Authors :
Dr. Reshmi B; Dr. P. Poongodi
Volume/Issue :
Volume 6 - 2021, Issue 5 - May
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3uY6pjO
DOI :
https://doi.org/10.5281/zenodo.6427461
Abstract :
Bigdata analytics with High Performance
Computing has attained focus of various researchers due
to the services that has been provided to the cloud users
with user satisfaction. Understanding the evolution of big
data systems and HPC systems helps to define the key
differences, the goals behind them, and their
architectures. There are four broad application classes
that driving the requirements of data analytics tools and
frameworks. They are the data pipelines, large-scale
machine learning including deep learning applications
streaming applications, and graph applications.
Historically, HPC systems have given less focus to data
management and more focus to designing highperformance algorithms. Big data systems have done an
excellent job in data management, data queries, and
streaming applications. In this Research optimal
scheduling of group of tasks would be done by using
Locality Aware Scheduling based on Cuckoo Search
Algorithm (LS-CSA) and the performance of Bigdata
systems can immensely benefit from HPC. This method
would schedule the similar tasks that shares the same data
in the virtual machine where its corresponding data
resides. The overall evaluation of the research work is
done in the Cloudsim environment which is implemented
and evaluated in terms of various performance metrics.
The proposed research method provides optimal results
than the existing research methods.
Keywords :
HPC Systems, ML, Scientific application, Workflow, Big Data Systems.
Bigdata analytics with High Performance
Computing has attained focus of various researchers due
to the services that has been provided to the cloud users
with user satisfaction. Understanding the evolution of big
data systems and HPC systems helps to define the key
differences, the goals behind them, and their
architectures. There are four broad application classes
that driving the requirements of data analytics tools and
frameworks. They are the data pipelines, large-scale
machine learning including deep learning applications
streaming applications, and graph applications.
Historically, HPC systems have given less focus to data
management and more focus to designing highperformance algorithms. Big data systems have done an
excellent job in data management, data queries, and
streaming applications. In this Research optimal
scheduling of group of tasks would be done by using
Locality Aware Scheduling based on Cuckoo Search
Algorithm (LS-CSA) and the performance of Bigdata
systems can immensely benefit from HPC. This method
would schedule the similar tasks that shares the same data
in the virtual machine where its corresponding data
resides. The overall evaluation of the research work is
done in the Cloudsim environment which is implemented
and evaluated in terms of various performance metrics.
The proposed research method provides optimal results
than the existing research methods.
Keywords :
HPC Systems, ML, Scientific application, Workflow, Big Data Systems.
