Cost‐efficient disaster backup for multiple data centers using capacity‐constrained multicast

To leverage periodic disaster backup in a cloud data center (DC) network, previous studies employ disjoint unicast paths for bulk data transfers among multiple geographically distributed DCs, causing massive unnecessary traffic duplication. This not only adds the overhead but also may result in severe network congestion. With flexible network resource management in software‐defined networks and powerful traffic aggregation capability of multicast, we propose capacity‐constrained multicast to realize cost‐efficient disaster backup. First, considering limited backup storage capacity and essential redundancy guarantee, we construct a capacity‐constrained multicasting backup model. Then, we formulate the disaster backup problem as capacity‐constrained multiple Steiner tree problem, which is NP‐hard. To solve this problem, we design a new multicasting backup ant colony optimization algorithm based on requirement‐aware growth. It directly optimizes every disaster‐backup multicast tree (DBMT) from its root node to cover enough destination nodes guaranteeing sufficient redundancy and then expands them into the forest under the guidance of a multicast tree shared degree, the ratio of available storage capacity, and backup load distribution offset. We introduce unique edge fitness evaluation and pheromones for every DBMT to reduce mutual influences among multiple trees. Extensive simulations demonstrate that our strategy performs with less bandwidth consumption cost and relatively good backup load distribution fairness simultaneously.