Optimizing Network Performance, Automation, and Intelligent Decision-Making through Real-Time Big Data Analytics

Abstract

Network performance optimization and automation are critical for providing quality digital services and enabling data-driven decision making. This paper examines how real-time big data analytics can be leveraged to optimize network operations, enable intelligent automation, and empower data-driven decision making. A conceptual framework is presented illustrating the key components for building a real-time analytics solution including data acquisition, stream processing, data warehousing, complex event processing, predictive modeling, visualization, and automation. Critical technical and organizational enablers are discussed. Through an extensive literature review and real-world use cases, the paper demonstrates the application of real-time analytics optimizing performance across network domains including capacity planning, traffic engineering, cybersecurity, customer experience management and network operations automation. Challenges in adopting real-time analytics are analyzed and mitigation strategies proposed. Overall, the paper highlights the immense potential of real-time big data analytics in driving the next phase of innovation for communication service providers by enabling intelligent, automated, and optimized network performance along with data-empowered decision making.