Dynamic Task Migration for Enhanced Load Balancing in Cloud Computing using K-means Clustering and Ant Colony Optimization

Main Article Content

Aliva Priyadarshini
Sateesh Kumar Pradhan
Saumendra Pattnaik
Suprava Ranjan Laha
Binod Kumar Pattanayak


Cloud computing efficiently allocates resources, and timely execution of user tasks is pivotal for ensuring seamless service delivery. Central to this endeavour is the dynamic orchestration of task scheduling and migration, which collectively contribute to load balancing within virtual machines (VMs). Load balancing is a cornerstone, empowering clouds to fulfill user requirements promptly. To facilitate the migration of tasks, we propose a novel method that exploits the synergistic potential of K-means clustering and Ant Colony Optimization (ACO). Our approach aims to maximize the cloud ecosystem by improving several critical factors, such as the system's make time, resource utilization efficiency, and workload imbalance mitigation. The core objective of our work revolves around the reduction of makespan, a metric directly tied to the overall system performance. By strategically employing K-means clustering, we effectively group tasks with similar attributes, enabling the identification of prime candidates for migration. Subsequently, the ACO algorithm takes the reins, orchestrating the migration process with an inherent focus on achieving global optimization. The multifaceted benefits of our approach are quantitatively assessed through comprehensive comparisons with established algorithms, namely Round Robin (RR), First-Come-First-Serve (FCFS), Shortest Job First (SJF), and a genetic load balancing algorithm. To facilitate this evaluation, we harness the capabilities of the CloudSim simulation tool, which provides a platform for realistic and accurate performance analysis. Our research enhances cloud computing paradigms by harmonizing task migration with innovative optimization techniques. The proposed approach demonstrates its prowess in harmonizing diverse goals: reducing makespan, elevating resource utilization efficiency, and attenuating the degree of workload imbalance. These outcomes collectively pave the way for a more responsive and dependable cloud infrastructure primed to cater to user needs with heightened efficacy. Our study delves into the intricate domain of cloud-based task scheduling and migration. By synergizing K-means clustering and ACO algorithms, we introduce a dynamic methodology that refines cloud resource management and bolsters the quintessential facet of load balancing. Through rigorous comparisons and meticulous analysis, we underscore the superior attributes of our approach, showcasing its potential to reshape the landscape of cloud computing optimization.

Article Details

How to Cite
Priyadarshini, A. ., Pradhan, S. K. ., Pattnaik, S. ., Laha, S. R. ., & Pattanayak, B. K. . (2023). Dynamic Task Migration for Enhanced Load Balancing in Cloud Computing using K-means Clustering and Ant Colony Optimization. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7), 156–162. https://doi.org/10.17762/ijritcc.v11i7.7841


Laha, S. R., Parhi, M., Pattnaik, S., Pattanayak, B. K., & Patnaik, S. (2020). Issues, Challenges and Techniques for Resource Provisioning in Computing Environment. In 2020 2nd International Conference on Applied Machine Learning (ICAML) (pp. 157-161). IEEE.

Pattanaik, B. C., Sahoo, B. K., Pati, B., & Laha, S. R. (2023). Dynamic Fault Tolerance Management Algorithm for VM Migration in Cloud Data Centers. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 85-96.

Parhi, M., Pattanayak, B. K., & Patra, M. R. (2018). A multi-agent-based framework for cloud service discovery and selection using ontology. Service Oriented Computing and Applications, 12, 137-154.

Pati, A., Parhi, M., Alnabhan, M., Pattanayak, B. K., Habboush, A. K., & Al Nawayseh, M. K. (2023). An IoT-Fog-Cloud Integrated Framework for Real-Time Remote Cardiovascular Disease Diagnosis. In Informatics (Vol. 10, No. 1, p. 21). MDPI.

Rao, M. N. . (2023). A Comparative Analysis of Deep Learning Frameworks and Libraries. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 337–342. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2707

Pati, A., Parhi, M., & Pattanayak, B. K. (2022). Heartfog: Fog computing enabled ensemble deep learning framework for automatic heart disease diagnosis. In Intelligent and Cloud Computing: Proceedings of ICICC 2021 (pp. 39-53). Singapore: Springer Nature Singapore.

Dr. S. Praveen Chakkravarthy. (2020). Smart Monitoring of the Status of Driver Using the Dashboard Vehicle Camera. International Journal of New Practices in Management and Engineering, 9(01), 01 - 07. https://doi.org/10.17762/ijnpme.v9i01.81

Pattanayak, B. K., Pattnaik, O., & Pani, S. (2021). Dealing with Sybil attack in VANET. In Intelligent and Cloud Computing: Proceedings of ICICC 2019, Volume 1 (pp. 471-480). Springer Singapore.

Katal, A., Dahiya, S., & Choudhury, T. (2023). Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing, 26(3), 1845-1875.

Al Nuaimi, K., Mohamed, N., Al Nuaimi, M., & Al-Jaroodi, J. (2012). A survey of load balancing in cloud computing: Challenges and algorithms. In 2012 second symposium on network cloud computing and applications (pp. 137-142). IEEE.

Hussein, W., Peng, T., & Wang, G. (2015). A weighted throttled load balancing approach for virtual machines in cloud environment. International Journal of Computational Science and Engineering, 11(4), 402-408.

Paul Garcia, Ian Martin, Laura López, Sigurðsson Ólafur, Matti Virtanen. Enhancing Student Engagement through Machine Learning: A Review. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/163

Deepa, T., & Cheelu, D. (2017). A comparative study of static and dynamic load balancing algorithms in cloud computing. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3375-3378). IEEE.

Mishra, S. K., Sahoo, B., & Parida, P. P. (2020). Load balancing in cloud computing: a big picture. Journal of King Saud University-Computer and Information Sciences, 32(2), 149-158.

Chraibi, A., Ben Alla, S., & Ezzati, A. (2021). Makespan optimisation in cloudlet scheduling with improved DQN algorithm in cloud computing. Scientific Programming, 2021, 1-11.

LD, D. B., & Krishna, P. V. (2013). Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied soft computing, 13(5), 2292-2303.

Sahoo, D. K. . (2022). A Novel Method to Improve the Detection of Glaucoma Disease Using Machine Learning. Research Journal of Computer Systems and Engineering, 3(1), 67–72. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/44

Remesh Babu, K. R., & 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.

Singh, A., Juneja, D., & Malhotra, M. (2015). Autonomous agent based load balancing algorithm in cloud computing. Procedia Computer Science, 45, 832-841.

Madni, S. H. H., Latiff, M. S. A., Coulibaly, Y., & Abdulhamid, S. I. M. (2017). Recent advancements in resource allocation techniques for cloud computing environment: a systematic review. cluster computing, 20, 2489-2533.

Mapetu, J. P. B., Chen, Z., & Kong, L. (2019). Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing. Applied Intelligence, 49, 3308-3330.q