Intelligent Multi-Cloud Orchestration for AI Workloads: Enhancing Performance and Reliability

This paper delves into the synergy between cloud computing and artificial intelligence (AI), presenting Intelligent Cloud Orchestration (ICO) as an innovative framework for optimizing AI workloads within cloud environments. ICO, powered by sophisticated AI algorithms, dynamically orchestrates, and enhances the deployment, execution, and management of AI workloads, tackling challenges related to scalability, efficiency, and reliability. Beginning with a comprehensive overview of ICO’s foundational AI-driven principles and architectures, the paper elucidates its core components and functionalities. It further explores ICO’s AI-specific applications, including intelligent resource allocation, predictive workload scheduling, adaptive auto-scaling, real-time performance monitoring, and fault tolerance, showeasing its ability to analyze real-time data and system metrics for proactive decision-making and adaptive adjustments. The discussion extends to the integration of ICO with existing cloud orchestration frameworks and management systems, illustrating how ICO seamlessly enhances AI capabilities across popular cloud platforms and services through case studies and practical examples. Emphasizing ICO’s transformative potential, the paper underscores its role in revolutionizing AI workload management and optimization in cloud environments, offering organizations unprecedented levels of performance, scalability, and reliability for their cloud-based AI applications, and paving the way for the next generation of intelligent cloud computing solutions.

Authors

Institutions

Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1109/ic3i61595.2024.10828941
Citations
8
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
6.23
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intelligent Multi-Cloud Orchestration for AI Workloads: Enhancing Performance and Reliability

Naween Kumar, Yajnaseni Dash, Subham Sharma, K. Merina Devi et al.
8 citations
Cloud Computing and Resource Management
6.23
article

Intelligent Multi-Cloud Orchestration for AI Workloads: Enhancing Performance and Reliability

Naween Kumar, Yajnaseni Dash, Subham Sharma, K. Merina Devi, Ankit Dubey
article en
8 citations

Abstract

This paper delves into the synergy between cloud computing and artificial intelligence (AI), presenting Intelligent Cloud Orchestration (ICO) as an innovative framework for optimizing AI workloads within cloud environments. ICO, powered by sophisticated AI algorithms, dynamically orchestrates, and enhances the deployment, execution, and management of AI workloads, tackling challenges related to scalability, efficiency, and reliability. Beginning with a comprehensive overview of ICO’s foundational AI-driven principles and architectures, the paper elucidates its core components and functionalities. It further explores ICO’s AI-specific applications, including intelligent resource allocation, predictive workload scheduling, adaptive auto-scaling, real-time performance monitoring, and fault tolerance, showeasing its ability to analyze real-time data and system metrics for proactive decision-making and adaptive adjustments. The discussion extends to the integration of ICO with existing cloud orchestration frameworks and management systems, illustrating how ICO seamlessly enhances AI capabilities across popular cloud platforms and services through case studies and practical examples. Emphasizing ICO’s transformative potential, the paper underscores its role in revolutionizing AI workload management and optimization in cloud environments, offering organizations unprecedented levels of performance, scalability, and reliability for their cloud-based AI applications, and paving the way for the next generation of intelligent cloud computing solutions.

Bennett University (IN)
Openalex Percentile: Top 3%
Cloud Computing and Resource Management
6.23
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.