Multi-Step Edge Cloud Load Prediction by Analyzing Behavior of Workload Groups

Xiaojun Wu, Wenxing Huang, Sheng Yuan, Jiahao Mi

Abstract: With the flourishing development of the Internet of Things (IoT) era, Edge Computing (EC) technology has garnered significant attention along with advancements in communication and IoT technologies. Effectively harnessing the computational resources in the edge environment has become a paramount concern. Accurate workload prediction is considered fundamental to optimizing the utilization of limited edge resources. However, most existing edge cloud load prediction approaches overlook the correlations among edge sites. Moreover, for cloud-native applications, user requests are typically handled by multiple containers. By evolving the work behavior of workload groups, it is possible to achieve a shift from focusing on individual containers to the collective, resulting in higher predictive accuracy. In this paper, the behavior of workload groups is analyzed through the reference to both static and dynamic container information. Leveraging the input data constructed based on the behavior of workload groups, an improved Sample Convolution and Interaction Network (SCINet) is employed for early multi-step prediction of edge container loads. Experimental validation on an edge cloud load dataset demonstrates the effectiveness of the proposed approach. Experimental results show that the proposed method can effectively improve the accuracy of edge cloud load prediction.