Kubernetes Cluster
Kubernetes is a tool that facilitates rapid deployment of software. Unfortunately, configuring Kubernetes is prone to errors.
Configuration defects are not uncommon and can result in serious consequences. This paper reports an empirical study about
configuration defects in Kubernetes with the goal of helping practitioners detect and prevent these defects. We study 719 defects that
we extract from 2,260 Kubernetes configuration scripts using open source repositories. Using qualitative analysis, we identify 15
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Performance models identified at run-time can be used by self-adaptive software systems to execute decisions on a cloud environment. These performance models are built by measuring the control inputs, disturbances, and outputs of the controlled system. These models have been shown to accurately interpolate for data already seen by the model identification method. However, automation in cloud operations can push the environment into operational regions the system has not seen, thus the performance model may not accurately extrapolate into unseen regions.
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The benchmarking dataset, GenAI on the Edge, contains performance metrics from evaluating Large Language Models (LLMs) on edge devices, utilizing a distributed testbed of Raspberry Pi devices orchestrated by Kubernetes (K3s). It includes performance data collected from multiple runs of prompt-based evaluations with various LLMs, leveraging Prometheus and the Llama.cpp framework. The dataset captures key metrics such as resource utilization, token generation rates/throughput, and detailed inference timing for stages such as Sample, Prefill, and Decode.
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