Adaptive AI Algorithms for Load Forecasting and Balancing in Hybrid Edge-Cloud Architectures - Sateesh Kumar Nallamala; Punukollu, Pavan; Punukollu, Midhun; Burugu, Sreeharsha; Yerneni, Raghuveer Prasad; Kodali, Sricharan (Los Angeles Journal of Intelligent Systems and Pattern Recognition, 2021)
The integration of edge and cloud computing has become essential for meeting the demands of modern applications that require high processing power, low latency, and scalable resources. Hybrid edge-cloud architectures offer a promising solution to this problem by combining the strengths of both paradigms. One critical challenge in such systems is the efficient load forecasting and balancing across the edge and cloud layers. Adaptive artificial intelligence (AI) algorithms have emerged as a key enabler for optimizing load distribution in these hybrid systems. This paper explores the use of adaptive AI techniques for load forecasting and balancing in hybrid edge-cloud architectures, focusing on their potential to improve resource allocation, reduce latency, and enhance system performance. Various adaptive AI models, including machine learning and deep learning algorithms, are discussed in the context of their application to load forecasting and balancing. The study also addresses the challenges and benefits of deploying AI-driven load management strategies in hybrid architectures, particularly in real-time scenarios. The paper concludes by highlighting future research directions and the potential of AI to enable more efficient and scalable hybrid edge-cloud systems.