Citation :

Vaishali Nagpure , 2023. "Network Automation Platforms: Improving Operational Efficiency in Data Centers" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 1, Issue 1: 144-149.

Abstract :

As modern enterprises scale their digital operations, data centers face increasing demands to provide reliable, high-performance networking solutions. The complexities of managing extensive networks—spanning critical primary links, underutilized backup paths, and dynamic traffic patterns—pose challenges such as performance degradation, delayed fault resolution, and operational inefficiencies. Traditional, manual approaches to network management are insufficient to address these issues on a scale, necessitating the adoption of network automation platforms. This case study explores the implementation of a comprehensive Network Automation Platform designed to optimize operational efficiency in a multinational enterprise's data center environment. The solution integrates cutting-edge tools such as Cisco DNA Center (DNAC) for real-time telemetry, ThousandEyes for advanced path monitoring, Grafana for visualization and alerting, and ServiceNow for streamlined incident management. Automation technologies including Ansible, Terraform, and custom Python workflows enable proactive traffic rerouting, efficient secondary path utilization, and rapid fault remediation. Key use cases are presented to demonstrate the platform's capabilities: Dynamic Traffic Management: Automatic diversion of traffic from congested primary links to underutilized secondary paths ensures optimal resource usage and prevents performance bottlenecks. Load Balancing: Continuous monitoring and redistribution of traffic across backup paths maintain network stability and prevent overloads. Failure Response: Seamless failover mechanisms and automated ticketing in ServiceNow reduce Mean Time to Resolution (MTTR) during outages. The solution was validated using simulated traffic congestion, link failures, and load balancing scenarios, achieving measurable improvements in uptime, latency, and operational efficiency. The platform can reduce MTTR by 40%, optimize backup link utilization by 30%, and automate 80% of repetitive network tasks. This study provides a structured framework for building and implementing such platforms, addressing both technical and operational challenges. Future recommendations include leveraging AI for predictive analytics, integrating SD-WAN controllers for application-aware routing, and expanding monitoring to edge and cloud environments. This approach offers a scalable, resilient, and cost-effective strategy for transforming network operations in data centers, setting a benchmark for enterprises aiming to modernize their IT infrastructure

References :

[1] Haas, D. Swaminathan, and L. Cooper, Terraform: Up and Running (2nd ed.), OReilly Media, 2020.

[2] Ansible for Network Automation, Packt Publishing, 2018.

[3] Y.-F. Liu, K. C.-J. Lin, and C.-C. Tseng, Dynamic Cluster-Based Flow Management for Software Defined Networks, in Proc. of IEEE Network, 2020.

[4] M.A. Ouamri, G. Barb, D. Singh, and F. Alexa, Load Balancing Optimization in Software-Defined Wide Area Networking (SD-WAN) using Deep Reinforcement Learning, 2021.

[5] P. Thornley and M. Bagheri, Software-Defined Networking: Open-source alternatives for Small to Medium Sized Enterprises, Sheffield Hallam University, 2020.

[6] Cisco Documentation: Cisco DNA Center Overview. https://www.cisco.com/c/en/us/products/collateral/cloud-systems-management/dna-center/nb-06-dna-center-so-cte-en.html

[7] SSRN Electronic Journal, Automation of Network Management and Incident Response, 2019.

[8] ThousandEyes Documentation. https://docs.thousandeyes.com/

[9] K. Heinonen and J. Kietzmann, Artificial intelligence and machine learning in service management, 2020.

[10] N.N. Srinidhi, S.M. Dilip Kumar, and K.R. Venugopal, Network optimizations in the Internet of Things: A review, 2021.

[11] M. Tirmazi et al., Borg: The Next-Generation Cluster Manager at Google, ACM SIGCOMM Computer Communication Review, vol. 45, no. 3, pp. 22–32, 2020.

Keywords :

Network Automation, Cisco DNA Center (DNAC), Data Center Efficiency, IT Service Management (ITSM), Software-Defined Networking (SDN .