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iSpot: Achieving Predictable Performance for Big Data Analytics with Cloud Transient Servers

机译：ISPOT：通过云瞬态服务器实现对大数据分析的可预测性能

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摘要

Achieving predictable performance for big data analytics running on cloud transient servers (e.g., EC2 spot instances) is challenging, because the transient server can be revoked by the cloud and the spot price is nontrivial to predict. Undoubtedly, choosing the low-price yet unstable cloud resources can severely degrade the job performance. To tackle this issue, this paper proposes iSpot, a cost-efficient spot instance provisioning framework in the cloud, by focusing on Spark as a representative DAG (Directed Acyclic Graph)-style big analytics workload. Specifically, it identifies the availability zones with stable spot instance resources by devising an accurate LSTM (Long Short-Term Memory)-based price prediction method. iSpot further predicts the performance of Spark stages and jobs by designing a fined-grained performance model using the job profiling and the DAG information of stages. Based on the price prediction and Spark performance model, iSpot is able to provision the spot instances with the cost-efficient instance type (i.e., the instance type that achieves the minimum monetary cost), in order to deliver predictable performance for big data analytics. Extensive prototype experiments on Amazon EC2 demonstrate that iSpot can guarantee the performance of big data analytics while reducing the job budget with cloud transient servers.
机译：实现云瞬态服务器上运行的大数据分析的可预测性能（例如，EC2 Spot Instances）是具有挑战性的，因为瞬态服务器可以被云撤销，并且现货价格是不动的预测。毫无疑问，选择低价但不稳定的云资源可能会严重降低工作表现。为了解决这个问题，本文提出了ISPOT，通过专注于火花作为代表性DAG（定向非循环图）-Style大分析工作负载来提出云中的成本效益的现场实例配置框架。具体地，它通过设计精确的LSTM（长短期内存）的价格预测方法来识别具有稳定的点实例资源的可用区域。 ISPOT通过使用作业分析和阶段的DAG信息设计被罚款粒度的性能模型，进一步预测了火花阶段和工作的性能。基于价格预测和火花性能模型，ISPot能够使用成本高效的实例类型（即实现最低货币成本的实例类型）提供现货实例，以便为大数据分析提供可预测的性能。亚马逊EC2上的广泛原型实验证明ISPOT可以保证大数据分析的性能，同时减少云瞬态服务器的工作预算。

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