Edge Workloads Monitoring and Failover: a StarlingX-Based Testbed Implementation and Measurement Study

Abstract

With the ever-growing amount of time-critical, compute-intensive, and private IoT applications, the need for High Availability (HA) Edge Clouds becomes indispensable. Realizing HA Edge Clouds is inherently challenging due to the geographically-dispersed hierarchy of the Distributed Cloud Infrastructure (DCI). For example, frequent isolation between the central Cloud and Edge Clouds due to networking instability necessitates some autonomous operations at the Edge Clouds. Furthermore, because Edge Clouds have fewer resources than central Clouds, configuring the Edge functions (i.e., control, compute, and storage) in HA clusters will undoubtedly reduce downtime. However, it will limit the Edge scalability. To that end, StarlingX is developing an HA-protected and scalable DCI virtualization platform based on the open-source ecosystem, focusing on low-touch management of Edge Clouds. StarlingX provides a fault management service that realizes DCI-wide alarming and logging capabilities, allowing for rapid response to virtualized infrastructure events. Recently, the IETF Network Working Group proposed that monitoring both the DCI and the Edge workloads (software containers) is critical for an Edge Computing Platform to maintain HA IoT application deployment. Indeed, the possibility of the infrastructure remaining stable and healthy while the workloads suffer a fatal failure simultaneously necessitates failover functionality that monitors both the infrastructure and the Edge workloads. In this paper, we first propose a dynamic failover functionality that centrally monitors Edge workloads to recover from deployment or Edge node failures, motivated by the IETF direction. Second, we experimentally optimize the failover functionality for monitoring a microservice-architected IoT application deployed on a StarlingX-based DCI testbed to collect temperature sensor readings from Raspberry Pis. Regardless of how quickly the Edge workload health checks are collected, the recorded failover measurements reveal that the recovery time will not drop below a predetermined level controlled by Edge resources and network speed. Furthermore, reducing the statistics collection timeout reduces the recovery time of an Edge node failure. When the timeout value is less than the minimum achievable recovery time, false-positive failures (FPFs) can occur. Third, to supplement the StarlingX fault management service, we provide a modular implementation of the proposed failover functionality. Finally, we present the first-ever introduction of the StarlingX platform’s software stack to promote its use in academic research.

Keywords

• Distributed cloud infrastructure
• edge computing
• failover
• IoT
• Kubernetes
• microservice architecture