Optimizing in the Dark: Learning an Optimal Solution through a Simple Request Interface
Optimizing in the Dark: Learning an Optimal Solution through a Simple Request Interface
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DOI：
10.1609/aaai.v33i01.33011674
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Qiao Xiang;Haitao Yu;J. Aspnes;Franck Le;L. Kong;Y. Yang
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文献类型：
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作者：
Qiao Xiang;Haitao Yu;J. Aspnes;Franck Le;L. Kong;Y. Yang
由于为现代分布式应用程序提供性能可预测性的需求和实际好处，正在开发和部署网络资源预留系统。然而，现有系统存在局限性：它们要么在寻找最佳资源保留方面效率低下，要么导致私有信息（例如，来自网络基础设施）暴露（例如，向用户）。在本文中，我们设计了一个新的系统BoxOpt，它利用优化和学习理论中高效的oracle构建技术来自动、快速地学习最优的资源预留，而不需要在网络和用户之间交换任何私有信息。我们实现了BoxOpt的原型，并通过使用真实网络拓扑和跟踪的大量实验证明了其效率和功效。结果表明：(1)BoxOpt具有100%的正确率；(2)对于95%的请求，BoxOpt在13秒内学习到最优的资源预留。
Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information (e.g., from the network infrastructure) to be exposed (e.g., to the user). In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user. We implement a prototype of BoxOpt and demonstrate its efficiency and efficacy via extensive experiments using real network topology and trace. Results show that (1) BoxOpt has a 100% correctness ratio, and (2) for 95% of requests, BoxOpt learns the optimal resource reservation within 13 seconds.