Sustainable Data Storage for AI Applications: Securing Critical Information for Environmental Responsibility

EasyChair Preprint 12214

Abstract

As artificial intelligence (AI) continues to permeate various aspects of society, the demand for data storage solutions has surged exponentially, leading to significant environmental concerns. The rapid growth in data generation, particularly driven by AI applications, has exacerbated the carbon footprint of data storage infrastructure. Addressing this challenge requires a paradigm shift towards sustainable data storage practices that not only ensure the security and accessibility of critical information but also minimize environmental impact. This paper investigates the intersection of AI applications and sustainable data storage, emphasizing the importance of environmental responsibility in managing vast datasets. We examine current data storage technologies and their environmental implications, highlighting the carbon emissions associated with conventional storage infrastructure. Additionally, we explore emerging trends in sustainable data storage solutions, including energy-efficient hardware, renewable energy integration, and innovative storage architectures. Furthermore, we discuss the role of data compression, deduplication, and tiered storage strategies in optimizing resource utilization and reducing energy consumption. Leveraging advancements in AI and machine learning, we propose intelligent data management techniques for dynamically adjusting storage configurations based on workload demands, thereby maximizing efficiency while minimizing environmental impact.

Keyphrases: Energy-efficient infrastructure, Environmental Responsibility, Sustainable data storage

@booklet{EasyChair:12214,
  author    = {James Henry and Mehmad Halil},
  title     = {Sustainable Data Storage for AI Applications: Securing Critical Information for Environmental Responsibility},
  howpublished = {EasyChair Preprint 12214},
  year      = {EasyChair, 2024}}