Objective: The microservices architecture has become a dominant paradigm in cloud computing due to its advantages in development, deployment, modularity, and scalability. Ensuring Quality of Service (QoS) through efficient Service Level Objective (SLO) resource allocation is a critical challenge. Current frameworks for microservice autoscaling based on SLOs often rely on heavy and complex models that are time-consuming and resource-intensive, making them unsuitable for rapidly changing environments and highly dynamic workloads. Methods: This study proposes LSRAM (Lightweight SLO Resource Allocation Management), a novel framework designed to overcome the limitations of existing SLO-based autoscaling methods. LSRAM operates in two stages:. 1). Lightweight SLO Resource Allocation Model: Computes optimal SLO resource allocation for each microservice using a gradient descent method, ensuring rapid computation and minimal computational overhead. 2). SLO Resource Update Model: Adapts resource allocation dynamically in response to changes in the cluster environment, such as varying loads and application types, without requiring extensive retraining. Results: LSRAM effectively addresses scenarios involving bursty traffic and fluctuating workloads. Compared to state-of-the-art SLO allocation frameworks, LSRAM achieves the following:. 1). Reduces resource usage by 17%. 2). Maintains QoS guarantees for users, even under dynamic conditions. 3). Demonstrates faster adaptability to changes in the system environment due to its lightweight design. Conclusion: LSRAM offers a scalable, efficient, and adaptive solution for SLO-based resource allocation in microservices architectures. By reducing resource usage while maintaining QoS, it provides a robust framework for managing dynamic and unpredictable workloads in cloud environments. Its lightweight design ensures practical applicability and superior performance compared to traditional, resource-intensive methods.