Development of a Hybrid Algorithm for efficient Task Scheduling in Cloud Computing environment using Artificial Intelligence

  • Mohammed Yousuf Uddin
  • Hikmat Awad Abdeljaber
  • Tariq Ahamed Ahanger


Cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. Cloud environment involves a set of virtual machines, which share the same computation facility and storage. Due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. Task scheduling denotes a set of policies to regulate the task processed by a system. Virtual machine scheduling is essential for effective operations in distributed environment. The aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the BAT Algorithm and the Ant Colony Optimization (ACO) algorithm in order to optimize the virtual machine scheduling process. The performance evaluation of the three algorithms (BAT, ACO and Hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms.

Author Biographies

Mohammed Yousuf Uddin
Lecturer, Department of Information Systems. College of Computer Engineering and Science.  Prince Sattam bin Abdulaziz University
Hikmat Awad Abdeljaber
Princee Sattam Bin Abdulaziz University
Tariq Ahamed Ahanger
Department of Computer SciencePrince Sattam bin Abdulaziz University, KSAAl Kharj, Kingdom of Saudi


[1] Mohit Agarwal and Gur Mauj Saran Srivastava. A cuckoo search algorithm-based task scheduling in cloud computing. In Advances in computer and computational sciences, pages 293-299. Springer, 2018.

[2] Hussein Al-Zoubi. Efficient task scheduling for applications on clouds. In 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), pages 10-13. IEEE, 2019.

[3] Rasha A Al-Arasi and Anwar Saif. Htscc a hybrid task scheduling algorithm in cloud computing environment. International Journal, 17(02), 2018.

[4] Ali Al-maamari and Fatma A Omara. Task scheduling using hybrid algorithm in cloud computing environments. Journal of Computer Engineering (IOSR-JCE), 17(3):96-106, 2015.

[5] Kamolov Nizomiddin Baxodirjonovich and Tae-Young Choe. Dynamic task scheduling algorithm based on ant colony scheme. International Journal of Engineering and Technology (IJET), 7(4):1163-1172, 2015.

[6] Timea Bezdan, Miodrag Zivkovic, Eva Tuba, Ivana Strumberger, Nebojsa Bacanin, and Milan Tuba. Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. In International Conference on Intelligent and Fuzzy Systems, pages 718-725. Springer, 2020.

[7] Bharot, Nitesh and Shukla, Shalini. A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm. International Conference on Computing and Information Technology (ICCIT-1441)IEEE, pages 1-4, 2020.

[8] Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, and Ivona Brandic. Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6):599-616, 2009.

[9] Sajjad Asadzadeh Chalack, Seyed Naser Razavi, and Ali Harounabadi. Job scheduling on the grid environment using max-min firefly algorithm. International Journal of Computer Applications Technology and ResearchVolume, pages 63-67, 2014.

[10] Prachi Chaturvedi, Abhishek Satyarthi, and Sanjiv Sharma. Time and reliability optimization bat algorithm for scheduling workflow in cloud. International Research Journal of Engineering and Technology, 4(6), 2017.

[11] Ling Ding, Ping Fan, and Bin Wen. A task scheduling algorithm for heterogeneous systems using aco. In 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA), pages 749-751. IEEE, 2013.

[12] Ashraf B El-Sisi, Medhat A Tawfeek, et al. Cloud task scheduling for load balancing based on intelligent strategy. International Journal of Intelligent Systems & Applications, 6(5), 2014.

[13] Salu George. Hybrid pso-moba for profit maximization in cloud computing. Int J Adv Comput Sci Appl, 6(2):159-163, 2015.

[14] Gu, Yi and Budati, Chandu. Energy-aware workflow scheduling and optimization in clouds using bat algorithm. In Future Generation Computer Systems, volume 113, page 106-112. Elsevier, 2020

[15] Qiang Guo. Task scheduling based on ant colony optimization in cloud environment. In AIP conference proceedings, volume 1834, page 040039. AIP Publishing LLC, 2017.

[16] Hariharan, Bhagavathi and Raj, Dassan Paul. A hybrid framework for job scheduling on cloud using firefly and BAT algorithm. In International Journal of Business Intelligence and Data Mining Inderscience Publishers (IEL), volume 15, page 388-407. 2019.

[17] Ning Hu, Zhihong Tian, Xiaojiang Du, and Mohsen Guizani. An energy-efficient in-network computing paradigm for 6g. IEEE Transactions on Green Communications and Networking, 2021.

[18] Liji Jacob. Bat algorithm for resource scheduling in cloud computing. population, 5(18):23, 2014.

[19] Mala Kalra and Sarbjeet Singh. A review of metaheuristic scheduling techniques in cloud computing. Egyptian informatics journal, 16(3):275-295, 2015.

[20] Navneet Kaur and Sarbjeet Singh. A budget-constrained time and reliability optimization bat algorithm for scheduling workflow applications in clouds. Procedia Computer Science, 98:199-204, 2016.

[21] V Suresh Kumar et al. Hybrid optimized list scheduling and trust based resource selection in cloud computing. Journal of Theoretical & Applied Information Technology, 69(3), 2014.

[22] D Maruthanayagam and Arun Prakasam. Job scheduling in cloud computing using ant colony optimization. Int. J. Adv. Res. Comput. Eng. Technol.(IJARCET), 3(2):540-547, 2014.

[23] YoungJu Moon, HeonChang Yu, Joon-Min Gil, and JongBeom Lim. A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments. Humancentric Computing and Information Sciences, 7(1):1-10, 2017.

[24] PM Mell and T Grance. Nist definition of cloud computing. national institute of standards and technology. nvlpubs. nist. gov/nistpubs. Legacy/SP/nistspecialpublication800-145. pdf, 2011.

[25] Shubham Mittal and Avita Katal. An optimized task scheduling algorithm in cloud computing. In 2016 IEEE 6th international conference on advanced computing (IACC), pages 197-202. IEEE, 2016.

[26] Kumar Mukesh and Singh H. To enhance energy aware cloud scheduling by using metaheuristic technique. International Journal of Innovative Research in Computer and Communication Engineering, 4:19762-19767, 2016.

[27] M.Jaeyalakshmi P.Kumar. Task scheduling using meta heuristic optimization techniques in cloud environment. International Journal of Pure and Applied Mathematics, 118:1893-1912, 2018.

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

[29] T Sunitha Rani and Dr Shyamala Kannan. Task scheduling on virtual machines using bat strategy for efficient utilization of resources in cloud environment. vol, 12:6663-6669, 2017.

[30] Prince Kwame Senyo, Erasmus Addae, and Richard Boateng. Cloud computing research: A review of research themes, frameworks, methods and future research directions. International Journal of Information Management, 38(1):128-139, 2018.

[31] Poonam Singh, Maitreyee Dutta, and Naveen Aggarwal. A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems, 52(1):1-51, 2017.

[32] Medhat A Tawfeek, Ashraf El-Sisi, Arabi E Keshk, and Fawzy A Torkey. Cloud task scheduling based on ant colony optimization. In 2013 8th international conference on computer engineering & systems (ICCES), pages 64-69. IEEE, 2013.

[33] Medhat A Tawfeek, Ashraf B El-Sisi, Arabi E Keshk, and Fawzy A Torkey. Virtual machine placement based on ant colony optimization for minimizing resource wastage. In International Conference on Advanced Machine Learning Technologies and Applications, pages 153-164. Springer, 2014.

[34] Xin-She Yang, Mehmet Karamanoglu, and Simon Fong. Bat algorithm for topology optimization in microelectronic applications. In The First International Conference on Future Generation Communication Technologies, pages 150-155. IEEE, 2012.
How to Cite
UDDIN, Mohammed Yousuf; ABDELJABER, Hikmat Awad; AHANGER, Tariq Ahamed. Development of a Hybrid Algorithm for efficient Task Scheduling in Cloud Computing environment using Artificial Intelligence. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 16, n. 5, oct. 2021. ISSN 1841-9844. Available at: <>. Date accessed: 18 oct. 2021. doi: