As one of the most popular platforms in big data stream computing, Storm is suffering from the problem of high energy consumption and low energy efficiency due to the lack of consideration for energy saving strategy in the design process. Without taking the performance constraint of Storm into consideration, the traditional energy-efficient strategies may affect the real-time performance of cluster. Aiming at this issue, models of the resource constraint, the optimal executor reallocation, and the data migration are set up, and the energy-efficient strategy based on executor reallocation and data migration in Storm (ERDM) is further proposed, while ERDM is composed of resource constraint algorithm and data migration algorithm. The resource constraint algorithm estimates whether the cluster is appropriate for data migration according to the utilization of CPU, memory, and network bandwidth in each work node. The data migration algorithm designs optimal method to migrate data according to the resource constraint model and the optimal executor reallocation model. Moreover, the ERDM allocates the executors so as to reduce communication cost between nodes. The ERDM is evaluated by measuring the cluster performance as well as energy consumption efficiency in big data stream computing environment. The experimental results show that the proposed strategy can reduce communication cost and energy consequence efficiently while the cluster performance is improved compared with existing researches.