Dynamic AI-Enabled Framework for Real-Time Analytics in Distributed Cloud Ecosystems

Abstract

As a result of this explosion in connected devices, digital services, cloud and streamed data there has been a significant demand for real-time analytics systems and the amount of data, which must be processed in a minimum delay. Artificial Intelligence (AI), distributed cloud computing, real-time data processing and workload variability and decisionmaking based on this data within a particular time frame with processing latency and scalability issues arises in the conventional cloud-based analytics approaches. It comes in four layers: data ingestion, distributed processing, AI-driven analytics, dynamically managing resources and visualization. The proposed approach is worked out to increase the responsiveness, scalability, resource efficiency and decision-making power of the analytical system kept under optimization throughout. The paper discusses an analysis of the problem and describes a systematic method for creating clever real-time analysis system applications in a distributed set-up with dynamically changing data volume and dynamics, as well as dynamically changing computational requirements.