Practice of Alibaba Cloud on Elastic Resource Provisioning for Large-scale Microservices Cluster

TL;DR

This paper discusses Alibaba Cloud's strategies for efficiently provisioning resources in large-scale microservice clusters, focusing on optimizing resource utilization and maintaining latency requirements through advanced algorithms.

Contribution

It introduces Alibaba's resource provisioning framework and proposes enhanced algorithms that improve resource usage by 10-15% while ensuring latency for microservices.

Findings

Resource usage improved by 10-15%

Enhanced algorithms balance proactive and reactive scheduling

Maintains latency requirements in large-scale clusters

Abstract

Cloud-native architecture is becoming increasingly crucial for today's cloud computing environments due to the need for speed and flexibility in developing applications. It utilizes microservice technology to break down traditional monolithic applications into light-weight and self-contained microservice components. However, as microservices grow in scale and have dynamic inter-dependencies, they also pose new challenges in resource provisioning that cannot be fully addressed by traditional resource scheduling approaches. The various microservices with different resource needs and latency requirements can create complex calling chains, making it difficult to provide fine-grained and accurate resource allocation to each component while maintaining the overall quality of service in the chain. In this work, we aim to address the research problem on how to efficiently provision resources for the growing scale of microservice platform and ensure the performance of latency-critical microservices. To address the problem, we present in-depth analyses of Alibaba's microservice cluster and propose optimized resource provisioning algorithms to enhance resource utilization while ensuring the latency requirement. First, we analyze the distinct features of microservices in Alibaba's cluster compared to traditional applications. Then we present Alibaba's resource capacity provisioning workflow and framework to address challenges in resource provisioning for large-scale and latency-critical microservice clusters. Finally, we propose enhanced resource provisioning algorithms over Alibaba's current practice by making both proactive and reactive scheduling decisions based on different workloads patterns, which can improve resource usage by 10-15% in Alibaba's clusters, while maintaining the necessary latency for microservices.