  Analytical performance models are very effective in ensuring the quality of
service and cost of service deployment remain desirable under different
conditions and workloads. While various analytical performance models have been
proposed for previous paradigms in cloud computing, serverless computing lacks
such models that can provide developers with performance guarantees. Besides,
most serverless computing platforms still require developers' input to specify
the configuration for their deployment that could affect both the performance
and cost of their deployment, without providing them with any direct and
immediate feedback. In previous studies, we built such performance models for
steady-state and transient analysis of scale-per-request serverless computing
platforms (e.g., AWS Lambda, Azure Functions, Google Cloud Functions) that
could give developers immediate feedback about the quality of service and cost
of their deployments. In this work, we aim to develop analytical performance
models for the latest trend in serverless computing platforms that use
concurrency value and the rate of requests per second for autoscaling
decisions. Examples of such serverless computing platforms are Knative and
Google Cloud Run (a managed Knative service by Google). The proposed
performance model can help developers and providers predict the performance and
cost of deployments with different configurations which could help them tune
the configuration toward the best outcome. We validate the applicability and
accuracy of the proposed performance model by extensive real-world
experimentation on Knative and show that our performance model is able to
accurately predict the steady-state characteristics of a given workload with
minimal amount of data collection.
