To　meet　the　increasing　inter-datacenter　traffic,　datacenter　storage　is　brought　in　the　forwarding　path.　Bulk　data　that　are　delay-tolerant　can　be　temporarily　stored　and　forwarded　(SnF)　at　a　later　time　when　link　is　less　busy.　However,　the　use　of　storage　transforms　the　conventional　routing　problem　into　a　scheduling　problem,　where　both　bandwidth　and　storage　resources　must　be　allocated　and　both　spatial　routing　and　temporal　scheduling　must　be　performed.　Such　an　SnF　scheduling　problem　is　critically　important　for　the　efficiency　of　SnF　approaches.　Most　prior　solutions　aimed　to　jointly　solve　its　temporal　and　spatial　components　and　formulated　this　problem　into　difficult　optimization　problems,　which　contributes　to　a　huge　expansion　size　of　the　problem　and　hence　are　too　complex　for　large　networks　and　dynamic　traffic.　In　this　paper,　we　present　analytic　models　to　quantify　the　performance-complexity　tradeoff　in　the　SnF　scheduling　problem.　Our　key　findings　reveal　that　desirable　performance　can　be　obtained　by　considering　only　a　few　pre-selected　routes　rather　than　searching　in　the　entire　network　topology.　Thus,　we　propose　a　time-space　decoupled　(TSD)　SnF　scheduling　method.　Compared　to　the　conventional　joint　methods,　the　advantages　of　the　TSD　method　are　as　follows:　(i)　by　decoupling　the　problem　and　solving　them　separately,　the　TSD　method　reduces　the　quadratic　complexity　of　the　joint　methods　to　linear　complexity;　(ii)　by　condensing　the　redundant　states,　the　TSD　method　obtains　a　longer　horizon　of　temporal　scheduling,　given　the　same　computational　cost;　(iii)　by　bounding　the　spatial　hop　count　of　routing　paths,　the　TSD　method　avoids　the　detour　issue　faced　by　the　joint　methods　and　hence　uses　bandwidth　more　efficiently;　(iv)　by　formulating　the　problem　into　a　routing　problem,　the　TSD　method　greatly　simplifies　the　problem　for　dynamic　traffic.　Simulations　demonstrate　that　the　TSD　method　can　outperform　the　conventional　joint　method,　especially　when　the　traffic　load　is　moderate-to-high.