Cross-Cluster Networking to Support Extended Reality Services

TL;DR

The paper addresses XR workloads' need for ultra-low latency and high bandwidth across geo-distributed clusters, highlighting Kubernetes' cross-cluster limitations. It proposes a two-framework solution—Cluster API for multi-cluster management and Liqo for cross-cluster interconnectivity—and validates it with cross-cluster video streaming experiments across two clusters. Key contributions include a comparative analysis of interconnectivity options, the selection of Liqo for cross-cluster networking, and empirical evaluation demonstrating feasible latency and resource overhead. The work provides a vendor-neutral, scalable approach for deploying XR services across cloud-edge environments with dynamic workload scheduling capabilities.

Abstract

Extented Reality (XR) refers to a class of contemporary services that are intertwined with a plethora of rather demanding Quality of Service (QoS) and functional requirements. Despite Kubernetes being the de-facto standard in terms of deploying and managing contemporary containerized microservices, it lacks adequate support for cross-cluster networking, hindering service-to-service communication across diverse cloud domains. Although there are tools that may be leveraged alongside Kubernetes in order to establish multi-cluster deployments, each one of them comes with its drawbacks and limitations. The purpose of this article is to explore the various potential technologies that may facilitate multi-cluster deployments and to propose how they may be leveraged to provide a cross-cluster connectivity solution that caters to the intricacies of XR services. The proposed solution is based on the use of two open source frameworks, namely Cluster API for multi-cluster management, and Liqo for multi-cluster interconnectivity. The efficiency of this approach is evaluated in the context of two experiments. This work is the first attempt at proposing a solution for supporting multi-cluster deployments in a manner that is aligned with the requirements of XR services

Figures (6)

• Figure 1: Multi-Cluster Example Use Case.
• Figure 2: Multi-Cluster Interconnectivity Taxonomy.
• Figure 3: The implementation of the proposed solution in the frame of a cross-cluster video streaming use-case that includes two clusters.
• Figure 4: Cluster API deployment time by Kubernetes distribution and cluster size.
• Figure 5: Latency between streaming and view times for each scenario.