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.　©　2021,　Science　Press.　All　right　reserved.