Introduced as an extension of the Cloud at the network edge for computing and storage purposes, the Fog is increasingly considered a key enabler for Internet-of-Things applications whose latency requirements are not compatible with a Cloud-only approach. Unlike Cloud platforms, which can elastically accommodate large numbers of requests, Fog deployments are usually dimensioned for an average traffic load and, thus, unable to handle sudden bursts of requests without violating latency guarantees. In this article, we address the problem of efficiently controlling Fog admission to guarantee application response time. We propose request-aware admission control (AC) strategies maximizing the number of Fog-handled requests by means of dynamic popularity estimation. In particular, the LRU-AC , an AC strategy based on online learning of the request popularity distribution via a Least Recently Used (LRU) filter, is introduced. We contribute an analytical model for assessing LRU-AC performance and quantifying the incurred reduction of Cloud offload cost, w.r.t. both an ideal oracle-based and a request-oblivious AC strategy. Further, we propose a feasible implementation design of LRU-AC on FPGA hardware using Aging Bloom Filters (ABF) to mimic the function of the LRU-AC, while providing a compact memory representation. The use of ABFs for LRU-AC is theoretically validated and verified through simulation. The current implementation shows a throughput of 16.7 Mpps and a processing latency of less than 3μ s while multiplying the Fog acceptance-rate by 10 in the evaluated scenario.

中文翻译：

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通过硬件加速的流行度估计在雾部署中进行有效的延迟控制

作为用于计算和存储目的的网络边缘云的扩展，Fog 越来越被认为是延迟要求与仅云方法不兼容的物联网应用程序的关键推动力。与可以弹性容纳大量请求的云平台不同，Fog 部署通常针对平均流量负载进行设计，因此无法在不违反延迟保证的情况下处理突然爆发的请求。在本文中，我们解决了有效控制雾准入以保证应用程序响应时间的问题。我们提出了请求感知准入控制（AC）策略，通过动态流行度估计来最大化雾处理请求的数量。特别是，LRU-AC，介绍了一种基于通过最近最少使用（LRU）过滤器在线学习请求流行度分布的AC策略。我们提供了一个分析模型，用于评估 LRU-AC 性能并量化云卸载成本的减少，同时采用理想的基于 Oracle 和请求忽略的 AC 策略。此外，我们提出了一种可行的 LRU-AC 在 FPGA 硬件上的实现设计，使用老化布隆过滤器 (ABF) 来模拟 LRU-AC 的功能，同时提供紧凑的内存表示。对 LRU-AC 使用 ABF 进行了理论上的验证和仿真验证。当前实现显示吞吐量为 16.7 Mpps，处理延迟小于 3μs同时在评估的场景中将雾接受率乘以 10。