我们在无计算网络中能走多远：计算的正确性和准确性
How Far Can We Go in Compute-less Networking: Computation Correctness and Accuracy
摘要：
新兴的应用，如增强现实和触觉互联网是计算密集型和延迟敏感的，这阻碍了他们在受限的终端设备单独或在遥远的云中运行。这种应用的严格要求推动了边缘计算的实现，其中计算被卸载到用户附近。无计算网络是边缘计算的扩展，旨在通过采用网络内计算和计算重用来减少计算和缩短通信。计算重用旨在缓存计算结果，并在将来使用它们执行类似的任务，从而避免冗余计算并优化资源的使用。在本文中，我们专注于计算重用产生的最终输出的正确性。由于输入可能不完全相同，但相似，因此重复使用以前的计算会对最终结果的准确性产生疑问。为此，我们实现了一个概念证明，以研究和衡量计算重用的有效性和效率。我们能够将任务完成时间缩短高达80%，同时确保高正确性。我们进一步讨论开放的挑战，并强调未来的研究方向。
英文摘要：
Emerging applications such as augmented reality and tactile Internet are compute-intensive and latency-sensitive, which hampers their running in constrained end devices alone or in the distant cloud. The stringent requirements of such application drove to the realization of Edge computing in which computation is offloaded near to users. Compute-less networking is an extension of edge computing that aims at reducing computation and abridging communication by adopting in-network computing and computation reuse. Computation reuse aims to cache the result of computations and use them to perform similar tasks in the future and, therefore, avoid redundant calculations and optimize the use of resources. In this paper, we focus on the correctness of the final output produced by computation reuse. Since the input might not be identical but similar, the reuse of previous computation raises questions about the accuracy of the final results. To this end, we implement a proof of concept to study and gauge the effectiveness and efficiency of computation reuse. We are able to reduce task completion time by up to 80% while ensuring high correctness. We further discuss open challenges and highlight future research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
