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首页> 外文会议>2016 International Conference on Cloud and Autonomic Computing >A Self-Adaptive Performance-Aware Capacity Controller in Overbooked Datacenters
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A Self-Adaptive Performance-Aware Capacity Controller in Overbooked Datacenters

机译：超量预订的数据中心中的自适应性能感知容量控制器

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摘要

Interference between co-located VMs may lead to performance fluctuations and degradation, especially in overbooked datacenters. To limit this problem, VMs access to physical resources needs to be controlled to ensure certain degree of isolation among them. However, the mapping between virtual and physical resources must be performed in a dynamic way so that it can be adapted to the changing applications requirements, as well as to the different set of co-located VMs. To address this problem we propose a twofold approach: (1) a Quality of Service (QoS) scheme that provides different isolation levels for VMs with different QoS requirements, and (2) a self-adaptive fuzzy Q-learning capacity controller that proactively readjusts the isolation degree based on applications performance. Our evaluation based on real cloud applications and workloads demonstrates that the efficient, adaptive mapping between VMs and physical resources reduces the interference between VMs, enabling the possibility of co-locating more VMs, increases overall utilization, and ensures the performance of critical applications while providing more resources to the low QoS applications.
机译：主机虚拟机之间的干扰可能导致性能波动和性能下降，尤其是在超额预定的数据中心中。为了限制此问题，需要控制VM对物理资源的访问，以确保它们之间一定程度的隔离。但是，虚拟和物理资源之间的映射必须以动态方式执行，以便可以适应不断变化的应用程序要求以及不同位置的虚拟机集。为了解决这个问题，我们提出了两种方法：（1）服务质量（QoS）方案，为具有不同QoS要求的VM提供不同的隔离级别，以及（2）主动重新调整的自适应模糊Q学习能力控制器基于应用程序性能的隔离度。我们基于真实云应用程序和工作负载的评估表明，虚拟机和物理资源之间的高效，自适应映射可减少虚拟机之间的干扰，从而可以将更多虚拟机并置在一起，提高总体利用率，并确保关键应用程序的性能，同时提供更多资源用于低QoS应用程序。

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