• Akhila Menon

This research delves into edge-to-cloud collaboration for scalable applications, with a primary focus on an adaptive resource allocation strategy. The study aims to explore the dynamics and implications of this strategy in distributed computing environments. The core objective is to analyze the feasibility and effectiveness of the adaptive resource allocation strategy in optimizing computational resources for real-time processing and workload management. Through a comprehensive review of literature and industry practices, the study establishes a foundational understanding of edge-to-cloud collaboration dynamics and its impact on scalability and performance. The proposed adaptive resource allocation strategy is designed as a dynamic framework leveraging machine learning algorithms, especially predictive analytics, to intelligently allocate resources across edge devices and cloud servers. Key components include workload monitoring, predictive analytics models, dynamic allocation algorithms, and optimization mechanisms. Discussions within the study revolve around the benefits of the adaptive resource allocation strategy, such as improved resource utilization, reduced latency, enhanced scalability, and cost-effectiveness. The study also addresses challenges such as data synchronization, communication protocols, and security considerations associated with implementing this strategy.

scalable applications,edge to cloud collaboration,cloud computing,edge computing,predictive analysis

"Enhancing Scalable Applications: Adaptive Edge-to-Cloud Collaboration", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.c224-c229, June-2024, Available :http://www.jetir.org/papers/JETIR2406230.pdf

"Enhancing Scalable Applications: Adaptive Edge-to-Cloud Collaboration", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppc224-c229, June-2024, Available at : http://www.jetir.org/papers/JETIR2406230.pdf