Anveshak: Placing Edge Servers In The Wild

Posted 2021-01-04 alpaca

Anveshak：在野外放置边缘服务器

本文为SIGCOMM 2018 Workshop (Mobile Edge Communications, MECOMM)论文。

笔者翻译了该论文。由于时间仓促，且笔者英文能力有限，错误之处在所难免；欢迎读者批评指正。

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本文作者包含4位，University of Helsinki, Finland的Nitinder Mohan，Aleksandr Zavodovski，Pengyuan Zhou和Jussi Kangasharju

ABSTRACT （摘要）

Edge computing provides an attractive platform for bringing data and processing closer to users in networked environments. Several edge proposals aim to place the edge servers at a couple hop distance from the client to ensure lowest possible compute and network delay. An attractive edge server placement is to co-locate it with existing (cellular) base stations to avoid additional infrastructure establishment costs. However, determining the exact locations for edge servers is an important question that must be resolved for optimal placement. In this paper, we present Anveshak, a framework that solves the problem of placing edge servers in a geographical topology and provides the optimal solution for edge providers. Our proposed solution considers both end-user application requirements as well as deployment and operating costs incurred by edge platform providers. The placement optimization metric of Anveshak considers the request pattern of users and existing user-established edge servers. In our evaluation based on real datasets, we show that Anveshak achieves 67% increase in user satisfaction while maintaining high server utilization. 

边缘计算为网络环境中数据和处理接近用户提供了具有吸引力的平台。一些边缘提案的目标是将边缘服务器放置到距离客户端几跳远的位置，以保证最低的可能计算和网路延迟。一种具有吸引力的边缘服务器放置是将其和现有的（蜂窝）基站放置在一起，以避免额外的基础设施建设开销。然而，确定边缘服务器的准确位置是一个重要的问题，其解决必须是最优放置。本文中，我们给出Anveshak框架，该框架在地理拓扑中解决边缘服务器放置问题，并为边缘提供商提供最优方案。我们提出的方案同时考虑了终端用户应用需求和边缘平台提供商的部署和运维成本。Anveshak的放置优化指标考虑了用户和现有用户建立的边缘服务器的请求模式。基于真实数据集的评估表明Anveshak在保持较高服务器利用率的同时取得67%的用户满意度增长。

1 INTRODUCTION （引言）

Novel applications, such as the Internet of Things (IoT) and augmented and virtual reality, have exponentially increased the amount of data generated and transported over the network. To mitigate the response time and handle large-scale data analysis closer to the users and data generators, the researchers have proposed edge clouds. As the name suggests, edge cloud is a consolidation of compute servers deployed very close to end user with limited compute, storage and network capability [1, 12, 22]. The central objective of edge clouds is to ensure low network delays for latency-critical applications such as autonomous driving, drones, augmented reality, etc. [10]. Such a requirement can be fulflled by exploiting the physical proximity between the edge server and the client.