Modern in-memory data-intensive computing systems like Spark create long-lived containers to execute diverse types of applications. They rely on a cluster manager like YARN or Mesos to perform resource allocation to the containers. The cluster manager or scheduler requires users of the containers to reserve resources beforehand. It is a challenge to estimate just right amounts of memory to run the applications before execution, so as to avoid over-or under-provisioning of memory space. We discover a general property of memory reservation elasticity, which allows applications to run with a reservation limit smaller than they would ideally need while only paying a moderate performance penalty. Based on the property, we designed a system, namely MEER, which performs online estimation of minimum necessary amount of memory limit that achieves nearly optimal performance. We referred to it as optimal reservation, which divides memory over-provisioning from under-provisioning. It is non-trivial to efficiently estimate optimal reservations on line through one step without runtime history. MEER uses a two-step approach to dealing with the challenge: 1) Do robust profiling and probability density analysis of applications' memory footprints in two pilot runs. By using confidence levels for the prediction, we reduce the negative effects of container footprints' randomness and achieve a highly accurate online initial estimation (over 80% accuracy) of optimal reservation. 2) By exploiting a self-decay property of the analytical results, MEER adaptively performs iterative search based on a feed-back control mechanism over subsequent recurring executions. We implemented MEER atop of YARN and evaluated the prototype by running 15 benchmark workloads on a 16-node local cluster. Evaluation results show that it achieves an average accuracy of more than 95%. By deploying MEER on schedulers and allocating memory according to the optimal reservations, one could improve cluster memory utilization by about 40%. It reduces individual application execution time by 2 to 6 times on average compared to the state-of-the-art approaches. A 90 times peak speedup for PageRank in comparison with the default Spark/Yarn is observed.