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All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu
Nipa Akter
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Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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ENHANCING DATA PRIVACY AND SECURITY IN MULTI CLOUD ENVIRONMENTS Md Emran Hossain; Md Farhad Kabir; Abdullah Al Noman; Nipa Akter; Zakir Hossain
BULLET : Jurnal Multidisiplin Ilmu Vol. 1 No. 05 (2022): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

In this study, we present and realize a solution for contributing to the provision of data security and data privacy in a hybrid configuration based Multi Cloud environment. This method combines prevention of independent cloud security attacks and server failures through a Byzantine fault tolerance protocol, a data encoding and decoding mechanism using the Dusky architecture to improve reliability and confidentiality; and Shamir's secret sharing scheme to guarantee data trustworthiness and privacy during storage at the cost of a minor performance implication. They compared the security and privacy of their hybrid approach with well-known protocols such as SAML with proxy encryption and Kerberos, showing the benefits in terms of memory footprint, encryption/decryption time and totaltimetoauthenticate. The experimental results show that our hybrid scheme provides considerable improvements with regard to encryption\\/decryption time, memory consumption and average precision.
The Role of AI and Machine Learning in Optimizing Cloud Resource Allocation Abdullah Al Noman; Zakir Hossain; Md Abu Shihab; Nipa Akter; Nur Nahar Rimi; Md Farhad Kabir
International Journal of Multidisciplinary Sciences and Arts Vol. 2 No. 1 (2023): International Journal of Multidisciplinary Sciences and Arts, Article January 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v1i2.2190

Abstract

Resource allocation inside the cloud infrastructure is an essential requirement for better performance and cost-effectiveness. With workloads growing so much more complex and dynamic, we now need automated solutions that allow us to move past tedious, manual resource management. AI provides a unique paradigm for optimizing resource allocation in such complex and dynamic environments. In this paper, we have compared various AI techniques such as machine learning, evolutionary algorithms and deep reinforcement learning for the problem of resource allocation in cloud infrastructure. We analyze their effectiveness for dynamic resource provisioning to satisfy performance objectives while reducing operational expenditure. We offer an in-depth performance comparison using real-world datasets and simulations to showcase the merits and drawbacks of each. Implications: We believe that our findings can offer guidelines to cloud providers, researchers, and practitioners who are interested in making proactive improvements on managing cloud infrastructures using intelligent resource allocation techniques.
Co-Authors Abdullah Al Noman Md Abu Shihab Md Emran Hossain Md Farhad Kabir Nur Nahar Rimi Zakir Hossain