A　key　factor　of　win-win　cloud　economy　is　how　to　trade　off　between　the　application　performance　from　customers　and　the　profit　of　cloud　providers.　Current　researches　on　cloud　resource　allocation　do　not　sufficiently　address　the　issues　of　minimizing　energy　cost　and　maximizing　revenue　for　various　applications　running　in　virtualized　cloud　data　centers　(VCDCs).　This　paper　presents　a　new　approach　to　optimize　the　profit　of　VCDC　based　on　the　service-level　agreements　(SLAs)　between　service　providers　and　customers.　A　precise　model　of　the　external　and　internal　request　arrival　rates　is　proposed　for　virtual　machines　at　different　service　classes.　An　analytic　probabilistic　model　is　then　developed　for　non-steady　VCDC　states.　In　addition,　a　smart　controller　is　developed　for　fine-grained　resource　provisioning　and　sharing　among　multiple　applications.　Furthermore,　a　novel　dynamic　hybrid　metaheuristic　algorithm　is　developed　for　the　formulated　profit　maximization　problem,　based　on　simulated　annealing　and　particle　swarm　optimization.　The　proposed　algorithm　can　guarantee　that　differentiated　service　qualities　can　be　provided　with　higher　overall　performance　and　lower　energy　cost.　The　advantage　of　the　proposed　approach　is　validated　with　trace-driven　simulations.　Note　to　Practitioners-Resource　allocation　plays　an　important　role　in　constructing　scalable　and　green　VCDC.　This　work　presents　a　novel　and　fundamental　framework　to　achieve　dynamic　fine-grained　resource　allocation.　It　develops　a　dynamic　fine-grained　resource　allocation　model　with　non-steady　states　according　to　the　external　and　internal　workload　of　different　resource-intensive　applications　in　a　VCDC.　In　order　to　meet　the　SLA　requirements　of　Gold　and　Silver　services　for　various　applications　while　maximizing　profit,　this　work　proposes　a　dynamic　hybrid　optimization　algorithm　by　combing　particle　swarm　optimization　and　simulated　annealing.　The　experimental　results　show　that　the　proposed　method　has　a　great　potential　to　maximize　the　VCDC　provider＇s　profit.　The　proposed　framework　can　aid　the　design　and　optimization　of　industrial　cloud　data　centers　and　practitioners＇　understanding　of　SLA　aspects　of　various　applications.