Research Article
HGCSO: Energy Efficient Multi-objective Task Scheduling in Cloud-Fog Environment
@INPROCEEDINGS{10.1007/978-3-031-66044-3_2, author={Santhosh Kumar Medishetti and Vamsheedhar Reddy Pillareddy and Bushra Muneeb and Sudha Rani Palakuri and Uma Maheshwari Garela and Rakesh Kumar Donthi and G. Soma Sekhar and Ganesh Reddy Karri and Baji Babu Indurthi and K. Vamshi Krishna}, title={HGCSO: Energy Efficient Multi-objective Task Scheduling in Cloud-Fog Environment}, proceedings={Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24--25, 2023, Proceedings}, proceedings_a={PERSOM}, year={2024}, month={8}, keywords={cloud-fog computing efficient task scheduling resource utilization Genetic Algorithms Cat Swarm Optimization}, doi={10.1007/978-3-031-66044-3_2} }- Santhosh Kumar Medishetti
Vamsheedhar Reddy Pillareddy
Bushra Muneeb
Sudha Rani Palakuri
Uma Maheshwari Garela
Rakesh Kumar Donthi
G. Soma Sekhar
Ganesh Reddy Karri
Baji Babu Indurthi
K. Vamshi Krishna
Year: 2024
HGCSO: Energy Efficient Multi-objective Task Scheduling in Cloud-Fog Environment
PERSOM
Springer
DOI: 10.1007/978-3-031-66044-3_2
Abstract
In the ever-evolving realm of cloud-fog computing, effective Task Scheduling (TS) plays a crucial role in maximizing resource utilization and enhancing overall system performance. This paper introduces a novel approach to TS using a hybrid algorithm that combines Genetic Algorithms (GA) and Cat Swarm Optimization (CSO). The proposed Hybrid Genetic Cat Swarm Optimization (HGCSO) algorithm harnesses the strengths of GA for global exploration and CSO for its unique swarm-based search mechanism. The integration of these algorithms aims to address the complexities of cloud-fog computing environments, characterized by heterogeneous resources and varying task requirements. The TS problem is formulated as an optimization challenge, incorporating objectives such as reducing makespan, response time, and energy consumption. The HGCSO algorithm iteratively refines solutions by employing genetic operators for diversity and swarm-based mechanisms for local refinement. Thorough simulations have been carried out to assess the performance of the algorithm, juxtaposing it against conventional scheduling algorithms as well as standalone Genetic Algorithm (GA) and Cuckoo Search Optimization (CSO) approaches. The outcomes reveal that the suggested HGCSO algorithm surpasses its counterparts, showcasing improvements in makespan time, response time, and a notable reduction in energy usage by 22%, 18%, and 28%, respectively. This research provides significant insights into the progress of bio-inspired algorithms in tackling the complex challenges of Task Scheduling (TS) within contemporary computing paradigms.
