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As the HPC systems increase their size, performance of collective communications is becoming an important issue. Usually, decisions for which algorithm of those communications to be used are done based on statically specified thresholds of the size of messages and the number of processes. However, on recent HPC systems that are hiring Fat Tree or Torus topology as their interconnect, the network speed has become unpredictable. The main reason is the effect of contentions. This effect depends heavily on the relative locations of the compute nodes. On the other hand, to reduce the number of idle nodes, there are attempts for building job schedulers to attach compute nodes flexibly, without considering their relative positions among each other. With this policy, the network performance becomes unstable. As an approach for finding an appropriate algorithm even on such environment, a dynamic method, STAR-MPI, has been proposed. This method examines each algorithm at runtime, and uses the empirical data to choose the suitable one for the given situation. This paper first examined the effect of STAR-MPI on an environment with unstable network speed. The results of experiments on this environment showed that the dynamic approach was effective, but the cost for testing slow algorithms limited the effect. Then, the authors proposed an enhancement, in which algorithms that have been predicted relatively slow were discarded from the list of candidates. The predictions were done by using the performance models of the algorithms with the latency and the bandwidth measured at the first call of the collective communication. At this point, the effect of this enhancement shown in experimental results was not significant. However, the results showed that there was a possibility for achieving better performance by using more cost-effective way of prediction and tuning thresholds and factors used in the enhancement.