Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee
Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee
复制标题
具有自适应神经模糊控制的自主配置用于基于百分位的延迟保证
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Xiaobo Zhou
中科院分区：
文献类型：
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作者：
P. Lama;Xiaobo Zhou
用于性能保证的自主服务器供应是Internet服务中的关键问题。保证通过多层系统的请求将经历可接受的延迟分布是具有挑战性的。困难主要是由于高度动态的工作负载，底层计算机系统的复杂性，以及缺乏准确的性能模型。我们提出了一种新的自主服务器配置方法的基础上，模型无关的自适应神经模糊控制（NFC）。现有的模型无关的模糊控制器是在试错的基础上手动设计的，并且在面对高度动态的工作负载时通常是无效的。NFC是控制理论和机器学习技术的混合体。它能够通过快速在线学习自构造其结构并调整其参数。我们进一步增强NFC以补偿服务器切换延迟的影响。大量的模拟表明，相比基于规则的模糊控制器和比例积分控制器，基于NFC的方法提供了上级性能保证，在面对高度动态的工作负载。它对工作负载强度、特征、延迟目标和服务器切换延迟的变化具有鲁棒性。我们证明了基于NFC的方法的可行性和性能与虚拟化刀片服务器托管多层在线拍卖基准的测试平台实施。
Autonomic server provisioning for performance assurance is a critical issue in Internet services. It is challenging to guarantee that requests flowing through a multi-tier system will experience an acceptable distribution of delays. The difficulty is mainly due to highly dynamic workloads, the complexity of underlying computer systems, and the lack of accurate performance models. We propose a novel autonomic server provisioning approach based on a model-independent self-adaptive Neural Fuzzy Control (NFC). Existing model-independent fuzzy controllers are designed manually on a trial-and-error basis, and are often ineffective in the face of highly dynamic workloads. NFC is a hybrid of control-theoretical and machine learning techniques. It is capable of self-constructing its structure and adapting its parameters through fast online learning. We further enhance NFC to compensate for the effect of server switching delays. Extensive simulations demonstrate that, compared to a rule-based fuzzy controller and a Proportional-Integral controller, the NFC-based approach delivers superior performance assurance in the face of highly dynamic workloads. It is robust to variation in workload intensity, characteristics, delay target, and server switching delays. We demonstrate the feasibility and performance of the NFC-based approach with a testbed implementation in virtualized blade servers hosting a multi-tier online auction benchmark.