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the pitfalls of both approaches while capitalising on
their strengths (Bandaranayake et al. 2020).
Factors such as makespan, load balancing, and
cloud computing — which inherently deals with an
expansive array of resources and requests — are
pivotal determinants in task scheduling (Etminani and
Naghibzadeh 2007; Tang 2018). Deploying a dataset
abundant with tasks could bolster the assertion of
time efficiency, all the while preserving an elevated
processing speed. For future ventures, transplanting
the proposed method directly into a genuine cloud
computing milieu (via cloud sim) for myriad
empirical evaluations, including scalability,
resilience, and accessibility, could further refine task
scheduling (Gandomi et al. 2020).
6 CONCLUSION
In the article under consideration, the study
underscores the efficiency of the Novel Resource
Aware Scheduling Algorithm, which boasts a
processing speed of 3.7430 MIPS, outpacing the
Max-Min Algorithm that operates at 8.4090 MIPS.
The empirical results derived from the Gridsim
simulator unequivocally establish RASA's
dominance over the Max-Min algorithm, especially
when deployed within extensive distributed systems.
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Improving the Processing Speed of Task Scheduling in Cloud Computing Using the Resource Aware Scheduling Algorithm over the
Max-Min Algorithm
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