Understanding and Auto-Adjusting Performance-Sensitive Configurations

Authors: Shu Wang, Chi Li, Henry Hoffman, Shan Lu, William Sentosa, Achmad Imam Kistijantoro
Venue: ASPLOS 2018

 This paper presents a control theory approach to solving performance problems in workloads with many configurable parameters. The authors reference database workloads such as Cassandra, HBase, HDFS, and Hadoop MapReduce. The authors employ control theory with two key components outside of traditional control theory: a dynamic pole (error tolerance factor), and a virtual goal. Combined, these two approaches allow SmartConf to meet performance goals and hard constraints better than previous approaches. The authors also go into detail as to how their approach could be integrated into commercial software.

 See Yukta (ISCA 2018) for a similar-flavor paper which also uses control theory. 

 The remainder of this post will be subjective. This paper is exceptionally well-written, using many real-world examples to build motivation. Objectively, the paper's novelty is software and use of a dynamic pole. However, my understanding is control theory by default incorporates model error as part of its feedback. Moreover, the controllers are limited to working with monotonic behaviors and operate independently. Yukta (ISCA 2018), uses control theory with multiple independent controls as well, but allows for feedback between these controllers. This solves the problem in which multiple configurations interact, whereas SmartConf requires they be explicitly programmed by developers.

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