Cloud Computing: Kubernetes Application Performance Improvement Using In-Memory Database
Author(s): Binoy Kurikaparambil Revi
Publication #: 2503006
Date of Publication: 01.05.2022
Country: USA
Pages: 1-5
Published In: Volume 8 Issue 3 May-2022
DOI: https://doi.org/10.5281/zenodo.14951871
Abstract
With remarkable advancements in computing power and the emergence of Kubernetes clusters, application deployment in the cloud has effectively addressed numerous challenges related to accessibility, availability, security, and scalability. This transformation is particularly evident for complex applications that demand substantial computing resources, which can now run efficiently on platforms like Azure Kubernetes Service. This service provides access to high-performance GPUs and CPUs, allowing applications to harness computing powers for their computational needs. As powerful computing resources become increasingly accessible, application performance and functionality expectations have evolved significantly. Today, most modern applications are intricately reliant on data for training machine learning models, data mining, or executing advanced image processing tasks. This heavy reliance on data necessitates the ability to process large datasets with remarkable speed and accuracy. The In-Memory database has emerged as an invaluable solution to meet these stringent requirements. This approach enables data storage in memory, accelerating computational algorithms and enhancing fast caching capabilities. It streamlines application data processing and offers a significant advantage over traditional methods that rely on files or conventional on-disk databases.
Keywords: Kubernetes, In-Memory Database, Redis, Docker
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