COIN: A Container Workload Prediction Model Focusing on Common and Individual Changes in Workloads
Zhijun Ding, Binbin Feng, Changjun Jiang
Abstract
Recently, containers have become the primary deployment form for cloud applications. Predicting container workload accurately is critical to ensure the quality of service (QoS) and cost-efficiency of the applications and meet service level agreements (SLAs) with users. However, facing multiple challenges, including model unavailability due to insufficient data, model maladaptation due to dynamic workload changes, and model non-generalization due to changeable workload patterns in container workload prediction, existing methods have not yet provided a united and effective solution. To this end, we propose a novel integrated forecasting model named COIN that combines COmmon and INdividual changes in container workloads to ensure the availability, adaptivity, and generality of the prediction model based on transfer learning and online learning. Besides, we present a container similarity calculation algorithm for real cloud scenarios, which combines the static and dynamic information of containers and comprehensively depicts the similarity between containers. Through experiments based on two public datasets, the COIN model achieves a higher accuracy than existing state-of-the-art solutions, demonstrating the effectiveness and robustness of our proposed model, which provides a new solution to container workload prediction.