ProxyDWRR: A Dynamic Load Balancing Approach for Heterogeneous-CPU Kubernetes Clusters

Abstract

Edge computing is booming as a promising paradigm to push the service and computation resources from the cloud to the edge of network. As the de-facto standard for container orchestration, Kubernetes is more and more widely used not only in cloud computing but also in edge computing. However, Kubernetes is designed for homogenous cloud data centers, and it does not take into account heterogeneous scenarios, which is ubiquitous is the edge. This will lead to load imbalance among containers with its default rough load balancing mechanism. To deal with this problem, we firstly propose a Dynamically Weighted Random Routing (DWRR) algorithm based on the default random algorithm in Kubernetes. Besides, we design and implement ProxyDWRR, a load balancing plugin for the Kubernetes cluster with heterogeneous CPU. It is fully compatible with the existing load balancing mechanism in Kubernetes. We validated our solution based on a cloud-native microservices application. The experimental results show that ProxyDWRR can effectively balance the load between containers in clusters with heterogeneous CPU. In our experiments, DWRR can improve the CPU utilization of the containers by about 25% and the throughput of the application by about 22.6% compared to the default load balancing algorithms, which enables the cluster to evacuate bursty load more effectively.