Resource Allocation Challenges and Solutions in Cloud Computing for Machine Learning Workloads: A Comprehensive Review
DOI:
https://doi.org/10.65417/ljere.v1i2.53Keywords:
Resource Allocation, Cloud Computing, Machine Learning Fog Computing, Efficient Distribution, Technological Advancement, Scalability, FlexibilityAbstract
In the rapidly evolving landscape of digital computation, the integration of cloud and fog computing stands at the forefront, offering unprecedented scalability and flexibility. This review paper delves into the critical realm of resource allocation within cloud computing, exploring various techniques and their implications, especially in the context of burgeoning machine learning applications. While cloud environments are revolutionizing how data-driven tasks are approached and executed, they concurrently pose intricate challenges, especially concerning efficient resource distribution. By examining related works and methodologies, this review provides a comprehensive understanding of current solutions and the inherent challenges they aim to address. As machine learning tasks become increasingly prevalent within the cloud, the nuances of resource allocation become even more pronounced, demanding innovative solutions. This paper encapsulates these nuances, charting a path for future research and highlighting the immense potential waiting to be unlocked in the confluence of cloud computing and machine learning.
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