Performing store-and-forward (SnF) using abundant storage resources inside datacenters has been proven to be effective in overcoming the challenges faced by inter-datacenter bulk transfers. Most prior studies attempt to fully leverage the network infrastructure and maximize the flexibility of the SnF scheme. Their proposed scheduling methods hence aim at a full storage placement where all network nodes (e.g., datacenters) are SnF-enabled and every node is taken into account in the scheduling process. However, the computational complexity of the prior methods exponentially increases with the network scale. As a result, the prior methods may become too complicated to implement for large-scale networks and online scheduling. In this work, based on the inter-datacenter optical network, SnF models are presented to quantify the impact of the number of SnF-enabled nodes on the performance and the complexity of the SnF scheduling problem. Our key findings show that taking a few SnF-enabled nodes into account in the scheduling process can provide high performance while maintaining low complexity under certain circumstances. It is unnecessary to take every node into account in the scheduling process. Therefore, a node-constraint SnF scheduling method is proposed, whose features are twofold: 1) by taking a portion of nodes into account, it reduces the complexity of the SnF scheduling problem; 2) by introducing a topology abstraction, it condenses the link states between the considered nodes and hence reduces the problem size, which improves its efficiency in solving the SnF scheduling problem. Simulations demonstrate that the proposed method outperforms the prior method in terms of blocking probability and computation time.