Cloud computing has become a widely used environment for database querying. In this context, the goal of a query optimizer is to satisfy the needs of tenants and maximize the provider’s benefit. Resource allocation is an important step toward achieving this goal. Allocation methods are based on analytical formulas and statistics collected from a catalog to estimate the cost of various possible allocations and then choose the best one. However, the allocation initially chosen is not necessarily the optimal one because of the approximate nature of the analytical formulas and the fact that the catalog may not be up to date. To solve this problem, existing work was proposed to collect statistics during the execution of the query and then trigger a re-allocation if suboptimality is detected. However, these proposals consider that queries have the same level of priority. Unlike the existing work, we propose in this paper a method of statistics collector placement and resource re-allocation by taking into account that the cloud is a multi-tenant environment and queries have different services-level agreements. In the experimental section, we show that our method provides a better benefit for the provider compared to state-of-the-art methods.