This paper investigates an edge computing system where requests are processed by a set of replicated edge servers. We investigate a class of applications where similar queries produce identical results. To reduce processing overhead on the edge servers we store the results of previous computations and return them when new queries are sufficiently similar to earlier ones that produced the results, avoiding the necessity of processing every new query. We implement a similarity-based data classification system, which we evaluate based on real-world datasets of images and voice queries. We evaluate a range of orchestration strategies to distribute queries and cached results between edge nodes and show that the throughput of queries over a system of distributed edge nodes can be increased by 25-33%, increasing its capacity for higher workloads.

翻译：本文研究一种由一组复制的边缘服务器处理请求的边缘计算系统。我们研究一类具有相似查询产生相同结果特性的应用程序。为减少边缘服务器的处理开销，我们存储先前计算的结果，并在新查询与产生这些结果的早期查询足够相似时直接返回存储结果，从而避免处理每个新查询的必要性。我们实现了一个基于相似度的数据分类系统，并使用真实世界的图像和语音查询数据集对其进行评估。我们评估了一系列编排策略以在边缘节点间分配查询和缓存结果，结果表明分布式边缘节点系统的查询吞吐量可提升25-33%，从而增强其处理更高工作负载的能力。