TITLE:
Job Scheduling for Cloud Computing Using Neural Networks
AUTHORS:
Mahmoud Maqableh, Huda Karajeh, Ra’ed Masa’deh
KEYWORDS:
Cloud Computing, Job Scheduling, Artificial Intelligence, Artificial Neural Networks
JOURNAL NAME:
Communications and Network,
Vol.6 No.3,
August
28,
2014
ABSTRACT:
Cloud computing aims to
maximize the benefit of distributed resources and aggregate them to achieve
higher throughput to solve large scale computation problems. In this
technology, the customers rent the resources and only pay per use. Job
scheduling is one of the biggest issues in cloud computing. Scheduling of
users’ requests means how to allocate resources to these requests to finish the
tasks in minimum time. The main task of job scheduling system is to find the
best resources for user’s jobs, taking into consideration some statistics and
dynamic parameters restrictions of users’ jobs. In this research, we introduce
cloud computing, genetic algorithm and artificial neural networks, and then
review the literature of cloud job scheduling. Many researchers in the
literature tried to solve the cloud job scheduling using different techniques.
Most of them use artificial intelligence techniques such as genetic algorithm
and ant colony to solve the problem of job scheduling and to find the optimal
distribution of resources. Unfortunately, there are still some problems in this
research area. Therefore, we propose implementing artificial neural networks to
optimize the job scheduling results in cloud as it can find new set of
classifications not only search within the available set.