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Enhancing security and scalability by AI/ML workload optimization in the cloud

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Abstract

The pervasive adoption of Artificial Intelligence (AI) and Machine Learning (ML) applications has exponentially increased the demand for efficient resource allocation, workload scheduling, and parallel computing capabilities in cloud environments. This research addresses the critical need for enhancing both the scalability and security of AI/ML workloads in cloud computing settings. The study emphasizes the optimization of resource allocation strategies to accommodate the diverse requirements of AI/ML workloads. Efficient resource allocation ensures that computational resources are utilized judiciously, avoiding bottlenecks and latency issues that could hinder the performance of AI/ML applications. The research explores advanced parallel computing techniques to harness the full possible cloud infrastructure, enhancing the speed and efficiency of AI/ML computations. The integration of robust security measures is crucial to safeguard sensitive data and models processed in the cloud. The research delves into secure multi-party computation and encryption techniques like the Hybrid Heft Pso Ga algorithm, Heuristic Function for Adaptive Batch Stream Scheduling Module (ABSS) and allocation of resources parallel computing and Kuhn–Munkres algorithm tailored for AI/ML workloads, ensuring confidentiality and integrity throughout the computation lifecycle. To validate the proposed methodologies, the research employs extensive simulations and real-world experiments. The proposed ABSS_SSMM method achieves the highest accuracy and throughput values of 98% and 94%, respectively. The contributions of this research extend to the broader cloud computing and AI/ML communities. By providing scalable and secure solutions, the study aims to empower cloud service providers, enterprises, and researchers to leverage AI/ML technologies with confidence. The findings are anticipated to inform the design and implementation of next-generation cloud platforms that seamlessly support the evolving landscape of AI/ML applications, fostering innovation and driving the adoption of intelligent technologies in diverse domains.

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Authors and Affiliations

  1. Department of CSE, Birla Institute of Technology, Mesra, Jharkhand, 835215, India

    Sabina Priyadarshini

  2. Department Of Electronics and Telecommunications Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, Maharashtra, 400614, India

    Tukaram Namdev Sawant

  3. Vishwakarma Institute of Information Technology, Kondhwa, Pune, 411048, India

    Gitanjali Bhimrao Yadav

  4. Department of Information Technology, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, 638060, India

    J. Premalatha

  5. Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, Maharashtra, 400614, India

    Sanjay R. Pawar

Authors
  1. Sabina Priyadarshini
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  2. Tukaram Namdev Sawant
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  3. Gitanjali Bhimrao Yadav
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  4. J. Premalatha
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  5. Sanjay R. Pawar
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All authors contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript

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Correspondence to Sabina Priyadarshini.

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Priyadarshini, S., Sawant, T.N., Bhimrao Yadav, G. et al. Enhancing security and scalability by AI/ML workload optimization in the cloud. Cluster Comput 27, 13455–13469 (2024). https://doi.org/10.1007/s10586-024-04641-x

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