Advances in Managed Services Optimization for End-to-End Network Performance in High-Density Mobile Environment

• Author(s): Nasiru Hayatu ; Abraham Ayodeji Abayomi ; Abel Chukwuemeke Uzoka
• Paper ID: 1708634
• Page: 301-322
• Published Date: 31-05-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 9 March-2020

Abstract

The exponential growth of mobile users and connected devices in high-density environments such as stadiums, urban centers, airports, and event venues poses significant challenges to network performance, reliability, and user experience. Traditional network management strategies are increasingly inadequate in responding to dynamic traffic demands, latency sensitivity, and service continuity. This paper explores recent advances in managed services optimization aimed at enhancing end-to-end network performance within high-density mobile ecosystems. Emphasis is placed on the integration of Artificial Intelligence (AI) and Machine Learning (ML) for predictive analytics, real-time traffic orchestration, and anomaly detection. Key innovations include self-optimizing network frameworks, network slicing in 5G architectures, and cloud-native operations that ensure scalability and agility. The paper also examines the role of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) in enabling dynamic service provisioning and reducing time-to-resolution of network incidents. Furthermore, case studies from telecom providers are presented to highlight the measurable benefits of these technologies, including improved throughput, lower packet loss, minimized downtime, and enhanced Quality of Experience (QoE). Particular attention is given to performance metrics such as spectral efficiency, network latency, handover success rates, and load balancing efficiency. The integration of Edge Computing and AI-enabled analytics platforms for real-time decision-making in congested scenarios is also discussed. Additionally, the challenges of deploying and managing such optimized frameworks—ranging from data privacy, regulatory compliance, and infrastructure limitations to vendor interoperability—are critically analyzed. This work proposes a holistic optimization architecture for managed services that leverages intelligent automation, hybrid cloud infrastructure, and continuous feedback loops to sustain network performance under extreme user density conditions. The findings serve as a guide for telecom operators, managed service providers, and policymakers in planning, implementing, and scaling network strategies that are resilient, adaptive, and aligned with evolving consumer and enterprise demands.

Keywords

Managed Services, High-Density Networks, Network Optimization, AI in Networking, 5G Network Slicing, SDN, NFV, Edge Computing, Quality of Experience (QoE), Network Automation.

Citations

IRE Journals:
Nasiru Hayatu , Abraham Ayodeji Abayomi , Abel Chukwuemeke Uzoka "Advances in Managed Services Optimization for End-to-End Network Performance in High-Density Mobile Environment" Iconic Research And Engineering Journals Volume 3 Issue 9 2020 Page 301-322

IEEE:
Nasiru Hayatu , Abraham Ayodeji Abayomi , Abel Chukwuemeke Uzoka "Advances in Managed Services Optimization for End-to-End Network Performance in High-Density Mobile Environment" Iconic Research And Engineering Journals, 3(9)