Tango: Harmonious Optimization for Mixed Services in Kubernetes-Based Edge Clouds

Shihao Shen, Yicheng Feng, Mengwei Xu, Yuanming Ren, Xiaofei Wang, Victor C. M. Leung, Wenyu Wang

Abstract: Deploying Latency-Critical (LC) services and Best-Effort (BE) services together is expected to improve resource utilization in edge clouds. However, co-locating LC and BE services on edge clouds presents unique challenges. Unlike cloud datacenters, edge clouds are heterogeneous, resource-constrained, and geographically distributed, leading to fiercer competition for resources and greater difficulty in balancing fluctuating co-located workloads. Due to the lack of consideration for the characteristics of edge environments, previous solutions designed for cloud datacenters are no longer applicable. To address these challenges, we introduce Tango, a harmonious scheduling framework for Kubernetes-based edge cloud systems with mixed services. Tango incorporates novel components and mechanisms for elastic resource allocation on the edge, as well as two traffic scheduling algorithms that efficiently manage distributed edge resources. Tango fosters harmony not only by supporting compatible mixed services but also by offering collaborative solutions that complement each other. Based on a non-intrusive design for Kubernetes, Tango further enhances it with automatic scaling and traffic scheduling capabilities. Compared to state-of-the-art approaches, experiments on large-scale hybrid edge clouds, driven by real workload traces, show that Tango improves system resource utilization by 36.9%, QoS-guarantee satisfaction rate by 11.3%, and throughput by 47.6%.