Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML

Edge computing offers significant advantages for real-time data processing tasks, such as object recognition, by reducing network latency and bandwidth usage. However, edge environments are susceptible to various types of fault. A remediator is an automated software component designed to adjust the configuration parameters of a software service dynamically. Its primary function is to maintain the service's operational state within predefined Service Level Objectives by applying corrective actions in response to deviations from these objectives. Remediators can be implemented based on the Kubernetes container orchestration tool by implementing remediation strategies such as rescheduling or adjusting application parameters. However, currently, there is no method to compare these remediation strategies fairly. This paper introduces Ecoscape, a comprehensive benchmark designed to evaluate the performance of remediation strategies in fault-prone environments. Using Chaos Engineering techniques, Ecoscape simulates realistic fault scenarios and provides a quantifiable score to assess the efficacy of different remediation approaches. In addition, it is configurable to support domainspecific Service Level Objectives. We demonstrate the capabilities of Ecoscape in edge machine learning inference, offering a clear framework to optimize fault tolerance in these systems without needing a physical edge testbed.