In the cloud computing, many of the users send
requests to cloud at the same time to access services. Thus, a
big challenge of scheduling of these tasks is in cloud
computing. Many algorithms like FCFS, SJF, Priority
based, RR, MLQ, LSTR used to schedule the tasks in cloud
computing. In cloud computing, most of the data centers
consume vast amount of energy and take much more time to
schedule the jobs. In this research paper, deploy a hybrid
algorithm for job scheduling in cloud computing, using the
combination of Multi-Level Feedback Queue Scheduling
and Least Slack Time Rate (LSTR) is proposed to improve
the issue of maximum energy consumption and time
consumption by the data centers. Least Slack Time Rate is
used to first select those processes that have the smallest
“slack time”. Multi-Level Feedback Queue is used in this
scheme the processes can move between the different
queues. MLFQ uses the working principle of Round Robin
and First come First Serve scheduling algorithms. The
performance of the proposed method is measured by
calculating the parameters of Energy Consumption,
Minimize Processing Time, Executed Jobs, and Unexecuted
Jobs.
During the survey of various job scheduling algorithms, it
has been widely observed that these algorithms consume
vast amount of energy and consume more time for
scheduling of jobs. So, the main motive behind this
research is to propose an algorithm which consumes less
energy and time for scheduling of jobs which are sent to
cloud at the same time to access services. Apart from
designing the proposed solution, its implementation is
also done using Dot Net framework over Microsoft Azure
server. Moreover, in order to reflect the optimal efficiency
of this composite platform in comparison to their
individual counterparts, performance analysis has been
done on the basis of energy consumption and time
consumption etc. and the outcome is graphically reflected
and thoroughly discussed.
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