Tango: Harmonious Management and Scheduling for Mixed Services Co-located among Distributed Edge-Clouds

Co-locating Latency-Critical (LC) and Best-Effort (BE) services in edge-clouds is expected to enhance resource utilization. However, this mixed deployment encounters unique challenges. Edge-clouds are heterogeneous, distributed, and resource-constrained, leading to intense competition for edge resources, making it challenging to balance fluctuating co-located workloads. Previous works in cloud datacenters are no longer applicable since they do not consider the unique nature of edges. Although very few works explicitly provide specific schemes for edge workload co-location, these solutions fail to address the major challenges simultaneously. In this paper, we propose Tango, a harmonious management and scheduling framework for Kubernetes-based edge-cloud systems with mixed services, to address these challenges. Tango incorporates novel components and mechanisms for elastic resource allocation and two traffic scheduling algorithms that effectively manage distributed edge resources. Tango demonstrates harmony not only in the compatible mixed services it supports, but also in the collaborative solutions that complement each other. Based on a backwards compatible design for Kubernetes, Tango enhances Kubernetes with automatic scaling and traffic scheduling capabilities. Experiments on large-scale hybrid edge-clouds, driven by real workload traces, show that Tango improves the system resource utilization by 36.9%, QoS-guarantee satisfaction rate by 11.3%, and throughput by 47.6%, compared to state-of-the-art approaches.