Abstract
Containerization, and container-based application orchestration and management - primarily using Kubernetes - are rapidly gaining popularity. Resilience in such environments is an increasingly critical aspect, especially in terms of fault recovery, as containerization-based microservices are becoming the de facto standard for soft real-time and cyber-physical workloads in edge computing.
The Worst Case Execution Time (WCET) of platform-supported recovery mechanisms is crucial for designing the resilience of applications, influencing, e.g., dimensioning and the design and parameterization of recovery policies.
However, due to the complexity of the underlying phenomena, establishing such WCET characteristics is generally feasible only empirically, carrying the risk of under- or overapproximating recovery time outliers, which, in turn, are crucial for assurance design.
Measurement-Based Probabilistic Timing Analysis (MBPTA) aims at estimating the Worst-Case Execution Time (WCET) based on measurements. A technique in the MBPTA “toolbox”, Extreme Value Analysis (EVA) is a statistical paradigm dealing with approximating the properties of extremely deviant values.
This paper demonstrates that container restarts, a key platform mechanism in Kubernetes, exhibits rare extreme execution time values. We also demonstrate that characterizing these rare values with EVA can lead to at least as good or better approximations as classic distribution fitting - and for the practice importantly, without distribution assumptions.
The results reported on in this paper partially rely on previous results of the EFOP-3.6.2-16-2017-00013 national project at the Budapest University of Technology and Economics and a joint research project with Ericsson.
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Bozóki, S. et al. (2020). Application of Extreme Value Analysis for Characterizing the Execution Time of Resilience Supporting Mechanisms in Kubernetes. In: Bernardi, S., et al. Dependable Computing - EDCC 2020 Workshops. EDCC 2020. Communications in Computer and Information Science, vol 1279. Springer, Cham. https://doi.org/10.1007/978-3-030-58462-7_15
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