LSRAM: A Lightweight Autoscaling and SLO Resource Allocation Framework for Microservices Based on Gradient Descent

ABSTRACT 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.