Deepak D

Work place: Department of Information Science and Engineering, Nitte (Deemed to be University), NMAM Institute of Technology, Nitte, Karnataka, India



Research Interests: Deep Learning, Cloud Computing, Machine Learning


Deepak D received his B. E degree in Computer Science & Engineering from Sri Taralabalu Jagadguru Institute of Technology, Ranebennur, Karnataka - Visvesvaraya Technological University, Belagavi, M.Tech degree in Software Engineering, AMC Engineering College, Bengaluru, Karnataka- Visvesvaraya Technological University, Belagavi, and currently pursuing his Ph.D. in Computer Science & Engineering from VTU, Belgavi. His major research interest is in the fields of Machine Learning, Cloud Computing, Deep Learning. He has 11 years of teaching experience and currently, he is working as an Asst. Professor (Senior Grade) in the Department of Information Science & Engineering at NMAM Institute of Technology, Nitte. He has published several articles in various national and international conferences and journals. He is a lifetime member of ISTE.

Author Articles
Dynamic Load Balancing in Cloud Computing: A Convergence of PSO, GSA, and Fuzzy Logic within a Hybridized Metaheuristic Framework

By Rajgopal K T Abhishek S. Rao Ramaprasad Poojary Deepak D

DOI:, Pub. Date: 8 Dec. 2023

In the recent era, there has been a significant surge in the demand for cloud computing due to its versatile applications in real-time situations. Cloud computing efficiently tackles extensive computing challenges, providing a cost-effective and energy-efficient solution for cloud service providers (CSPs). However, the surge in task requests has led to an overload on cloud servers, resulting in performance degradation. To address this problem, load balancing has emerged as a favorable approach, wherein incoming tasks are allocated to the most appropriate virtual machine (VM) according to their specific needs. However, finding the optimal VM poses a challenge as it is considered a difficult problem known as NP-hard. To address this challenge, current research has widely adopted meta-heuristic approaches for solving NP-hard problems. This research introduces a novel hybrid optimization approach, integrating the particle swarm optimization algorithm (PSO) to handle optimization, the gravitational search algorithm (GSA) to improve the search process, and leveraging fuzzy logic to create an effective rule for selecting virtual machines (VMs) efficiently. The integration of PSO and GSA results in a streamlined process for updating particle velocity and position, while the utilization of fuzzy logic assists in discerning the optimal solution for individual tasks. We assess the efficacy of our suggested method by gauging its performance through various metrics, including throughput, makespan, and execution time. In terms of performance, the suggested method demonstrates commendable performance, with average load, turnaround time, and response time measuring at 0.168, 18.20 milliseconds, and 11.26 milliseconds, respectively. Furthermore, the proposed method achieves an average makespan of 92.5 milliseconds and average throughput performance of 85.75. The performance of the intended method is improved by 90.5%, 64.9%, 36.11%, 24.72%, 18.27%, 11.36%, and 5.21 in comparison to the existing techniques. The results demonstrate the efficacy of this approach through significant improvements in execution time, CPU utilization, makespan, and throughput, providing a valuable contribution to the field of cloud computing load balancing.

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