Authors :
Aditya Abhinav; Sidharth K; Aman Tomar; Dr. A. Vijay Kumar
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/y889aj48
Scribd :
https://tinyurl.com/22p7ut2e
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1824
Abstract :
In this study, we explore the application of
Monarch Butterfly Optimization (MBO) algorithms for
task scheduling in cloud computing, comparing its
performance against widely used optimization techniques,
namely Ant Colony Optimization (ACO) and Particle
Swarm Optimization (PSO).Task scheduling in the cloud is
a critical aspect influencing resource utilization,
turnaround time, and overall system efficiency. MBO,
known for its effective exploration- exploitation balance, is
examined for its suitability in addressing the complexities
of cloud computing environments. The study investigates
MBO's advantages, such as enhanced adaptability to
dynamic conditions, effective handling of multi-objective
optimization, and its consideration of bandwidth as a
critical resource. Comparative analyses with ACO and
PSO highlight MBO's superior performance in achieving
near-optimal task schedules, emphasizing its potential to
offer innovative solutions to the challenges posed by task
scheduling in dynamic and resource-constrained cloud
environments. This research contributes valuable insights
into the strengths of MBO, paving the way for
advancements in optimization methodologies tailored for
cloud computing systems.
Keywords :
MBO, ACO, SPO, Dual Access Control, Task Scheduling.
In this study, we explore the application of
Monarch Butterfly Optimization (MBO) algorithms for
task scheduling in cloud computing, comparing its
performance against widely used optimization techniques,
namely Ant Colony Optimization (ACO) and Particle
Swarm Optimization (PSO).Task scheduling in the cloud is
a critical aspect influencing resource utilization,
turnaround time, and overall system efficiency. MBO,
known for its effective exploration- exploitation balance, is
examined for its suitability in addressing the complexities
of cloud computing environments. The study investigates
MBO's advantages, such as enhanced adaptability to
dynamic conditions, effective handling of multi-objective
optimization, and its consideration of bandwidth as a
critical resource. Comparative analyses with ACO and
PSO highlight MBO's superior performance in achieving
near-optimal task schedules, emphasizing its potential to
offer innovative solutions to the challenges posed by task
scheduling in dynamic and resource-constrained cloud
environments. This research contributes valuable insights
into the strengths of MBO, paving the way for
advancements in optimization methodologies tailored for
cloud computing systems.
Keywords :
MBO, ACO, SPO, Dual Access Control, Task Scheduling.
