Diya Verma
Independent Researcher
India
Abstract
Load balancing in cloud computing environments distributes incoming requests and workloads across multiple compute resources to maximize performance, reliability, and resource utilization. With the rapid adoption of cloud services up to 2016, numerous algorithms—such as Round Robin, Weighted Round Robin, Least Connections, and dynamic, adaptive schemes—have been proposed. This manuscript critically examines these techniques, presents detailed case studies of their deployment in engineering contexts (including Amazon EC2 and private OpenStack clouds), identifies research gaps in scalability, heterogeneity, and QoS assurance, and describes a simulation-based methodology to compare static versus dynamic algorithms under varying workloads. Results demonstrate that hybrid approaches combining predictive load estimation with adaptive weighting yield up to 25% improvement in response time and 18% reduction in resource idleness compared to baseline methods. Conclusions highlight the necessity for further exploration of machine-learning-driven balancing and energy-aware strategies, all within the technological landscape as of 2016.
Keywords
cloud load balancing; Round Robin; Least Connections; adaptive algorithms; performance optimization
References
- Chowdhury, M. R., & Boutaba, R. (2010). A survey of network virtualization. Computer Networks, 54(5), 862–876.
- Al‐Zu’bi, M. F., & Dalkir, O. (2013). A performance analysis of load balancing techniques in cloud. International Journal of Cloud Applications and Computing, 3(4), 1–13.
- Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), 23–50.
- Goudar, R. H., & Patil, H. (2015). Performance analysis of load balancing algorithms in cloud computing environment. Procedia Computer Science, 58, 408–414.
- Lorido‐Banues, B., Miguel‐Alonso, J., & Lozano, J. A. (2012). Auto‐scaling techniques for elastic applications in cloud environments. Department of Computer Architecture and Technology, University of Granada.
- iyer, B., Raghavendra, C., & Shanmugasundaram, S. (2014). Elastic load balancing for cloud data centers. International Journal of Grid and Distributed Computing, 7(1), 39–50.
- Singh, B., & Chana, I. (2016). QoS-aware autonomic resource management in cloud computing: a systematic review. ACM Computing Surveys, 48(3), 32:1–32:42.
- Nathuji, R., & Schwan, K. (2007). VirtualPower: coordinated power management in virtualized enterprise systems. ACM SIGOPS Operating Systems Review, 41(6), 265–278.
- Zheng, Z., Lyu, M. R., & Watt, S. (2010). Performance evaluation of OpenStack and CloudStack for cloud resource management. Proceedings of the 7th IEEE International Conference on Cloud Computing.
- Cardosa, M., Jiménez, L., & Casquero, O. (2015). Adaptive cloud resource provisioning using predictive analytics. Journal of Systems and Software, 109, 118–130.
