Long-term Cloud Workload Prediction (LCWP) is critical for efficient resource provisioning and cost optimization in cloud computing environments. However, traditional prediction approaches compress complex workload patterns into a single token, leading to catastrophic forgetting of historical variations. Additionally, they lack the capability to capture distinct periodic patterns (e.g., minutely and hourly cycles) and bursty trends. To overcome these limitations, we propose the Multi-period Augmented LSTM (MUPA), a comprehensive encoder-decoder model featuring explicit cross-period connections designed to maximize utilization of inherent periodic information. MUPA architecture integrates two novel LSTM variants as core components: Multi-input LSTM, which aggregates latent representations across time steps to establish global workload dynamics understanding, and Broadened LSTM, which enhances memory mechanisms by progressively expanding the cell state’s value range to learn long-term dependencies. Extensive experiments on real-world cloud workload datasets demonstrate MUPA’s superior effectiveness for workload prediction tasks.