# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 1 Introduction  
In the era of cloud computing, microservice architectures [1] have become the cornerstone of modern distributed systems such as the booming data center networks, supported by their scalability, flexibility, and rapid deployability. However, this paradigm introduces significant complexities in resource management [2], particularly under dynamic workload conditions of real-world applications such as advanced manufacturing and smart agriculture, where achieving a balance among performance, cost, and energy efficiency is critical [3]. As a core component of resource management, workload prediction [4] plays a pivotal role by enabling real-time responses to sudden workload surges and identifying long-term trends to optimize resource allocation. Yet, the intricate characteristics of microservice workloads pose substantial challenges to achieving accurate predictions. Microservice workload prediction is not only affected by the dynamics of workload evolution over time but also driven by the complex synergistic relationships among microservices. Most of the existing research draws on the framework of time series prediction to capture the temporal dynamics of the workload [5, 6] through statistical methods [7, 8, 9, 10],machine learning models [11; 12; 13; 14] and ensemble learning methods [15; 16; 17]. These methods show a strong ability to model time dynamics but give less consideration to the complex synergistic relationship between microservices. The inter-dependency and interaction characteristics of microservices often directly affect the propagation pattern and change rule of the load, which may lead to biased prediction results. Some studies address modeling the synergistic relationship between microservices [18; 19; 20]. However, they mainly focus on microservice dependency modeling and pay insufficient attention to the temporal dynamic characteristics of workload, making it difficult to fully reflect the multi-scale characteristics of workload changes. In addition, traditional microservice workload prediction methods have high requirements on data volume and data integrity. Machine learning-based methods suffer from long training time, high computing and latency costs, making their applicability limited in cold start and resource-constrained microservice environments. In contrast, matrix factorization [21], originally deployed to impute missing entries in high-dimensional data by performing dimensionality reduction and denoising after augmentation with temporal regularization, enables the direct modeling of short-term fluctuations, long-term trends, and periodic behaviors to capture the essential modes of workload evolution without a prohibitive compute overhead. However, existing matrix-factorization approaches typically ignore the rich dependency structure among microservices. To address the knowledge gap, existing works studied workload prediction and microservice dependency separately due to the lack of a joint portrayal of modeling time dynamics and microservice dependencies, making them unable to achieve a satisfactory performance. We will study how to construct a unified prediction framework of jointly modeling the temporal features of workloads and microservice dependencies so as to capture the multidimensional dynamic characteristics of workloads more accurately.