Scalable Container-Based Time Synchronization for Smart Grid Data Center Networks
The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Technologies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7080/13/3/105 |
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| Summary: | The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (DCN) with low-cost, and fast-converging optimization is required. This paper introduces a container-based time synchronization model (CTSM) within a spine–leaf virtual private cloud (SL-VPC), deployed via AWS CloudFormation stack as a practical use case. The CTSM optimizes resource utilization, security, and traffic management while reducing computational overhead. The model was benchmarked against five DCN topologies—DCell, Mesh, Skywalk, Dahu, and Ficonn—using Mininet simulations and a software-defined CloudFormation stack on an Amazon EC2 HPC testbed under realistic SG traffic patterns. The results show that CTSM achieved near-100% reliability, with the highest received energy data (29.87%), lowest packetization delay (13.11%), and highest traffic availability (70.85%). Stateless container engines improved resource allocation, reducing administrative overhead and enhancing grid stability. Software-defined Network (SDN)-driven adaptive routing and load balancing further optimized performance under dynamic demand conditions. These findings position CTSM-SL-VPC as a secure, scalable, and efficient solution for next-generation smart grid automation. |
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| ISSN: | 2227-7080 |