基于决策树的智能轻量级I/O推荐系统

Yiting Huang, Zhiwen Wang, Yuguo Li, Junlang Huang, Dingding Li, Yong Tang, Deze Zeng
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引用次数: 0

摘要

系统的基本I/O操作可以分为两种不同的模式:同步(sync) I/O和异步(async) I/O,这两种模式的性能根据系统状态、工作负载和存储设备的不同而不同。适当地应用I/O模式对系统性能至关重要。然而,服务器中各种应用程序的I/O访问是不稳定和不规则的,尤其是在云中。因此,这可能缺乏灵活和自适应的I/O模式,导致I/O性能不够理想。为了解决这一问题,本文提出了智能I/O模式推荐系统IObrain,该系统可以根据应用需求和系统状态动态自适应地选择合适的I/O模式。IObrain首先用决策树训练轻量级推荐模型。然后,在存储引擎中插入一个查询钩子,以拦截来自上层应用程序的读/写操作。这样,IObrain在执行读/写操作之前,首先查询推荐模型,以找到正确的I/O模式。此外,还提出了推理缓存和gRPC桥接两种技术来降低固有的查询延迟。我们实际实现了IObrain,并在原型系统的基础上验证了IObrain的优势。实验结果表明,与现有方法相比,IObrain的I/O性能提高了1.33倍,运行成本较低。
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IObrain: An Intelligent Lightweight I/O Recommendation System based on Decision Tree
The basic I/O operations of a system can be categorized as two distinct modes: synchronous (sync) I/O and asynchronous (async) I/O, whose performance varies on the system statues, workloads and storage devices. Appropriately applying I/O modes is critical to the system performance. However, the I/O access of diverse applications in a server, especially in a cloud, is volatile and irregular. As a result, this can lack a flexible and adaptive I/O modes, leading to the sub-optimal I/O performance. To tackle this problem, in this paper, we propose IObrain, an intelligent I/O mode recommendation system, which can adopt the appropriate I/O mode in a dynamic and self-adaptive manner according to both application needs and system statuses. IObrain first trains a lightweight recommendation model with decision tree. Then, a query hook is interposed into the storage engine to intercept the read/write operations from upper application. In this way, IObrain queries the recommendation model first before executing a read/write operation to find the right I/O mode. In addition, two techniques, called inference cache and gRPC bridge, are proposed to reduce the inherent query latency. We practically implement IObrain and verify the advantage of IObrain based on the prototype system. The experimental results show that, compared to existing approach, IObrain improves the I/O performance by up to 1.33× with mild running costs.
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