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

机译:通过利用和奖励P2P网络中闲置的用户资源来进行分布式计算

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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 (TM) 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. (C) 2018 Elsevier Inc. All rights reserved.
机译:当前,许多新兴的计算机科学应用要求为需要大量计算资源才能完成的复杂项目提供协作解决方案,例如物理科学模拟,大数据分析。已经提出了许多用于高性能计算的设计任务划分策略的方法,该任务划分策略能够将执行部分分配给适当的工作程序以并行化任务执行。在本文中,我们描述了Coremuniti(TM)系统,这是我们的对等解决方案,用于通过使用连接到我们网络的用户的空闲计算资源来解决复杂的工作。更详细地说,我们设计了一个框架,该框架允许用户以安全有效的方式共享其CPU和内存。通过这样做,用户可以在面对高计算要求的任务时询问网络计算资源,从而互相帮助。在这方面,由于用户无需提供特定的人员技能即可提供计算能力,因此我们的方法可以被视为混合众包。与可用于自愿计算的许多提议不同,向提供其资源的用户提供有形的积分,即,他们可以通过请求解决自己的任务的计算能力和/或通过将其兑换成金钱来兑现积分。我们在一个有趣的场景(如3D渲染)中进行了全面的实验评估,这使我们能够验证解决方案的可扩展性和有效性以及其对最终用户的盈利能力。由于我们不需要为解决任务而提供额外的资源(我们可以更好地利用已供电的未使用资源),因此我们假设在稳态下会产生显着的副作用:与传统服务器场或基于云的执行相比,能耗降低了。 (C)2018 Elsevier Inc.保留所有权利。

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