We present FLIC, a distributed software data caching framework for fogs that reduces network traffic and latency. FLICis targeted toward city-scale deployments of cooperative IoT devices in which each node gathers and shares data with surrounding devices. As machine learning and other data processing techniques that require large volumes of training data are ported to low-cost and low-power IoT systems, we expect that data analysis will be moved away from the cloud. Separation from the cloud will reduce reliance on power-hungry centralized cloud-based infrastructure. However, city-scale deployments of cooperative IoT devices often connect to the Internet with cellular service, in which service charges are proportional to network usage. IoT system architects must be clever in order to keep costs down in these scenarios. To reduce the network bandwidth required to operate city-scale deployments of cooperative IoT systems, FLIC implements a distributed cache on the IoT nodes in the fog. FLIC allows the IoT network to share its data without repetitively interacting with a simple cloud storage service reducing calls out to a backing store. Our results displayed a less than 2% miss rate on reads. Thus, allowing for only 5% of requests needing the backing store. We were also able to achieve more than 50% reduction in bytes transmitted per second.

中文翻译：

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FLIC：用于城市规模应用的分布式雾缓存

我们提出了 FLIC，这是一种用于雾的分布式软件数据缓存框架，可减少网络流量和延迟。FLIC 的目标是城市规模的协作物联网设备部署，其中每个节点都收集并与周围设备共享数据。随着需要大量训练数据的机器学习和其他数据处理技术被移植到低成本、低功耗的物联网系统，我们预计数据分析将远离云。与云分离将减少对耗电的集中式云基础设施的依赖。然而，城市规模的协作物联网设备部署通常通过蜂窝服务连接到互联网，其中服务费用与网络使用量成正比。物联网系统架构师必须很聪明，才能在这些场景中降低成本。为了减少运营协作物联网系统的城市规模部署所需的网络带宽，FLIC 在雾中的物联网节点上实施了分布式缓存。FLIC 允许物联网网络共享其数据，而无需与简单的云存储服务重复交互，从而减少对后备存储的调用。我们的结果显示读取的缺失率低于 2%。因此，只允许 5% 的请求需要后备存储。我们还能够将每秒传输的字节数减少 50% 以上。我们的结果显示读取的缺失率低于 2%。因此，只允许 5% 的请求需要后备存储。我们还能够将每秒传输的字节数减少 50% 以上。我们的结果显示读取的缺失率低于 2%。因此，只允许 5% 的请求需要后备存储。我们还能够将每秒传输的字节数减少 50% 以上。