Predicting Resource Consumption of Kubernetes Container Systems using Resource Models

• URL: http://arxiv.org/abs/2305.07651v1
• Date: Fri, 12 May 2023 17:59:01 GMT
• Title: Predicting Resource Consumption of Kubernetes Container Systems using Resource Models
• Authors: Gianluca Turin, Andrea Borgarelli, Simone Donetti, Ferruccio Damiani, Einar Broch Johnsen, Silvia Lizeth Tapia Tarifa
• Abstract summary: This paper considers how to derive resource models for cloud systems empirically. We do so based on models of deployed services in a formal language with explicit adherence to CPU and memory resources. We report on leveraging data collected empirically from small deployments to simulate the execution of higher intensity scenarios on larger deployments.
• Score: 3.138731415322007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cloud computing has radically changed the way organisations operate their software by allowing them to achieve high availability of services at affordable cost. Containerized microservices is an enabling technology for this change, and advanced container orchestration platforms such as Kubernetes are used for service management. Despite the flourishing ecosystem of monitoring tools for such orchestration platforms, service management is still mainly a manual effort. The modeling of cloud computing systems is an essential step towards automatic management, but the modeling of cloud systems of such complexity remains challenging and, as yet, unaddressed. In fact modeling resource consumption will be a key to comparing the outcome of possible deployment scenarios. This paper considers how to derive resource models for cloud systems empirically. We do so based on models of deployed services in a formal modeling language with explicit CPU and memory resources; once the adherence to the real system is good enough, formal properties can be verified in the model. Targeting a likely microservices application, we present a model of Kubernetes developed in Real-Time ABS. We report on leveraging data collected empirically from small deployments to simulate the execution of higher intensity scenarios on larger deployments. We discuss the challenges and limitations that arise from this approach, and identify constraints under which we obtain satisfactory accuracy.

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