Abstract
The proliferation of real-time artificial intelligence (AI) applications across domains such as autonomous vehicles, smart manufacturing, and healthcare demands computing infrastructures that balance low latency, high processing power, and scalability. Edge-cloud collaboration has emerged as a promising paradigm that leverages the proximity and responsiveness of edge computing with the computational capabilities and resource availability of cloud platforms. This paper explores the architecture, design principles, and operational strategies for effective edge-cloud collaboration in real-time AI systems. Key challenges such as data partitioning, model synchronization, latency constraints, security, and resource orchestration are analyzed, along with current solutions and open research directions. We present use cases that demonstrate the efficacy of collaborative edge-cloud AI, and highlight the trade-offs involved in deploying machine learning inference and training tasks across heterogeneous environments. Our study underscores the critical role of intelligent workload distribution and adaptive system design in enabling efficient, robust, and scalable real-time AI applications.
Citations
IRE Journals:
Mohammed Abdus Salam
"Edge-Cloud Collaboration in Real-Time AI Applications" Iconic Research And Engineering Journals Volume 8 Issue 6 2024 Page 1125-1136
IEEE:
Mohammed Abdus Salam
"Edge-Cloud Collaboration in Real-Time AI Applications" Iconic Research And Engineering Journals, 8(6)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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