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Vol. 1 No. 1 (2020)
2020 Edition

Articles

Secure Performance Optimization in Multi-Tenant Cloud Environments

Published 2020-10-20

  • Vijay Govindarajan
    • ,
  • Raj Sonani
    • ,
  • Pratik Surendrakumar Patel

Vijay Govindarajan
Independent Researcher, USA

Raj Sonani
Independent Researcher, USA

Pratik Surendrakumar Patel
US Bank, USA

Abstract

Multi-tenant cloud environments have become the backbone of modern computing, offering scalability, flexibility, and cost-effectiveness. However, these environments introduce significant challenges related to security and performance optimization. Ensuring robust security mechanisms while maintaining high system performance is crucial to achieving efficient cloud operations. This paper explores techniques to enhance secure performance optimization in multi-tenant cloud environments, addressing key challenges such as resource contention, isolation, data confidentiality, and regulatory compliance. We analyze various strategies, including workload balancing, encryption techniques, access control mechanisms, and machine learning-based anomaly detection. Experimental analysis demonstrates the effectiveness of these solutions in mitigating security vulnerabilities and optimizing computational efficiency. Our findings indicate that adopting a hybrid approach that integrates security protocols with performance-enhancing algorithms significantly improves cloud service reliability. The research contributes to developing practical solutions for securing multi-tenant architectures while ensuring seamless performance, providing a roadmap for future advancements in cloud computing.

Keywords

Multi-Tenant Cloud, Secure Performance Optimization, Resource Contention, Data Confidentiality, , Machine Learning, Access Control, Workload Balancing, Cloud Security

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