Animation rendering consumes massive computation time, therefore cloud rendering is emerging as a solution. Cloud rendering runs over the Data Center Network(DCN) and consolidates heterogeneous DC resources into a single cloud renderfarm, where plentiful computing resources can sufficiently accelerate any rendering process. And if one user wants to get a quick animation result, a high-speed optical interconnection is an urgent requirement, thus cloud rendering needs a convergence of Optical and DCN(ODCN) as the substrate network. In the ODCN supporting cloud rendering, each rendering task will be successfully handled only when we embed its virtual network into the cloud renderfarm. But because a virtual network includes virtual machines and virtual lightpaths, we must simultaneously perform the node-level mapping between virtual machine and server, as well as link-level mapping between virtual lightpath and fiber link(s). In addition, the joint implementation of the Photorealistic cloud Rendering(PR) and Non-Photorealistic cloud Rendering(NPR) should be considered to exhibit the unique animation effect with the low mapping cost. In this paper, considering the unique characteristic of hybrid cloud rendering, we flexibly select routing strategies according to the rendering task type. We then utilize server consolidation and traffic grooming to achieve node- and link-level mappings,respectively, thus building a mapping-cost-aware cloud renderfarm that includes multiple virtual networks. The mathematical formulation is also made with a bound analysis. Especially for the lower bound, we analyze the least number of servers and wavelengths(i.e., mapping cost) consumed by hybrid cloud rendering. In terms of heuristics, according to the processing order of rendering tasks, Smaller Virtual Resource First(SVRF) and Manycast Routing First(MRF) algorithms are proposed by us. In SVRF, NPR tasks are first tackled and then PR tasks follow. MRF is a reverse process of SVRF. The simulation results demonstrate the effectiveness of our methods in reducing the mapping cost because the heuristic solution well matches the lower bound. Animation rendering consumes massive computation time, therefore cloud rendering is emerging as a solution.. Cloud rendering runs over the Data Center Network (DCN) and consolidates heterogeneous DC resources into a single cloud renderfarm, where plentiful computing resources can be accelerated any rendering process. if one user wants to get a quick animation result, a high-speed optical interconnection is an urgent requirement, thus cloud rendering needs a convergence of Optical and DCN (ODCN) as the substrate network. In the ODCN supporting cloud rendering, each rendering task will be successfully handled only when we embed its virtual network into the cloud renderfarm. But because a virtual network includes virtual machines and virtual lightpaths, we must simultaneously perform the node-level mapping between virtual machine and server, as well as link-level mapping between virtual lightpath and fiber link (s). In addition, the joint implementation of the Photorealistic clou d Rendering (PR) and Non-Photorealistic cloud Rendering (NPR) should be considered to exhibit the unique animation effect with the low mapping cost. In this paper, considering the unique characteristic of hybrid cloud rendering, we flexibly select routing strategies according to the rendering task type. We then utilize server consolidation and traffic grooming to achieve node- and link-level mappings, respectively, thus building a mapping-cost-aware cloud renderfarm that includes multiple virtual networks. The mathematical formulation is also made with a bound analysis Especially for the lower bound, we analyze the least number of servers and wavelengths (ie, mapping cost) consumed by hybrid cloud rendering. Manycast Routing First (MRF) algorithms are proposed by us. In SVRF, NPR tasks are first tackled and then PR tasks follow. MRF is a reverse process of SVRF. The simulation results demonstrate the effectiveness of our methods in reducing the mapping cost because the heuristic solution well matches the lower bound.