Minimizing Resource Allocation for Cloud-Native Microservices

Erdei, Roland and Toka, László (2023) Minimizing Resource Allocation for Cloud-Native Microservices. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 31. No.-35. ISSN 1064-7570 (print); 1573-7705 (online)

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Abstract

With the continuous progress of cloud computing, many microservices and complex multi-component applications arise for which resource planning is a great challenge. For example, when it comes to data-intensive cloud-native applications, the tenant might be eager to provision cloud resources in an economical manner while ensuring that the application performance meets the requirements in terms of data throughput. However, due to the complexity of the interplay between the building blocks, adequately setting resource limits of the components separately for various data rates is nearly impossible. In this paper, we propose a comprehensive approach that consists of measuring the resource footprint and data throughput performance of such a microservices-based application, analyzing the measurement results by data mining techniques, and finally formulating an optimization problem that aims to minimize the allocated resources given the performance constraints. We illustrate the benefits of the proposed approach on Cortex, an extension to Prometheus for storing monitored metrics data. The data-intensive nature of this illustrative example stems from real-time monitoring of metrics exposed by a multitude of applications running in a data center and the continuous analysis performed on the collected data that can be fetched from Cortex. We present Cortex’s performance vs resource footprint trade-off, and then we build regression models to predict the microservices’ resource consumption and draw a mathematical programming formulation to optimize the most important configuration parameters. Our most important finding is the linear relationship between resource consumption and application performance, which allows for applying linear regression and linear programming models. After the optimization, we compare our results to Cortex’s recommendation, leading to a CPU reservation reduced by 50–80%.

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