The cloud computing is the new technique used most today for
computation and storage. Edge computing is also a new computing
domain that gives computation of tasks and storage of data nearer to
the data sources. Various applications of edge computing are IOT,
healthcare, retail, manufacturing etc. Virtualization technologies have
enabled cloud platforms to provide various services such as virtual
machines to the users. Task scheduling is the major issues faced in both
cloud and edge domains. Since the usage of machines is high, it is
difficult to assign tasks manually. So, an efficient and optimized
algorithm is required in the cloud as well as the edge environment.
Here, a task scheduling is implemented based on Heuristic
Optimization Technique to examine the performance measures using
cloudsim. Task scheduling and optimization techniques dynamically
allocate resources in the cloud environment. Similarly, in edge
computing, scheduling the task is performed with different optimization
techniques such as random fit, and max fit algorithms. These task
scheduling methods are allocated to the VM based on the resources that
are available at the VM. These algorithms are implemented, and it
increased the VM utilization by 4.82%. The processing time taken is
reduced by 2.67% and the percentage of failed tasks is reduced by
3.68%. The experiment result shows the importance of the scheduling
in the cloud and edge environment, in terms of processing time, failed
task and increase in VM utilization in edge devices.
P. Priya Ponnuswamy, C.P. Shabariram
PSG Institute of Technology and Applied Research, India
IoT, Healthcare, Random Fit, Max Fit, Heuristic optimization Technique, Task Scheduling
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 15 , Issue: 3 , Pages: 3261 - 3270 )
Date of Publication :
September 2024
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