Latency-aware Scheduling in the Cloud-Edge Continuum

In recent years, containerized deployment models have gained favor across many domain of applications. Kubernetes, the de-facto standard for containers orchestration, can efficiently manage heterogeneous devices, but fails to adapt to possibly stringent requirements, as it only considers computing metrics for scheduling decisions. In addition, the rising prominence of distributed cloud environments, which enable the development of highly available, performant solutions, requires modifications to the default Kubernetes scheduler. To address these challenges, we introduce LAIS, 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, LAIS can either meet a specific latency constraint or schedule pods in the cluster with the lowest latency. After implementing LAIS 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.