AI for self-adaptive cloud systems: Towards fully autonomous data centers

The increasing complexity of modern computing systems, coupled with growing demands for energy-efficient and cost-effective data centers, has driven the need for self-adaptive cloud systems. Advancements in artificial intelligence hold the promise of enabling fully autonomous data centers that can adapt to dynamic workloads, optimize resource utilization, and reduce environmental impact. This paper explores the applications of AI techniques in the context of self-adaptive cloud systems, highlighting the potential for AI-powered solutions to address key challenges in the design, operation, and maintenance of modern data centers. The rapid growth of cloud computing and the proliferation of data-intensive applications have placed significant strain on the infrastructure of modern data centers. To meet the demands for increased computing power, storage, and energy efficiency, cloud providers and data center operators must navigate a complex landscape of operational challenges, including workload fluctuations, resource allocation, energy management, and fault tolerance. AI-driven approaches offer a promising avenue to address these challenges, enabling cloud systems to become more self-aware, self-healing, and self-optimizing.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-30
DOI
https://doi.org/10.5281/zenodo.17163899
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI for self-adaptive cloud systems: Towards fully autonomous data centers

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

AI for self-adaptive cloud systems: Towards fully autonomous data centers

article en

Abstract

The increasing complexity of modern computing systems, coupled with growing demands for energy-efficient and cost-effective data centers, has driven the need for self-adaptive cloud systems. Advancements in artificial intelligence hold the promise of enabling fully autonomous data centers that can adapt to dynamic workloads, optimize resource utilization, and reduce environmental impact. This paper explores the applications of AI techniques in the context of self-adaptive cloud systems, highlighting the potential for AI-powered solutions to address key challenges in the design, operation, and maintenance of modern data centers. The rapid growth of cloud computing and the proliferation of data-intensive applications have placed significant strain on the infrastructure of modern data centers. To meet the demands for increased computing power, storage, and energy efficiency, cloud providers and data center operators must navigate a complex landscape of operational challenges, including workload fluctuations, resource allocation, energy management, and fault tolerance. AI-driven approaches offer a promising avenue to address these challenges, enabling cloud systems to become more self-aware, self-healing, and self-optimizing.

Zenodo (CERN European Organization for Nuclear Research)
Film Independent (US)
Openalex Percentile: Top 33%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.