Load balancing has been a fundamental building block of cloud and, more recently, edge computing environments. At the same time, in edge computing environments, prior research has highlighted that applications operate on similar (correlated) data. Based on this observation, prior research has advocated for the direction of "computation reuse", where the results of previously executed computational tasks are stored at the edge and are reused (if possible) to satisfy incoming tasks with similar input data, instead of executing incoming tasks from scratch. Both load balancing and computation reuse are critical to the deployment of scalable edge computing environments, yet they are contradictory in nature. In this paper, we propose the Deduplicator, a middlebox that aims to facilitate both load balancing and computation reuse at the edge. The Deduplicator features mechanisms to identify and deduplicate similar tasks offloaded by user devices, collect information about the usage of edge servers' resources, manage the addition of new edge servers and the failures of existing edge servers, and ultimately balance the load imposed on edge servers. Our evaluation results demonstrate that the Deduplicator achieves up to 20% higher percentages of computation reuse compared to several other load balancing approaches, while also effectively balancing the distribution of tasks among edge servers at line rate.

翻译：负载均衡一直是云计算及新兴边缘计算环境的基础性构建模块。同时，在边缘计算环境中，已有研究指出应用程序处理的数据具有相似性（关联性）。基于这一发现，先前研究提出了“计算复用”方向，即将已执行计算任务的结果存储于边缘端，并在可能的情况下将其复用于输入数据相似的新任务，而非从头执行这些任务。负载均衡与计算复用对可扩展边缘计算环境的部署均至关重要，但二者本质上相互矛盾。本文提出Deduplicator中间盒，旨在促进边缘端负载均衡与计算复用的协同。该中间盒具备以下机制：识别并去重用户设备卸载的相似任务、收集边缘服务器资源使用信息、管理新边缘服务器的添加及现有服务器的故障处理，并最终均衡边缘服务器的负载。评估结果表明，与多种其他负载均衡方法相比，Deduplicator在实现高达20%的计算复用率提升的同时，还能以线速有效均衡边缘服务器间的任务分配。