Title: TopFull: An Adaptive Top-Down Overload Control for SLO-Oriented Microservices
Authors: Jinwoo Park, Jaehyeong Park, Youngmok Jung, Hwijoon Lim, Hyunho Yeo, Dongsu Han (KAIST, Moloco)
Scribe: Mingyuan Song (Xiamen University)
Introduction
TopFull addresses the critical issue of overload control in microservices architectures. Microservices have become the de facto standard for building large-scale cloud applications, but they are vulnerable to unexpected loads, which can lead to Service Level Objective (SLO) violations and even service outages. Traditional overload control methods focus on individual microservices, lacking the holistic view required to manage interdependent microservices and APIs effectively. This paper introduces TopFull, an adaptive overload control system designed to maximize throughput while meeting SLOs by leveraging global observations and implementing API-wise load control.
Key Idea and Contribution
TopFull’s primary innovation lies in its adaptive API-wise load control that optimizes the rates for each API, ensuring maximal goodput while avoiding resource wastage. The system employs reinforcement learning (RL)-based rate controllers to dynamically adjust the admitted rates of APIs based on real-time performance metrics. Additionally, TopFull utilizes a parallel control mechanism by clustering APIs and microservices, allowing for independent and concurrent load control. This approach not only resolves overloads more efficiently but also adapts to changes in workload and resource availability. The presentation did not delve into several crucial aspects in detail in the paper. The original paper provides a more in-depth discussion of the Sim2real transfer learning process used to train the RL-based rate controller, including the design principles of the simulator and the steps involved in transferring and fine-tuning the RL model in real-world applications. Additionally, the paper includes detailed algorithms, such as the specific steps for the load control execution and the method for handling APIs with branching execution paths, which were not covered in the presentation.
Evaluation
The evaluation of TopFull was conducted using various open-source microservices benchmark applications, such as Train Ticket and Online Boutique. The results demonstrated that TopFull significantly improves goodput under overload conditions, outperforming existing methods like DAGOR and Breakwater by 1.82x and 2.26x, respectively. Moreover, when integrated with Kubernetes autoscaler, TopFull achieved up to 3.91x higher goodput during traffic surges and used 57% fewer resources. This result is significant because it shows that TopFull can substantially enhance the performance and efficiency of microservices applications, addressing a critical gap in current overload control solutions.
Q: How does TopFull ensure effective load control when service replicas can migrate and the load changes dynamically?
A: TopFull addresses this challenge by implementing adaptive API-wise load control that adjusts to the current state of the microservices. It uses end-to-end performance metrics and resource utilization data to make informed decisions, ensuring that the load control remains effective despite dynamic changes in the environment. The RL-based rate controller helps rapidly adapt to these changes and maintain optimal performance.
Q: How does the accuracy of the simulated environment affect the performance of the RL-based rate controller in real-world applications?
A: To mitigate the impact of potential inaccuracies in the simulated environment, TopFull adopts a Sim2real transfer learning approach. The RL agent is initially trained in a simulated environment to learn the basic overload control policy and then fine-tuned in the real-world application to adapt to its specific characteristics. This approach significantly reduces the training time and ensures that the RL model performs well in real-world scenarios.
Personal Thoughts
TopFull presents a well-rounded solution to a pervasive problem in microservices architectures. Its ability to integrate global observations and adaptively control API rates is commendable. The use of RL-based rate controllers for dynamic adaptation adds a layer of intelligence that is often missing in traditional methods. However, the complexity of implementing such a system in a real-world environment may pose challenges, particularly in terms of training the RL models and managing the computational overhead. Future work could explore more efficient training techniques and the potential for integrating TopFull with other resource management frameworks to further enhance its scalability and robustness.
Overall, TopFull makes a significant contribution to the field of microservices overload control, and its approach opens up new avenues for research and development in this area. It will be interesting to see how this framework evolves and is adopted in practical applications.