Abstract
Big memory applications such as in-memory database, denovo assembly application in the human genome sequencing area, big data analytics, and large scale scientific calculation are increasing explosively. However, the big memory system has been too expensive for many researchers and students. Therefore, methods to harvest remotely distributed memory has been considered as a cost effective way to run big memory applications in the cluster environment where computing nodes are connected via high speed network. We designed and implemented a new framework, DMIf(Distributed Memory Integrated framework), which harvests idle memory of distributed nodes in a cluster and serves it to the big memory application. The collected distributed memory will provide faster data processing in a cost-effective way for the big-data/memory application.