Parallel simulation is a powerful tool to evaluate the performance of large-scale systems. However when it comes to simulating large scale Web search engines, the parallel simulation execution can introduce imbalance among processors because event occurrence is driven by user behavior which is unpredictable, making events take place in different parts of the system in an irregular manner. In this paper, we study the impact of load balance strategies on the performance of a parallel simulation strategy. In particular, we present a consistent hashing load balance algorithm aimed to reduce queuing waiting times, evenly distribute the costs of executing events among processors, and more importantly migration of logical processes only occurs between neighbor processors. We use a Web search engine composed by services deployed on a cluster of processors as the application case study. Our simulations are driven by actual query log traces. We evaluate our proposed load balance algorithm in terms of running time, communication, memory consumption and accuracy. Results show that the proposed load balance strategy is capable of reducing the execution times of simulations and maintaining quality of results.