Serving　the　ever-growing　demand　for　computation,　storage,　and　networking　resources　for　multi-tenant　in　cloud　computing　is　an　important　mission　of　Data　Center　Networks　(DCNs).　In　this　paper,　we　study　the　dynamic　request　updating　problem,　and　our　objective　is　to　maximize　the　elasticity　of　cloud-based　DCNs　while　achieving　rapid　response　to　multi-tenants.　We　use　virtual　clusters　under　the　hose　communication　model　to　denote　requests.　Instead　of　using　heuristic　algorithms　as　the　existing　work　does,　this　paper　introduces　a　novel　two-stage　dynamic　request　updating　framework　with　elastic　resource　scheduling　strategy.　In　the　first　stage,　we　propose　a　multi-tenant　fast　initial　provisioning　scheme　to　realize　the　real-time　response　and　analyze　its　optimality　and　complexity.　Additionally,　we　provide　a　deep　reinforcement　learning-based　dynamic　updating　strategy　to　enhance　the　elasticity　of　virtual　clusters　that　are　being　used　or　scaling　during　the　second　stage.　We　train　a　fully　connected　neural　network　by　creating　a　new　feasible　action　set　to　realize　the　reduction,　and　it　approximates　the　policy　based　on　a　proposed　aggressive　objective　selection　method　to　improve　training　speed　while　avoiding　high　dimensions　caused　by　large　scales　of　tenants　and　DCNs.　Extensive　evaluations　demonstrate　that　our　scheme　outperforms　baselines　in　terms　of　both　elasticity　and　efficiency.　©　2013　IEEE.