Minimizing the cost of periodically replicated systems via model and quantitative analysis

Geographically replicating objects across multiple data centers improves the performance and reliability of cloud storage systems. Maintaining consistent replicas comes with high synchronization costs, as it faces more expensive WAN transport prices and increased latency. Periodic replication is the widely used technique to reduce the synchronization costs. Periodic replication strategies in existing cloud storage systems are too static to handle traffic changes, which indicates that they are inflexible in the face of unforeseen loads, resulting in additional synchronization cost. We propose quantitative analysis models to quantify consistency and synchronization cost for periodically replicated systems, and derive the optimal synchronization period to achieve the best tradeoff between consistency and synchronization cost. Based on this, we propose a dynamic periodic synchronization method, Sync-Opt, which allows systems to set the optimal synchronization period according to the variable load in clouds to minimize the synchronization cost. Simulation results demonstrate the effectiveness of our models. Compared with the policies widely used in modern cloud storage systems, the Sync-Opt strategy significantly reduces the synchronization cost.