Cognitive Cloud Computing: Harnessing AI to Enable Proactive Fault Prediction and Resource Allocation in Complex Cloud Systems

Authors

  • Prathyusha Nama
  • Purushotham Reddy
  • Suprit Kumar Pattanayak

Keywords:

Cognitive Cloud Computing, Artificial Intelligence (AI), Proactive Fault Prediction, Resource Allocation, Cloud Systems

Abstract

Cognitive cloud computing represents a paradigm shift in the management of cloud systems, leveraging artificial intelligence (AI) to enhance operational efficiency and reliability. This research article explores integrating AI techniques in proactive fault prediction and resource allocation within complex cloud environments. Traditional cloud computing faces significant challenges, including unpredictable faults and inefficient resource utilization, leading to performance degradation and increased operational costs. By employing cognitive computing principles, this study aims to identify and implement advanced AI methodologies, such as machine learning and predictive analytics, to predict potential failures before they occur and optimize resource allocation dynamically. Through a comprehensive literature review and analysis of case studies, the article highlights successful implementations of these techniques, discusses the implications for cloud service providers, and outlines future research directions in this emerging field. The findings underscore the potential of cognitive cloud computing to transform cloud management by enabling proactive decision-making and enhancing overall system resilience.

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Published

30-03-2022

How to Cite

Prathyusha Nama, Purushotham Reddy, & Suprit Kumar Pattanayak. (2022). Cognitive Cloud Computing: Harnessing AI to Enable Proactive Fault Prediction and Resource Allocation in Complex Cloud Systems. Well Testing Journal, 31(1), 36–63. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/112

Issue

Section

Research Articles

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