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:在IOT系统中使用的分布式信息传输系统(DMS)需要及时和可靠的数据传播,这可以通过可配置参数实现。然而,高维配置空间使用户难以找到最佳选择,在满足特定潜伏限制的同时,最大限度地实现应用吞吐量。现有的自动软件剖析方法有局限性,例如,仅优化吞吐量,不保证明显的潜伏限制,并由于参数范围分散而导致本地选择。为了克服这些挑战,建议采用名为DMSConfig的新型配置调节系统,该系统使用机器学习和深层强化学习。DMSConfig与数据驱动环境预测模型互动,避免与生产环境进行在线互动的成本。DMSConfig采用深度确定性政策梯度(DPG)方法和定制奖励机制,根据预测的DMS系统状态和性能做出配置决定。实验显示,DMSConfig的表现比默认的配置要好得多,高度适应于不同延迟边界的调控调要求,并且具有与常用参数调整工具相似的特征。