Authors :
B. Suganya; Dr.R. Padmapriya
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4752t9x4
DOI :
https://doi.org/10.5281/zenodo.8295899
Abstract :
The development of cloud computing in
current decades has led to it serving as the basis for a
variety of systems. It enables customers to access a list of
specified resources, act immediately and adaptably to
customer preferences, and only be charged for actual
utilization. One of the most important problems in cloud
computing is Task Scheduling (TS). The issue is how to
equitably distribute and organize the user-provided
tasks for Virtual Machine (VM) execution. Also, user
experience is directly impacted by the effectiveness of
scheduling efficiency. As a result, the TS issue in cloud
computing has to be more precisely addressed. In cloud
computing, the TS is essential such that the optimal
scheduling of task requests may boost network
efficiency. The main objective of TS is to assign tasks to
appropriate processors to create the shortest deadline
achievable without compromising on priority criteria.
Numerous research has been conducted to design TS
schemes based on various metaheuristic and machine
learning algorithms that satisfy several criteria such as
minimization of the makespan, execution cost and
energy. They have demonstrated that conventional TS is
effective only to satisfy certain criteria and have devised
an optimum solution using multi-objectives in cloud
computing. This paper presents a systematic and
extensive analysis of TS algorithms in cloud computing
depending on the different optimization and machine
learning algorithms. Also, it addresses the challenges in
those algorithms and recommends a few possible
solutions for improving the utilization of cloud
computing.
Keywords :
Cloud computing, Task scheduling, Virtual machine, Makespan, Metaheuristic, Machine learning, Optimization.
The development of cloud computing in
current decades has led to it serving as the basis for a
variety of systems. It enables customers to access a list of
specified resources, act immediately and adaptably to
customer preferences, and only be charged for actual
utilization. One of the most important problems in cloud
computing is Task Scheduling (TS). The issue is how to
equitably distribute and organize the user-provided
tasks for Virtual Machine (VM) execution. Also, user
experience is directly impacted by the effectiveness of
scheduling efficiency. As a result, the TS issue in cloud
computing has to be more precisely addressed. In cloud
computing, the TS is essential such that the optimal
scheduling of task requests may boost network
efficiency. The main objective of TS is to assign tasks to
appropriate processors to create the shortest deadline
achievable without compromising on priority criteria.
Numerous research has been conducted to design TS
schemes based on various metaheuristic and machine
learning algorithms that satisfy several criteria such as
minimization of the makespan, execution cost and
energy. They have demonstrated that conventional TS is
effective only to satisfy certain criteria and have devised
an optimum solution using multi-objectives in cloud
computing. This paper presents a systematic and
extensive analysis of TS algorithms in cloud computing
depending on the different optimization and machine
learning algorithms. Also, it addresses the challenges in
those algorithms and recommends a few possible
solutions for improving the utilization of cloud
computing.
Keywords :
Cloud computing, Task scheduling, Virtual machine, Makespan, Metaheuristic, Machine learning, Optimization.
