Armada: A Robust Latency-Sensitive Edge Cloud in Heterogeneous Edge-Dense Environments

TL;DR

Armada is a new dense edge cloud infrastructure that leverages dedicated and volunteer resources to support latency-sensitive applications at scale in heterogeneous environments.

Contribution

It introduces a lightweight architecture with optimization techniques like edge selection, auto-scaling, and load balancing for scalable, low-latency edge computing.

Findings

Effective deployment of real-time object detection and face recognition at scale.

Demonstrated low latency and scalability in volunteer and emulated environments.

Improved support for geo-distributed users in heterogeneous edge settings.

Abstract

Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading latency-sensitive and computation-intensive workloads to nearby edge servers. However, supporting edge application users at scale in wide-area environments poses challenges due to limited point-of-presence edge sites and constrained elasticity. In this paper, we introduce Armada: a densely-distributed edge cloud infrastructure that explores the use of dedicated and volunteer resources to serve geo-distributed users in heterogeneous environments. We describe the lightweight Armada architecture and optimization techniques including performance-aware edge selection, auto-scaling and load balancing on the edge, fault tolerance, and in-situ data access. We evaluate Armada in both real-world volunteer environments and emulated platforms to show how common edge applications, namely real-time object detection and face recognition, can be easily deployed on Armada serving distributed users at scale with low latency.