In　cloud　data　centers,　the　exponential　growth　of　data　places　increasing　demands　on　computing,　storage,　and　network　resources,　especially　in　multi-tenant　environments.　While　this　growth　is　crucial　for　ensuring　Quality　of　Service　(QoS),　it　also　introduces　challenges　such　as　fluctuating　resource　requirements　and　static　container　configurations,　which　can　lead　to　resource　underutilization　and　high　energy　consumption.　This　article　addresses　online　resource　provisioning　and　efficient　scheduling　for　multi-tenant　environments,　aiming　to　minimize　energy　consumption　while　balancing　elasticity　and　QoS　requirements.　To　address　this,　we　propose　a　novel　optimization　framework　that　reformulates　the　resource　provisioning　problem　into　a　more　manageable　form.　By　reducing　the　original　multi-constraint　optimization　to　a　container　placement　problem,　we　apply　the　interior-point　barrier　method　to　simplify　the　optimization,　integrating　constraints　directly　into　the　objective　function　for　efficient　computation.　We　also　introduce　elasticity　as　a　key　parameter　to　balance　energy　consumption　with　autonomous　resource　scaling,　ensuring　that　resource　consolidation　does　not　compromise　system　flexibility.　The　proposed　Energy-Efficient　and　Elastic　Resource　Provisioning　(EEP)　framework　comprises　three　main　modules:　a　distributed　resource　management　module　that　employs　vertical　partitioning　and　dynamic　leader　election　for　adaptive　resource　allocation;　a　prediction　module　using　omega-step　prediction　for　accurate　resource　demand　forecasting;　and　an　elastic　scheduling　module　that　dynamically　adjusts　to　tenant　scaling　needs,　optimizing　resource　allocation　and　minimizing　energy　consumption.　Extensive　experiments　across　diverse　cloud　scenarios　demonstrate　that　the　EEP　framework　significantly　improves　energy　efficiency　and　resource　utilization　compared　to　established　baselines,　supporting　sustainable　cloud　management　practices.