Spatial–Temporal Prediction Models for Active Ticket Managing in Data Centers
Spatial–Temporal Prediction Models for Active Ticket Managing in Data Centers
复制标题
DOI：
10.1109/tnsm.2018.2794409
复制
发表时间：
2018-01
影响因子：
5.3
通讯作者：
Ji Xue;R. Birke;L. Chen;E. Smirni
中科院分区：
文献类型：
--
作者：
Ji Xue;R. Birke;L. Chen;E. Smirni
在物理机托管多个虚拟机 (VM) 的数据中心中，性能票据处理是一项昂贵的操作。大量票证源自资源使用警告，例如 CPU 和 RAM 使用量超过预定义阈值。 CPU 和 RAM 使用的瞬态性质以及它们在同一位置的虚拟机之间随时间的强相关性极大地增加了票证管理的复杂性。基于从生产数据中心（具有 6K 物理机和超过 80K 虚拟机）收集的大量资源使用数据，我们首先发现共置资源使用系列之间/内部的空间和时间依赖性模式。利用我们的主要发现，我们开发了一个主动票证管理 (ATM) 系统，旨在大幅减少票证使用量。 ATM 包括：1) 基于时空依赖性的时间序列预测方法；2) 针对位于单个数据中心客户端内的一个或多个盒子内的虚拟机的 CPU 和 RAM 资源的主动容量规划策略，旨在大幅减少使用票据。 ATM 利用共置虚拟机和单客户端设备的多个资源之间/内部的时空依赖性来进行使用预测，然后启动主动容量规划。对运营数据中心6K物理盒子痕迹的评估结果表明，ATM能够以较低的计算开销准确预测云数据中心的使用序列。同时，ATM 对于 VM 和 Box 使用系列都实现了高达 60% 的票据大幅减少。
Performance ticket handling is an expensive operation in data centers, where physical boxes host multiple virtual machines (VMs). A large body of tickets arise from resource usage warnings, e.g., CPU and RAM usages that exceed predefined thresholds. The transient nature of CPU and RAM usage as well as their strong correlation across time among co-located VMs within boxes drastically increase the complexity of ticket management. Based on large resource usage data collected from production data centers, with 6K physical boxes and more than 80K VMs, we first discover patterns of spatial and temporal dependencies among/within the usage series of co-located resources. Leveraging our key findings, we develop an active ticket managing (ATM) system that aims to drastically reduce usage tickets. ATM consists of: 1) a spatial–temporal dependency-based time series prediction methodology and 2) a proactive capacity planning policy for CPU and RAM resources for VMs co-located within a box and boxes within a single data center client, that aims to drastically reduce usage tickets. ATM exploits the spatial–temporal dependency across/within multiple resources of co-located VMs and single-client boxes for usage prediction, and then actuates proactive capacity planning. Evaluation results on traces of 6K physical boxes from operating data centers show that ATM is able to provide accurate prediction of usage series in cloud data centers with low computational overhead. At the same time ATM achieves significant ticket reduction up to 60% for both VM and box usage series.