Cost-Efficient Data Backup for Data Center Networks against {\epsilon}-Time Early Warning Disaster

TL;DR

This paper proposes a cost-efficient data backup framework for geo-distributed data center networks to protect against predictable disasters with a limited early warning time, optimizing backup node selection and data transmission paths.

Contribution

It introduces an ILP-based theoretical model for optimal backup node and path selection under early warning constraints, addressing emergency data protection in DCNs.

Findings

The ILP model effectively minimizes backup costs.

Numerical results validate the framework's efficiency.

The approach enhances disaster preparedness in DCNs.

Abstract

Data backup in data center networks (DCNs) is critical to minimize the data loss under disaster. This paper considers the cost-efficient data backup for DCNs against a disaster with $\varepsilon$ early warning time. Given geo-distributed DCNs and such a $\varepsilon$-time early warning disaster, we investigate the issue of how to back up the data in DCN nodes under risk to other safe DCN nodes within the $\varepsilon$ early warning time constraint, which is significant because it is an emergency data protection scheme against a predictable disaster and also help DCN operators to build a complete backup scheme, i.e., regular backup and emergency backup. Specifically, an Integer Linear Program (ILP)-based theoretical framework is proposed to identify the optimal selections of backup DCN nodes and data transmission paths, such that the overall data backup cost is minimized. Extensive numerical results are also provided to illustrate the proposed framework for DCN data backup.