Enhanced Butterfly Optimization Algorithm for Task Scheduling in Cloud Computing Environments

Cloud computing is transforming the provision of elastic and adaptable capabilities on demand. A scalable infrastructure and a wide range of offerings make cloud computing essential to today's computing ecosystem. Cloud resources enable users and various companies to utilize data maintained in a distant location. Generally, cloud vendors provide services within the limitations of Service Level Agreement (SLA) terms. SLAs consist of various Quality of Service (QoS) requirements the supplier promises. Task scheduling is critical to maintaining higher QoS and lower SLAs. In simple terms, task scheduling aims to schedule tasks to limit wasted time and optimize performance. Considering the NP-hard character of cloud task scheduling, metaheuristic algorithms are widely applied to handle this optimization problem. This study presents a novel approach using the Butterfly Optimization Algorithm (BOA) for scheduling cloud-based tasks across diverse resources. BOA performs well on non-constrained and non-biased mathematical functions. However, its search capacity is limited to shifted, rotated, and/or constrained optimization problems. This deficiency is addressed by incorporating a virtual butterfly and improved fuzzy decision processes into the conventional BOA. The suggested methodology improves throughput and resource utilization while reducing the makespan. Regardless of the number of tasks, better results are consistently produced, indicating greater scalability.

@article{ZHAO2024,
  title     = {Enhanced Butterfly Optimization Algorithm for Task Scheduling in Cloud Computing Environments},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yue ZHAO},
  doi       = {10.14569/IJACSA.2024.0151246},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151246}
}