Distributed computing by leveraging and rewarding idling user resources from P2P networks

Abstract Currently, many emerging computer science applications call for collaborative solutions to complex projects that require huge amounts of computing resources to be completed, e.g., physical science simulation, big data analysis. Many approaches have been proposed for high performance computing designing a task partitioning strategy able to assign pieces of execution to the appropriate workers in order to parallelize task execution. In this paper, we describe the Coremuniti T M system, our peer to peer solution for solving complex works by using the idling computational resources of users connected to our network. More in detail, we designed a framework that allows users to share their CPU and memory in a secure and efficient way. By doing this, users help each other by asking the network computational resources when they face high computing demanding tasks. In this respect, as users provide their computational power without providing specific human skill, our approach can be considered as a hybrid crowdsourcing. Differently from many proposals available for volunteer computing, users providing their resources are rewarded with tangible credits, i.e., they can redeem their credits by asking for computational power to solve their own task and/or by exchanging them for money. We conducted a comprehensive experimental assessment in an interesting scenario as 3D rendering, which allowed us to validate the scalability and effectiveness of our solution and its profitability for end-users. 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 farms or cloud based executions.