Latency-aware task scheduling in the cloud continuum

In recent years, containerized deployment models have gained popularity across various application domains. Kubernetes, the de-facto standard for container orchestration, can efficiently manage heterogeneous devices. However, it struggles to adapt to potentially stringent requirements as it only considers computing metrics for scheduling decisions. Moreover, the rising prominence of distributed cloud environments, which enable the development of highly available and performant solutions, necessitates modifications to the default Kubernetes scheduler. To address these challenges, we introduce a multi-cluster Kubernetes scheduler optimized for end-to-end latency measurements to enhance user Quality of Experience (QoE). Unlike existing approaches, we define a geographically distributed environment and deploy a solution that satisfies user-specified intents in terms of latency. Depending on user needs, our scheduler can either meet a specific latency constraint or schedule pods in the cluster with the lowest latency. After implementing our scheduler in a multi-cluster environment, we found it highly effective in accommodating a range of user intents, outperforming the default Kubernetes scheduler in this regard.