The tremendous advancements in the Internet of Things (IoT) increasingly involve computationally intensive services. These services often require more computation resources than can entirely be satisfied on local IoT devices. Cloud computing is traditionally used to provide unlimited computation resources at distant servers. However, such remote computation may not address the short-delay constraints that many of today's IoT applications require. Edge computing allows offloading computing close to end users to overcome computation and delay issues. Nonetheless, the edge servers may suffer from computing inefficiencies. Indeed, some IoT applications are invoked multiple times by multiple devices. These invocations are often used with the same or similar input data, which leads to the same computational output (results). Still, the edge server willfully executes all received redundant tasks. In this work, we investigate the use of the computation reuse concept at the edge server. We design a network-based computation reuse architecture for IoT applications. The architecture stores previously executed results and reuses them to satisfy newly arrived similar tasks instead of performing computation from scratch. By doing so, we eliminate redundant computation, optimize resource utilization, and decrease task completion time. We implemented the architecture and evaluated its performance both at the networking and application levels. From the networking perspective, we reach up to an 80\% reduction in task completion time and up to 60\% reduction in resource utilization. From the application perspective, we achieve up to 90\% computation correctness and accuracy.
翻译:在Tings Internet(IoT)上的巨大进步越来越多地涉及计算密集的服务。这些服务通常需要更多的计算资源,而当地IoT设备无法完全满足。 Cloud 计算传统上用于为远端服务器提供无限的计算资源。 但是,这种远程计算可能无法解决今天许多IoT应用程序所需要的短期拖延限制。 边缘计算允许在接近终端用户的地方卸载计算,以克服计算和延迟问题。 尽管如此, 边缘服务器可能因为计算效率低而受到影响。 事实上, 多个设备会多次引用一些IoT应用程序。 这些职业往往使用相同的或类似的投入数据,从而导致相同的计算产出(结果)。 尽管如此, 边端服务器仍然会执行所有收到的冗余任务。 在这项工作中,我们调查了在边端服务器上对计算再利用概念的使用情况。 我们设计了一个基于网络的再利用结构, 并重新利用了它们来满足新到的类似任务, 而不是从零开始计算。 通过这样做,我们消除了多余的计算, 优化了资源利用, 并降低了任务完成时间从60年到完成时间。 我们从网络化了80年的利用了架构和业绩, 。