yang, B.: A task scheduling algorithm based on PSO for grid computing. Int. J. Comput. Intell. Res. 4(1), 37–43 (2008)

    Google Scholar 

  157. Liu, H., Abraham, A., Hassanien, A.E.: Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Futur. Gener. Comput. Syst. 26(8), 1336–1343 (2010)

    Article  Google Scholar 

  158. Aron, R., Chana, I., Abraham, A.: A hyper-heuristic approach for resource provisioning-based scheduling in grid environment. J. Supercomput. 71, 1427–1450 (2015)

    Article  Google Scholar 

  159. Sidhu, M.S., Thulasiraman, P. and Thulasiram, R.K., 2013, April. A load-rebalance PSO heuristic for task matching in heterogeneous computing systems. In 2013 IEEE Symposium on Swarm Intelligence (SIS) (pp. 180–187). IEEE.

  160. Ramezani, F., Lu, J., Hussain, F.K.: Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Prog. 42, 739–754 (2014)

    Article  Google Scholar 

  161. Milani, F.S., Navin, A.H.: Multi-objective task scheduling in the cloud computing based on the Patrice swarm optimization. Int. J. Inf. Technol. Comput. Sci. 7(5), 61–66 (2015)

    Google Scholar 

  162. Wang, Z., Shuang, K., Yang, L., Yang, F.: Energy-aware and revenue-enhancing combinatorial scheduling in virtualized of cloud datacenter. J. Converg. Inf. Technol. 7(1), 62–70 (2012)

    Google Scholar 

  163. Karaboga, D., 2005. An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1–10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.

  164. Liu, Y.F., Liu, S.Y.: A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem. Appl. Soft Comput. 13(3), 1459–1463 (2013)

    Article  Google Scholar 

  165. Huang, Y.M., Lin, J.C.: A new bee colony optimization algorithm with idle-time-based filtering scheme for open shop-scheduling problems. Expert Syst. Appl. 38(5), 5438–5447 (2011)

    Article  Google Scholar 

  166. Ziarati, K., Akbari, R., Zeighami, V.: On the performance of bee algorithms for resource-constrained project scheduling problem. Appl. Soft Comput. 11(4), 3720–3733 (2011)

    Article  Google Scholar 

  167. Karaboga, D. and Gorkemli, B., 2011, June. A combinatorial artificial bee colony algorithm for traveling salesman problem. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (pp. 50–53). IEEE.

  168. Hashemi, S.M., Hanani, A.: Solving the scheduling problem in computational grid using artificial bee colony algorithm. Adv. Comput. Sci.: Int. J. 2, 37–41 (2013)

    Google Scholar 

  169. Mousavinasab, Z., Entezari-Maleki, R. and Movaghar, A., 2011. A bee colony task scheduling algorithm in computational grids. In Digital Information Processing and Communications: International Conference, ICDIPC 2011, Ostrava, Czech Republic, July 7-9, 2011, Proceedings, Part I (pp. 200-210). Springer Berlin Heidelberg

  170. de Mello, R.F., Senger, L.J. and Yang, L.T., 2006, April. A routing load balancing policy for grid computing environments. In 20th International Conference on Advanced Information Networking and Applications-Volume 1 (AINA'06) (Vol. 1, pp. 6-pp). IEEE.

  171. Dhinesh Babu, L.D., Krishna, P.V.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)

    Article  Google Scholar 

  172. Soni, A., Vishwakarma, G., Jain, Y.K.: A bee colony based multi-objective load balancing technique for cloud computing environment. Int. J. Comput. Appl. 114(4), 19–25 (2015)

    Google Scholar 

  173. Priyadarsini, R.J., Arockiam, L.: PBCOPSO: A parallel optimization algorithm for task scheduling in cloud environment. Indian J. Sci. Technol. 8(16), 1–5 (2015)

    Google Scholar 

  174. Kashani, M.H., Jamei, M., Akbari, M. and Tayebi, R.M., 2011, July. Utilizing bee colony to solve task scheduling problem in distributed systems. In 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks (pp. 298–303). IEEE.

  175. Navimipour, N.J., 2015, June. Task scheduling in the cloud environments based on an artificial bee colony algorithm. In International Conference on Image Processing (pp. 38–44).

  176. Hesabian, N., Haj, H., Javadi, S.: Optimal scheduling in cloud computing environment using the bee algorithm. Int J Comput Netw Commun Secur 3, 253–258 (2015)

    Google Scholar 

  177. Udomkasemsub, O., Xiaorong, L. and Achalakul, T., 2012, May. A multiple-objective workflow scheduling framework for cloud data analytics. In 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE) (pp. 391–398). IEEE.

  178. Liang, Y.C., Chen, A.H.L. and Nien, Y.H., 2014, July. Artificial bee colony for workflow scheduling. In 2014 IEEE Congress on Evolutionary Computation (CEC) (pp. 558–564). IEEE.

  179. Kansal, N.J., Chana, I.: Artificial bee colony based energy-aware resource utilization technique for cloud computing. Concurr. Comput.: Practice Exp. 27(5), 1207–1225 (2015)

    Article  Google Scholar 

  180. Hasançebi, O., Teke, T., Pekcan, O.: A bat-inspired algorithm for structural optimization. Comput. Struct. 128, 77–90 (2013)

    Article  Google Scholar 

  181. Jacob, L.: Bat algorithm for resource scheduling in cloud computing. Population 5(18), 23 (2014)

    Google Scholar 

  182. Kumar, V.S., Aramudhan, M.: Trust based resource selection in cloud computing using hybrid algorithm. Int. J. Intell. Syst. Appl. 7(8), 59 (2015)

    Google Scholar 

  183. Kumar, V.S.: Hybrid optimized list scheduling and trust based resource selection in cloud computing. J. Theor. Appl. Inf. Technol. 69(3), 434–442 (2014)

    Google Scholar 

  184. Raghavan, S., Sarwesh, P., Marimuthu, C. and Chandrasekaran, K., 2015, January. Bat algorithm for scheduling workflow applications in cloud. In 2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV) (pp. 139–144). IEEE.

  185. George, S.: Hybrid PSO-MOBA for profit maximization in cloud computing. Int J Adv Comput Sci Appl 6(2), 159–163 (2015)

    Google Scholar 

  186. Chu, S.C., Tsai, P.W. and Pan, J.S., 2006. Cat swarm optimization. In PRICAI 2006: Trends in Artificial Intelligence: 9th Pacific Rim International Conference on Artificial Intelligence Guilin, China, August 7–11, 2006 Proceedings 9 (pp. 854–858). Springer Berlin Heidelberg.

  187. Chu, S.C., Tsai, P.W.: Computational intelligence based on the behavior of cats. Int. J. Innov. Comput. Inf. Control 3(1), 163–173 (2007)

    Google Scholar 

  188. Tsai, P.W., Pan, J.S., Chen, S.M., Liao, B.Y. and Hao, S.P., 2008, July. Parallel cat swarm optimization. In 2008 international conference on machine learning and cybernetics (Vol. 6, pp. 3328–3333). IEEE.

  189. Pradhan, P.M., Panda, G.: Solving multiobjective problems using cat swarm optimization. Expert Syst. Appl. 39(3), 2956–2964 (2012)

    Article  Google Scholar 

  190. Sharafi, Y., Khanesar, M.A. and Teshnehlab, M., 2013, September. Discrete binary cat swarm optimization algorithm. In 2013 3rd IEEE international conference on computer, control and communication (IC4) (pp. 1–6). IEEE.

  191. Bilgaiyan, S., Sagnika, S. and Das, M., 2014, February. Workflow scheduling in cloud computing environment using cat swarm optimization. In 2014 IEEE International Advance Computing Conference (IACC) (pp. 680–685). IEEE.

  192. Rouhi, S. and Nejad, E.B., 2015. CSO-GA: a new scheduling technique for cloud computing systems based on cat swarm optimization and genetic algorithm. Fen Bilimleri Dergisi (CFD), 36(4).

  193. Hof, P.R., Van der Gucht, E.: Structure of the cerebral cortex of the humpback whale, Megaptera novaeangliae (Cetacea, Mysticeti, Balaenopteridae). Anat. Rec.: Adv. Integr. Anat. Evolut. Biol.: Adv. Integr. Anat. Evolut. Biol. 290(1), 1–31 (2007)

    Article  Google Scholar 

  194. Mangalampalli, S., Karri, G.R., Kose, U.: Multi Objective Trust aware task scheduling algorithm in cloud computing using whale optimization. J. King Saud Univ.-Comput. Inf. Sci. 35(2), 791–809 (2023)

    Google Scholar 

  195. Mangalampalli, S., Swain, S.K., Mangalampalli, V.K.: Prioritized energy efficient task scheduling algorithm in cloud computing using whale optimization algorithm. Wireless Pers. Commun. 126(3), 2231–2247 (2022)

    Article  Google Scholar 

  196. Sreenu, K., Sreelatha, M.: W-Scheduler: whale optimization for task scheduling in cloud computing. Clust. Comput. 22, 1087–1098 (2019)

    Article  Google Scholar 

  197. Chen, X., Cheng, L., Liu, C., Liu, Q., Liu, J., Mao, Y., Murphy, J.: A WOA-based optimization approach for task scheduling in cloud computing systems. IEEE Syst. J. 14(3), 3117–3128 (2020)

    Article  Google Scholar 

  198. Jia, L., Li, K., Shi, X.: Cloud computing task scheduling model based on improved whale optimization algorithm. Wirel. Commun. Mob. Comput. 2021, 1–13 (2021)

    Google Scholar 

  199. Masadeh, R., Sharieh, A., Mahafzah, B.: Humpback whale optimization algorithm based on vocal behavior for task scheduling in cloud computing. Int. J. Adv. Sci. Technol. 13(3), 121–140 (2019)

    Google Scholar 

  200. Arora, S., Singh, S.: The firefly optimization algorithm: convergence analysis and parameter selection. Int. J. Comp. Appl. (2013). https://doi.org/10.5120/11826-7528

    Article  Google Scholar 

  201. Mangalampalli, S., Karri, G.R., Elngar, A.A.: An efficient trust-aware task scheduling algorithm in cloud computing using firefly optimization. Sensors 23(3), 1384 (2023)

    Article  Google Scholar 

  202. Ebadifard, F., Doostali, S. and Babamir, S.M., 2018, December. A firefly-based task scheduling algorithm for the cloud computing environment: Formal verification and simulation analyses. In 2018 9th International Symposium on Telecommunications (IST) (pp. 664–669). IEEE.

  203. Malleswaran, S.K.A., Kasireddi, B.: An efficient task scheduling method in a cloud computing environment using firefly crow search algorithm (FF-CSA). Int. J. Sci. Technol. Res. 8(12), 623–627 (2019)

    Google Scholar 

  204. Rajagopalan, A., Modale, D.R. and Senthilkumar, R., 2020. Optimal scheduling of tasks in cloud computing using hybrid firefly-genetic algorithm. In Advances in Decision Sciences, Image Processing, Security and Computer Vision: International Conference on Emerging Trends in Engineering (ICETE), Vol. 2 (pp. 678–687). Springer International Publishing.

  205. Kashikolaei, S.M.G., Hosseinabadi, A.A.R., Saemi, B., Shareh, M.B., Sangaiah, A.K., Bian, G.B.: An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm. J. Supercomput. 76, 6302–6329 (2020)

    Article  Google Scholar 

  206. Fanian, F., Bardsiri, V.K., Shokouhifar, M.: A new task scheduling algorithm using firefly and simulated annealing algorithms in cloud computing. Int. J. Adv. Comput. Sci. Appl. (2018). https://doi.org/10.14569/IJACSA.2018.090228

    Article  Google Scholar 

  207. Du, Y., Wang, J.L., Lei, L.: Multi-objective scheduling of cloud manufacturing resources through the integration of cat swarm optimization and firefly algorithm. Adv. Prod. Eng. Manag. (2019). https://doi.org/10.14743/apem2019.3.331

    Article  Google Scholar 

  208. Ammari, A.C., Labidi, W., Mnif, F., Yuan, H., Zhou, M., Sarrab, M.: Firefly algorithm and learning-based geographical task scheduling for operational cost minimization in distributed green data centers. Neurocomputing 490, 146–162 (2022)

    Article  Google Scholar 

  209. Zolghadr-Asli, B., Bozorg-Haddad, O., Chu, X.: Crow search algorithm (CSA). Adv. Optim. Nat. -inspired Algorithms (2018). https://doi.org/10.1007/978-981-10-5221-7_14

    Article  Google Scholar 

  210. Prasanna Kumar, K.R., Kousalya, K.: Amelioration of task scheduling in cloud computing using crow search algorithm. Neural Comput. Appl. 32, 5901–5907 (2020)

    Article  Google Scholar 

  211. Kumar, K.P., Kousalya, K., Vishnuppriya, S., Ponni, S. and Logeswaran, K., 2021, February. Enhanced Crow Search Algorithm for Task Scheduling in Cloud Computing. In IOP Conference Series: Materials Science and Engineering (Vol. 1055, No. 1, p. 012102). IOP Publishing.

  212. Singh, H., Tyagi, S., Kumar, P.: Crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing. Int. J. Commun. Syst. 33(14), e4467 (2020)

    Article  Google Scholar 

  213. Singh, H., Tyagi, S., Kumar, P.: Crow search based scheduling algorithm for load balancing in cloud environment. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(9), 1058–1064 (2019)

    Article  Google Scholar 

  214. Singh, H., Tyagi, S., Kumar, P.: Cloud resource mapping through crow search inspired metaheuristic load balancing technique. Comput. Electr. Eng. 93, 107221 (2021)

    Article  Google Scholar 

  215. Wang, J.: Grey wolf optimization and crow search algorithm for resource allocation scheme in cloud computing: grey wolf optimization and crow search algorithm in cloud computing. Multime’d. Res. (2021). https://doi.org/10.46253/j.mr.v4i3.a3

    Article  Google Scholar 

  216. Kak, S.M., Agarwal, P., Alam, M.A., Siddiqui, F.: A hybridized approach for minimizing energy in cloud computing. Clus. Comput. (2022). https://doi.org/10.1007/s10586-022-03807-9

    Article  Google Scholar 

  217. Mangalampalli, S., Mangalampalli, V.K. and Swain, S.K., A Task scheduling approach in cloud computing to minimize the power cost in datacenters using crow search.

  218. Joshi, A.S., Kulkarni, O., Kakandikar, G.M., Nandedkar, V.M.: Cuckoo search optimization-a review. Mater. Today: Proc. 4(8), 7262–7269 (2017)

    Article  Google Scholar 

  219. Elnahary, M.K., Hamed, A.Y., El-Sayed, H.: Task scheduling optimization in cloud computing by cuckoo search algorithm. Sohag J. Sci. 7(3), 29–37 (2022)

    Google Scholar 

  220. Navimipour, N.J., Milani, F.S.: Task scheduling in the cloud computing based on the cuckoo search algorithm. Int. J. Model. Optim. 5(1), 44 (2015)

    Article  Google Scholar 

  221. Prem Jacob, T., Pradeep, K.: A multi-objective optimal task scheduling in cloud environment using cuckoo particle swarm optimization. Wireless Pers. Commun. 109, 315–331 (2019)

    Article  Google Scholar 

  222. Krishnadoss, P., Pradeep, N., Ali, J., Nanjappan, M., Krishnamoorthy, P., Kedalu Poornachary, V.: CCSA: Hybrid cuckoo crow search algorithm for task scheduling in cloud computing. Int. J. Intell. Eng. Syst. 14(4), 241–250 (2021)

    Google Scholar 

  223. Agarwal, M. and Srivastava, G.M.S., 2018. A cuckoo search algorithm-based task scheduling in cloud computing. In Advances in Computer and Computational Sciences: Proceedings of ICCCCS 2016, Volume 2 (pp. 293–299). Springer Singapore.

  224. Nazir, S., Shafiq, S., Iqbal, Z., Zeeshan, M., Tariq, S. and Javaid, N., 2019. Cuckoo optimization algorithm based job scheduling using cloud and fog computing in smart grid. In Advances in Intelligent Networking and Collaborative Systems: The 10th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2018) (pp. 34–46). Springer International Publishing.

  225. Gawali, M.B., Shinde, S.K.: Standard deviation based modified cuckoo optimization algorithm for task scheduling to efficient resource allocation in cloud computing. J. Adv. Inf. Technol. 8, 4 (2017)

    Google Scholar 

  226. Madni, S.H.H., Latiff, M.S.A., Ali, J., Abdulhamid, S.I.M.: Multi-objective-oriented cuckoo search optimization-based resource scheduling algorithm for clouds. Arab. J. Sci. Eng. 44, 3585–3602 (2019)

    Article  Google Scholar 

  227. Krishnadoss, P., Jacob, P.: OCSA: task scheduling algorithm in cloud computing environment. Int. J. Intell. Eng. Syst. 11(3), 271–279 (2018)

    Google Scholar 

  228. Madni, S.H.H., Abd Latiff, M.S., Abdulhamid, S.I.M., Ali, J.: Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment. Clust. Comput. 22, 301–334 (2019)

    Article  Google Scholar 

  229. Pradeep, K., Prem Jacob, T.: A hybrid approach for task scheduling using the cuckoo and harmony search in cloud computing environment. Wireless Pers. Commun. 101, 2287–2311 (2018)

    Article  Google Scholar 

  230. Shahdi-Pashaki, S., Teymourian, E., Kayvanfar, V., Komaki, G.M., Sajadi, A.: Group technology-based model and cuckoo optimization algorithm for resource allocation in cloud computing. IFAC-PapersOnLine 48(3), 1140–1145 (2015)

    Article  Google Scholar 

  231. Durgadevi, P., Srinivasan, S.: Resource allocation in cloud computing using SFLA and cuckoo search hybridization. Int. J. Parallel Prog. 48, 549–565 (2020)

    Article  Google Scholar 

  232. Faris, H., Aljarah, I., Al-Betar, M.A., Mirjalili, S.: Grey wolf optimizer: a review of recent variants and applications. Neural Comput. Appl. 30, 413–435 (2018)

    Article  Google Scholar 

  233. Natesan, G., Chokkalingam, A.: An improved grey wolf optimization algorithm based task scheduling in cloud computing environment. Int. Arab J. Inf. Technol. 17(1), 73–81 (2020)

    Google Scholar 

  234. Sreenu, K., Malempati, S.: MFGMTS: Epsilon constraint-based modified fractional grey wolf optimizer for multi-objective task scheduling in cloud computing. IETE J. Res. 65(2), 201–215 (2019)

    Article  Google Scholar 

  235. Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M. and Zivkovic, M., 2019, November. Task scheduling in cloud computing environment by grey wolf optimizer. In 2019 27th telecommunications forum (TELFOR) (pp. 1–4). IEEE.

  236. Gobalakrishnan, N., Arun, C.: A new multi-objective optimal programming model for task scheduling using genetic gray wolf optimization in cloud computing. Comput. J. 61(10), 1523–1536 (2018)

    Article  Google Scholar 

  237. Natesan, G., Chokkalingam, A.: Task scheduling in heterogeneous cloud environment using mean grey wolf optimization algorithm. ICT Express 5(2), 110–114 (2019)

    Article  Google Scholar 

  238. Natesha, B.V., Sharma, N.K., Domanal, S. and Guddeti, R.M.R., 2018, September. GWOTS: grey wolf optimization based task scheduling at the green cloud data center. In 2018 14th International Conference on Semantics, Knowledge and Grids (SKG) (pp. 181–187). IEEE.

  239. Mohammadzadeh, A., Masdari, M., Gharehchopogh, F.S., Jafarian, A.: Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing. Evol. Intel. 14, 1997–2025 (2021)

    Article  Google Scholar 

  240. Arora, N., Banyal, R.K.: A particle grey wolf hybrid algorithm for workflow scheduling in cloud computing. Wireless Pers. Commun. 122(4), 3313–3345 (2022)

    Article  Google Scholar 

  241. Balasubramanian, K., Ramya, K., Devi, K.G.: Improved swarm optimization of deep features for glaucoma classification using SEGSO and VGGNet. Biomed. Signal Process. Control 77, 103845 (2022)

    Article  Google Scholar 

  242. Zhou, J., Dong, S.: Hybrid glowworm swarm optimization for task scheduling in the cloud environment. Eng. Optim. 50(6), 949–964 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  243. Alboaneen, D.A., Tianfield, H. and Zhang, Y., 2017, March. Glowworm swarm optimisation based task scheduling for cloud computing. In Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing (pp. 1–7).

  244. Abdullahi, M., Ngadi, M.A.: Symbiotic organism search optimization based task scheduling in cloud computing environment. Futur. Gener. Comput. Syst. 56, 640–650 (2016)

    Article  Google Scholar 

  245. Sa’ad, S., Muhammed, A., Abdullahi, M., Abdullah, A., Hakim Ayob, F.: An enhanced discrete symbiotic organism search algorithm for optimal task scheduling in the cloud. Algorithms 14(7), 200 (2021)

    Article  Google Scholar 

  246. Abdullahi, M., Ngadi, M.A., Dishing, S.I., Ahmad, B.I.E.: An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment. J. Netw. Comput. Appl. 133, 60–74 (2019)

    Article  Google Scholar 

  247. Abdullahi, M., Ngadi, M.A., Dishing, S.I., Abdulhamid, S.I.M.: An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. J. Ambient Intell. Humanized Comput. (2022). https://doi.org/10.1007/s12652-021-03632-9

    Article  Google Scholar 

  248. Sharma, M. and Verma, A., 2017, February. Energy-aware discrete symbiotic organism search optimization algorithm for task scheduling in a cloud environment. In 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 513–518). IEEE.

  249. Zubair, A.A., Razak, S.A., Ngadi, M.A., Al-Dhaqm, A., Yafooz, W.M., Emara, A.H.M., Saad, A., Al-Aqrabi, H.: A cloud computing-based modified symbiotic organisms search algorithm (AI) for optimal task scheduling. Sensors 22(4), 1674 (2022)

    Article  Google Scholar 

  250. Siddique, N., Adeli, H.: Physics-based search and optimization: Inspirations from nature. Expert. Syst. 33(6), 607–623 (2016)

    Article  Google Scholar 

  251. Hashim, F.A., Houssein, E.H., Mabrouk, M.S., Al-Atabany, W., Mirjalili, S.: Henry gas solubility optimization: a novel physics-based algorithm. Futur. Gener. Comput. Syst. 101, 646–667 (2019)

    Article  Google Scholar 

  252. Aarts, E., Korst, J., Michiels, W.: Simulated annealing. Search Methodol.: Introd. Tutor. Optim. Decis. Support Techn. (2005). https://doi.org/10.1007/0-387-28356-0_7

    Article  MATH  Google Scholar 

  253. Rashedi, E., Rashedi, E., Nezamabadi-Pour, H.: A comprehensive survey on gravitational search algorithm. Swarm Evol. Comput. 41, 141–158 (2018)

    Article  MATH  Google Scholar 

  254. Erol, O.K., Eksin, I.: A new optimization method: big bang–big crunch. Adv. Eng. Softw. 37(2), 106–111 (2006)

    Article  Google Scholar 

  255. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    Article  MATH  Google Scholar 

  256. Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mech. 213(3–4), 267–289 (2010)

    Article  MATH  Google Scholar 

  257. Formato, R.A.: Central force optimization: a new deterministic gradient-like optimization metaheuristic. Opsearch 46(1), 25–51 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  258. Alatas, B.: ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38(10), 13170–13180 (2011)

    Article  Google Scholar 

  259. Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184 (2013)

    Article  MathSciNet  Google Scholar 

  260. Kaveh, A., Khayatazad, M.: A new meta-heuristic method: ray optimization. Comput. Struct. 112, 283–294 (2012)

    Article  Google Scholar 

  261. Abd Elaziz, M., Attiya, I.: An improved Henry gas solubility optimization algorithm for task scheduling in cloud computing. Artif. Intell. Rev. 54, 3599–3637 (2021)

    Article  Google Scholar 

  262. Wen, X., Huang, M. and Shi, J., 2012, October. Study on resources scheduling based on ACO allgorithm and PSO algorithm in cloud computing. In 2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science (pp. 219–222). IEEE.

  263. Mathiyalagan, P., Sivanandam, S.N., Saranya, K.S.: Hybridization of modified ant colony optimization and intelligent water drops algorithm for job scheduling in computational grid. ICTACT J. Soft Comput. 4(1), 651–655 (2013)

    Article  Google Scholar 

  264. Cho, K.M., Tsai, P.W., Tsai, C.W., Yang, C.S.: A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput. Appl. 26, 1297–1309 (2015)

    Article  Google Scholar 

  265. Madivi, R. and Kamath, S.S., 2014, July. An hybrid bio-inspired task scheduling algorithm in cloud environment. In Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1–7). IEEE.

  266. Singhal, U., Jain, S.: A new fuzzy logic and GSO based load balancing mechanism for public cloud. Int. J. Grid Distrib. Comput. 7(5), 97–110 (2014)

    Article  Google Scholar 

  267. Mandal, T. and Acharyya, S., 2015, December. Optimal task scheduling in cloud computing environment: meta heuristic approaches. In 2015 2nd International Conference on Electrical Information and Communication Technologies (EICT) (pp. 24–28). IEEE.

  268. 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 International Conference, ICSOC 2013, Berlin, Germany, December 2-5, 2013, Proceedings 11 (pp. 237-251). Springer Berlin Heidelberg.

  269. Ramezani, F., Lu, J., Taheri, J., Hussain, F.K.: Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments. World Wide Web 18, 1737–1757 (2015)

    Article  Google Scholar 

  270. Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. Ieee Access 3, 2687–2699 (2015)

    Article  Google Scholar 

  271. He, H., Xu, G., Pang, S., Zhao, Z.: AMTS: Adaptive multi-objective task scheduling strategy in cloud computing. China Commun. 13(4), 162–171 (2016)

    Article  Google Scholar 

  272. Raju, R., Babukarthik, R.G., Chandramohan, D., Dhavachelvan, P. and Vengattaraman, T., 2013, February. Minimizing the makespan using Hybrid algorithm for cloud computing. In 2013 3rd IEEE International Advance Computing Conference (IACC) (pp. 957–962). IEEE.

  273. Khalili, A. and Babamir, S.M., 2015, May. Makespan improvement of PSO-based dynamic scheduling in cloud environment. In 2015 23rd Iranian Conference on Electrical Engineering (pp. 613–618). IEEE.

  274. Gabi, D., Ismail, A.S. and Dankolo, N.M., 2019, June. Minimized makespan based improved cat swarm optimization for efficient task scheduling in cloud datacenter. In Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference (pp. 16–20).

  275. Frincu, M.E. and Craciun, C., 2011, December. 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.

  276. Cui, H., Li, Y., Liu, X., Ansari, N., Liu, Y.: Cloud service reliability modelling and optimal task scheduling. IET Commun. 11(2), 161–167 (2017)

    Article  Google Scholar 

  277. Tao, F., Feng, Y., Zhang, L., Liao, T.W.: CLPS-GA: a case library and pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl. Soft Comput. 19, 264–279 (2014)

    Article  Google Scholar 

  278. Goyal, A. and Chahal, N.S., 2015, November. Bio inspired approach for load balancing to reduce energy consumption in cloud data center. In 2015 Communication, Control and Intelligent Systems (CCIS) (pp. 406–410). IEEE.

  279. Meshkati, J., Safi-Esfahani, F.: Energy-aware resource utilization based on particle swarm optimization and artificial bee colony algorithms in cloud computing. J. Supercomput. 75(5), 2455–2496 (2019)

    Article  Google Scholar 

  280. Meena, J., Kumar, M., Vardhan, M.: Cost effective genetic algorithm for workflow scheduling in cloud under deadline constraint. IEEE Access 4, 5065–5082 (2016)

    Article  Google Scholar 

  281. Nasr, A.A., El-Bahnasawy, N.A., Attiya, G., El-Sayed, A.: Cost-effective algorithm for workflow scheduling in cloud computing under deadline constraint. Arab. J. Sci. Eng. 44, 3765–3780 (2019)

    Article  Google Scholar 

  282. Wu, Z., Liu, X., Ni, Z., Yuan, D., Yang, Y.: A market-oriented hierarchical scheduling strategy in cloud workflow systems. J. Supercomput. 63, 256–293 (2013)

    Article  Google Scholar 

  283. Gabi, D., Zainal, A., Ismail, A.S. and Zakaria, Z., 2017, May. Scalability-Aware scheduling optimization algorithm for multi-objective cloud task scheduling problem. In 2017 6th ICT International Student Project Conference (ICT-ISPC) (pp. 1–6). IEEE.

  284. Yassa, S., Chelouah, R., Kadima, H., Granado, B.: Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. Sci. World J. (2013). https://doi.org/10.1155/2013/3509345

    Article  MATH  Google Scholar 

  285. Li, Z., Ge, J., Yang, H., Huang, L., Hu, H., Hu, H., Luo, B.: A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds. Futur. Gener. Comput. Syst. 65, 140–152 (2016)

    Article  Google Scholar 

  286. Wen, Y., Liu, J., Dou, W., Xu, X., Cao, B., Chen, J.: Scheduling workflows with privacy protection constraints for big data applications on cloud. Futur. Gener. Comput. Syst. 108, 1084–1091 (2020)

    Article  Google Scholar 

  287. Sharma, M., Garg, R.: HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers. Eng. Sci. Technol. Int. J. 23(1), 211–224 (2020)

    Google Scholar 

  288. Thanka, M.R., Uma Maheswari, P., Edwin, E.B.: An improved efficient: artificial bee colony algorithm for security and QoS aware scheduling in cloud computing environment. Clust. Comput. 22, 10905–10913 (2019)

    Article  Google Scholar 

  289. Maurya, A.K. and Tripathi, A.K., 2018, March. Deadline-constrained algorithms for scheduling of bag-of-tasks and workflows in cloud computing environments. In Proceedings of the 2nd International Conference on High Performance Compilation, Computing and Communications (pp. 6–10).

  290. Wu, Q., Yun, D., Lin, X., Gu, Y., Lin, W. and Liu, Y., 2013. On workflow scheduling for end-to-end performance optimization in distributed network environments. In Job Scheduling Strategies for Parallel Processing: 16th International Workshop, JSSPP 2012, Shanghai, China, May 25, 2012. Revised Selected Papers 16 (pp. 76-95). Springer Berlin Heidelberg.

  291. Jianfang, C., Junjie, C., Qingshan, Z.: An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm. Cybern. Inf. Technol. 14(1), 25–39 (2014)

    MathSciNet  Google Scholar 

  292. Sakellariou, R., Zhao, H.: A low-cost rescheduling policy for efficient mapping of workflows on grid systems. Sci. Program. 12(4), 253–262 (2004)

    Google Scholar 

  293. Liu, K., 2009. Scheduling algorithms for instance-intensive cloud workflows. Swinburne University of Technology, Faculty of Engineering and Industrial Sciences, Centre for Complex Software Systems and Services.

  294. Wang, X., Wang, Y., Zhu, H.: Energy-efficient multi-job scheduling model for cloud computing and its genetic algorithm. Math. Probl. Eng. (2012). https://doi.org/10.1155/2012/589243

    Article  MathSciNet  MATH  Google Scholar 

  295. Negru, C., Pop, F., Cristea, V., Bessisy, N. and Li, J., 2013, September. Energy efficient cloud storage service: key issues and challenges. In 2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies (pp. 763–766). IEEE.

  296. Shu, W., Wang, W., Wang, Y.: A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing. EURASIP J. Wirel. Commun. Netw. 2014(1), 1–9 (2014)

    Article  Google Scholar 

  297. Sellami, K., Ahmed-Nacer, M., Tiako, P.F., Chelouah, R.: Immune genetic algorithm for scheduling service workflows with QoS constraints in cloud computing. S. Afr. J. Ind. Eng. 24(3), 68–82 (2013)

    Google Scholar 

  298. Zhao, C., Zhang, S., Liu, Q., Xie, J. and Hu, J., 2009, September. Independent tasks scheduling based on genetic algorithm in cloud computing. In 2009 5th international conference on wireless communications, networking and mobile computing (pp. 1–4). IEEE.

  299. Almezeini, N., Hafez, A.: Task scheduling in cloud computing using lion optimization algorithm. Int. J. Adv. Comput. Sci. Appl. (2017). https://doi.org/10.14569/IJACSA.2017.081110

    Article  Google Scholar 

  300. Li, K., Xu, G., Zhao, G., Dong, Y. and Wang, D., 2011, August. Cloud task scheduling based on load balancing ant colony optimization. In 2011 sixth annual ChinaGrid conference (pp. 3–9). IEEE.

  301. Hu, Y., Xing, L., Zhang, W., Xiao, W. and Tang, D., 2010. A knowledge-based ant colony optimization for a grid workflow scheduling problem. In Advances in Swarm Intelligence: First International Conference, ICSI 2010, Beijing, China, June 12-15, 2010, Proceedings, Part I 1 (pp. 241-248). Springer Berlin Heidelberg.

  302. Liu, W., Peng, S., Du, W., Wang, W., Zeng, G.S.: Security-aware intermediate data placement strategy in scientific cloud workflows. Knowl. Inf. Syst. 41, 423–447 (2014)

    Article  Google Scholar 

  303. Javanmardi, S., Shojafar, M., Amendola, D., Cordeschi, N., Liu, H. and Abraham, A., 2014. Hybrid job scheduling algorithm for cloud computing environment. In Proceedings of the fifth international conference on innovations in bio-inspired computing and applications IBICA 2014 (pp. 43–52). Springer International Publishing

  304. Rodriguez, M.A., Buyya, R.: Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)

    Article  Google Scholar 

  305. Verma, A. and Kaushal, S., 2014, March. Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud. In 2014 Recent Advances in Engineering and Computational Sciences (RAECS) (pp. 1–6). IEEE.

  306. Milan, S.T., Rajabion, L., Darwesh, A., Hosseinzadeh, M., Navimipour, N.J.: Priority-based task scheduling method over cloudlet using a swarm intelligence algorithm. Clust. Comput. 23, 663–671 (2020)

    Article  Google Scholar 

  307. Wang, X., Cao, B., Hou, C., Xiong, L. and Fan, J., 2015, October. Scheduling budget constrained cloud workflows with particle swarm optimization. In 2015 IEEE Conference on Collaboration and Internet Computing (CIC) (pp. 219–226). IEEE.

  308. Guo, P. and Xue, Z., 2017, October. Cost-effective fault-tolerant scheduling algorithm for real-time tasks in cloud systems. In 2017 IEEE 17th International Conference on Communication Technology (ICCT) (pp. 1942–1946). IEEE.

  309. Islam, M.R. and Habiba, M., 2012, December. Dynamic scheduling approach for data-intensive cloud environment. In 2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM) (pp. 179–185). IEEE.

  310. Kumar, N. and Patel, P., 2016, March. Resource management using feed forward ANN-PSO in cloud computing environment. In Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies (pp. 1–6).

  311. Hu, H. and Wang, H., 2016, October. A prediction-based aco algorithm to dynamic tasks scheduling in cloud environment. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC) (pp. 2727–2732). IEEE.

  312. Rahman, M., Hassan, R., Ranjan, R., Buyya, R.: Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurr. Comput.: Pract. Exp. 25(13), 1816–1842 (2013)

    Article  Google Scholar 

  313. Alla, H.B., Alla, S.B. and Ezzati, A., 2016, May. A novel architecture for task scheduling based on dynamic queues and particle swarm optimization in cloud computing. In 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech) (pp. 108–114). IEEE.

  314. Askarizade Haghighi, M., Maeen, M., Haghparast, M.: An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms: energy efficient dynamic cloud resource management. Wireless Pers. Commun. 104, 1367–1391 (2019)

    Article  Google Scholar 

  315. Negi, S., Panwar, N., Vaisla, K.S. and Rauthan, M.M.S., 2020. Artificial neural network based load balancing in cloud environment. In Advances in Data and Information Sciences: Proceedings of ICDIS 2019 (pp. 203–215). Springer Singapore.

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  1. Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh

    Farida Siddiqi Prity & K. M. Aslam Uddin

  2. High Court Division, Supreme Court of Bangladesh, Dhaka, 1000, Bangladesh

    Md. Hasan Gazi

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FSP: writing original draft, literature surveys, writing—review and editing; MdHG: draft manuscript preparation, writing—review, and editing; KMAU: study conception, supervision, and investigation on challenges.

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Prity, F.S., Gazi, M.H. & Uddin, K.M.A. A review of task scheduling in cloud computing based on nature-inspired optimization algorithm. Cluster Comput 26, 3037–3067 (2023). https://doi.org/10.1007/s10586-023-04090-y

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