ChainsFormer: A Chain Latency-aware Resource Provisioning Approach for Microservices Cluster

TL;DR

ChainsFormer is a resource provisioning framework for microservices that uses machine learning to optimize resource allocation and improve application performance in dynamic environments.

Contribution

It introduces a novel approach combining chain analysis and reinforcement learning for resource provisioning in microservices clusters.

Findings

Reduces response time by up to 26%.

Increases processed requests per second by 8%.

Effective in real Kubernetes testbed environments.

Abstract

The trend towards transitioning from monolithic applications to microservices has been widely embraced in modern distributed systems and applications. This shift has resulted in the creation of lightweight, fine-grained, and self-contained microservices. Multiple microservices can be linked together via calls and inter-dependencies to form complex functions. One of the challenges in managing microservices is provisioning the optimal amount of resources for microservices in the chain to ensure application performance while improving resource usage efficiency. This paper presents ChainsFormer, a framework that analyzes microservice inter-dependencies to identify critical chains and nodes, and provision resources based on reinforcement learning. To analyze chains, ChainsFormer utilizes light-weight machine learning techniques to address the dynamic nature of microservice chains and workloads. For resource provisioning, a reinforcement learning approach is used that combines vertical and horizontal scaling to determine the amount of allocated resources and the number of replicates. We evaluate the effectiveness of ChainsFormer using realistic applications and traces on a real testbed based on Kubernetes. Our experimental results demonstrate that ChainsFormer can reduce response time by up to 26% and improve processed requests per second by 8% compared with state-of-the-art techniques.