Efficient high performance computing by user networks

The advances in computational techniques both from a software and hardware viewpoint lead to the development of projects whose complexity could be quite challenging, e.g., biomedical simulations. In order to deal with the increased demand of computational power many collaborative approaches have been proposed in order to apply proper partitioning strategy able to assign pieces of execution to a crowd of workers. In this paper, we address this problem in a peer to peer way. We leverage the idling computational resources of users connected to a network. More in detail, we designed a framework that allows users to share their CPU and memory in a secure and efficient way. The latter allows users help each others by asking the network computational resources when they face high computing demanding tasks. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead), we hypothesize a remarkable side effect at steady state: energy consumption reduction compared with traditional server farm or cloud based executions.