Data and Analytics Workflows for Decision Systems Enabled by Learning-Based RAN Intelligence across Distributed Computing Environments
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I2P116Keywords:
Distributed Analytics, Learning-Based RAN, Decision Systems, Edge Intelligence, Workflow Orchestration, Adaptive Networks, AI-Driven Optimization, Low-Latency SystemsAbstract
The development of distributed computing, edge intelligence, and learning-based Radio Access Network (RAN) optimization is fundamentally changing the process of designing and operating modern decision systems. The classical decision architecture was based upon centralized analytics, deterministic policy and fixed optimization strategies. Nevertheless, modern digital services require extremely low latency, dynamic flexibility, situational sensitivity, and uncertainty resiliency. They are especially exacerbated in situations where the decision systems are required to execute with respect to a heterogeneous infrastructure such as cloud platform, edge node, and RAN layers. In this paper, a detailed outline of client-focused data and analytics processes based on learning-oriented RAN intelligence will be demonstrated in order to design scalable, adaptable and latency-sensitive decision processes. To address this, we recommend a combined workflow framework, which is a distributed data acquisition, hierarchical analytics, and machine learning-based RAN intelligence. The framework focuses on decision-centric processing pipelines, cross-layer feedback loops and adaptive resource orchestration mechanisms. RAN-intelligence based on learning is as critical an enabler because it dynamically optimizes network behaviour in response to traffic patterns, user mobility and application requirements. The suggested solution illustrates the ability of intelligent workflows to decrease the decision latency, increase the level of reliability, and improve the system-level performance indicators. The paper also examines architectural elements, algorithmic plans and orchestration of workflow that is required to deploy learning-enabled decision systems. The major contributions are: (i) a workflow based reference architecture, (ii) mathematical representations between learning policies and distributed analytics behavior, (iii) an assessment of the accuracy and responsiveness of decisions made and (iv) experimental evidence that demonstrates an efficiency improvement. The findings suggest that RAN-aware workflows based on learning have high throughput efficiency, accuracy of decisions and reduce latencies as compared to the traditional non-adaptive systems. The current work is part of the wider discussion of intelligent distributed systems, as it formalizes the interaction between network intelligence and analytics processes. The results provide the practical recommendations to implement future decision platforms that can work in large-scale computing ecosystems.
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