The Distributed Messaging Systems (DMSs) used in IoT systems require timely and reliable data dissemination, which can be achieved through configurable parameters. However, the high-dimensional configuration space makes it difficult for users to find the best options that maximize application throughput while meeting specific latency constraints. Existing approaches to automatic software profiling have limitations, such as only optimizing throughput, not guaranteeing explicit latency limitations, and resulting in local optima due to discretizing parameter ranges. To overcome these challenges, a novel configuration tuning system called DMSConfig is proposed that uses machine learning and deep reinforcement learning. DMSConfig interacts with a data-driven environment prediction model, avoiding the cost of online interactions with the production environment. DMSConfig employs the deep deterministic policy gradient (DDPG) method and a custom reward mechanism to make configuration decisions based on predicted DMS states and performance. Experiments show that DMSConfig performs significantly better than the default configuration, is highly adaptive to serve tuning requests with different latency boundaries, and has similar throughput to prevalent parameter tuning tools with fewer latency violations.

翻译：分布式消息系统（DMS）在物联网系统中需要及时可靠的数据分发，这可通过可配置参数实现。然而，高维度的配置空间使用户难以找到既能最大化应用吞吐量又能满足特定延迟约束的最佳配置。现有自动化软件分析的方法存在局限性，例如仅优化吞吐量、无法保证显式延迟限制、以及因参数范围离散化而导致局部最优等问题。为克服这些挑战，本文提出了一种名为DMSConfig的新型配置调优系统，该系统融合了机器学习和深度强化学习技术。DMSConfig通过与数据驱动的环境预测模型交互，避免了与生产环境在线交互的成本。该系统采用深度确定性策略梯度（DDPG）方法和自定义奖励机制，基于预测的DMS状态和性能进行配置决策。实验表明，DMSConfig的性能显著优于默认配置，能够高度自适应地处理不同延迟边界的调优请求，且在延迟违规更少的情况下，其吞吐量与主流参数调优工具相当。