Home / Current Issue / Paper 1707655
Optimizing Microservices in .NET 8: Performance, Security, and Scalability
Subject area: Science,Engineering and Technology · Area of research: .NET
Abstract
The modern cloud-native application relies on a microservices architecture to develop applications through modules and achieve scalability and tolerance of faults. Many significant enhancements related to performance optimization, security improvements, and scalability features have been introduced in .NET 8. This paper examines the most effective techniques for optimizing .NET 8 microservices through containerization mechanisms, Kubernetes orchestration, and service mesh deployment strategies. This investigation shows how machine learning models can use historical water quality records to perform event prediction of contaminations while boosting treatment operation efficiency. The document provides solutions to important issues, including microservices resource management, service communication, and security vulnerabilities affecting microservices deployments.
Keywords
Microservices Optimization, .NET 8 Performance Tuning, Kubernetes and Service Mesh, Cloud Security in Microservices, Machine Learning in Water Quality Analysis
How to cite this paper
@article{1707655,
author = {Sohan Singh Chinthalapudi},
title = {Optimizing Microservices in .NET 8: Performance, Security, and Scalability},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {7},
pages = {641-655},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707655.pdf},
abstract = {The modern cloud-native application relies on a microservices architecture to develop applications through modules and achieve scalability and tolerance of faults. Many significant enhancements related to performance optimization, security improvements, and scalability features have been introduced in .NET 8. This paper examines the most effective techniques for optimizing .NET 8 microservices through containerization mechanisms, Kubernetes orchestration, and service mesh deployment strategies. This investigation shows how machine learning models can use historical water quality records to perform event prediction of contaminations while boosting treatment operation efficiency. The document provides solutions to important issues, including microservices resource management, service communication, and security vulnerabilities affecting microservices deployments.},
keywords = {Microservices Optimization, .NET 8 Performance Tuning, Kubernetes and Service Mesh, Cloud Security in Microservices, Machine Learning in Water Quality Analysis},
month = {January},
}