NeiLatS: Neighbor-Aware Latency-Sensitive Application Scheduling in Heterogeneous Cloud-Edge Environment

Huadong Li, Hui Liu, Changyuan Liu, Aoqi Chen, Zhaocheng Niu, Junzhao Du

Abstract: The deployment and management of distributed latency-sensitive applications pose significant challenges due to the volatile nature of modern Internet of Things (IoT) edge networks, constrained resources for heterogeneous edge devices, and the increasingly demanding user requirements for service level agreement (SLA) and quality of service (QoS). To address these challenges, Kubernetes (k8s) orchestration platform has emerged, with previous research using heuristics to make scheduling decisions quickly or leveraging artificial intelligence (AI) to adapt to changing circumstances. However, the former often struggle to adapt to highly dynamic and resource-competitive edge environments, while the latter’s long decision latency negatively impacts QoS. In this paper, we propose NeiLatS, a heterogeneous cloud-edge cluster scheduling system that addresses these issues. NeiLatS can monitor real-time resource and communication link conditions and make the scheduler latency-aware and location-aware. Additionally, we innovatively propose an efficacy coefficient method with tolerance factor ε (ε -ECM), which fully accounts for multi-resource load balancing, network stability, and future communication link conditions to resolve the conflict between resource utilization balance and QoS. We test NeiLatS on physical clusters and compare it with five different algorithms. Our evaluation results show a 4.59%-16% improvement in resource load balancing degree, a 41.95%-82.85% reduction in SLA violation rates, and a 1-5x improvement in QoS, which greatly demonstrate the effectiveness of our proposed method.

External IDs:dblp:conf/icpp/Li0LCND23