Optimizing edge computing and AI for low-latency cloud workloads

Ravi Chandra Thota *

Independent Researcher, Sterling, Virginia, USA.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 3484-3500.
Article DOI: 10.30574/ijsra.2024.13.1.1761
Publication history: 
Received on 30 August 2024; revised on 22 September 2024; accepted on 24 September 2024
 
Abstract: 
Cloud workload evolution has progressed because end-users need real-time applications such as autonomous systems, industrial IoT, and innovative healthcare. Traditional cloud computing systems cause substantial latency because they process information centrally while sending and receiving data. Artificial intelligence and Edge computing unite to provide an effective solution through network edge-based computations distribution, enabling fast real-time data processing. This research analyzes important strategies used in edge computing and artificial intelligence technology to minimize delays in cloud computing operations.
The paper introduces basic principles of edge computing with AI capabilities for cloud workload management. We examine three main challenges: network bottleneck, processing capacity limitations, and security threat considerations. The proposed solution incorporates edge AI accelerators with hardware implementation and lightweight AI models, federated learning and reinforcement learning as software approaches, and 5G technology and edge caching as network optimization methods. The document demonstrates real-world applications based on case studies and experimental outcomes from autonomous vehicles, the healthcare sector, and industrial IoT implementations.
The paper conducts a systematic evaluation that compares edge-based AI systems against traditional cloud computing based on their latency, power efficiency, and flexibility performance. It examines upcoming technologies that involve AI-driven self-adjusting edge networks and combinations of cloud and edge AI platforms. The discussion grows in technical depth through the addition of graphs, flowcharts, pseudocode, and system diagrams.
Our research confirms that AI integration with edge computing lowers end-to-end latency while improving real-time decisions and maximizing cloud-based resource performance. The study establishes a framework for developing next-generation cloud architectural infrastructure that uses low-latency artificial intelligence.
 
Keywords: 
AI At the Edge; Edge Computing Architecture; Energy-Efficient AI Models; Federated Learning for Edge AI
 
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