Abstract
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.