# Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging
## 2 Related Work  
Researchers make an effort to understand the relationship of parameters and performance. Several performance prediction models [3; 5; 10; 11; 12; 13; 14; 21; 22; 23] and tuning strategies [6; 16; 24; 25; 26; 27; 28; 29; 30; 31] have been proposed to explore the relationship and recommend good configurations further.  
**Performance Prediction for Databases**. The performance prediction methods fall into two major categories: analytical prediction models and learning-based prediction models. The first class of approaches require an in-depth analysis of the constraints on system performance, and the analysis models are given accordingly [24; 25; 26]. The latter class of approaches train prediction models using machine learning techniques,including GP regression [2; 6; 27; 32], neural networks [7; 10; 28; 29], CART [3; 5; 11; 12; 13; 14], Fourier Learning [21; 22], and so on.  
Existing approaches usually assume a fixed environment such that the modeling/tuning process has to start from the scratch once the environment changes, which exacerbates the performance modeling problem. Therefore, transferring knowledge across environments to assist the modeling/tuning task has become a hot area of research in recent years.  
**Knowledge Transfer for Performance Prediction**. The most relevant research to this paper is to transfer performance prediction models across environments.  
To cope with the workload changes, [PERSON] [2; 32] reuses past experience to reduce the tuning cost for a new application. [PERSON] [7] includes workload characteristics and the most impactful parameters directly in its surrogate model.  
[PERSON] et al. [33] analyzed different hardware platforms to understand the similarity on performance prediction. A simple linear regression model is used to transfer knowledge from a related environment to a target environment. Differently, there is another kind of approach that reuses source data with the hope to capture correlation between environments using learners such as GP Models [34].  
Further, it is worth exploring why and when transfer learning works for performance modeling. [PERSON] et al. [17] combine many statistical and machine learning techniques to study this research question. [PERSON] et al. [35] exploit causal analysis to identify the key knowledge pieces that can be exploited for transfer learning.  
[PERSON] et al. [15] propose a sampling strategy L2S, which is inspired by the research results in [17]. L2S extracts transferable knowledge from the source to drive the selection of more informative samples in the target environment.  
Moreover, transfer learning can only be useful in cases where the source environment is similar to the target environment. [PERSON] [36] focuses on the problem of whence to learn. A racing algorithm is applied to sequentially evaluate candidate environments to discover which of the available environments are best suited to be a source environment.  
In this paper, we concentrate on transferring knowledge across different database versions. There are few relevant researches on this issue at present. The studies on version change scenario is the least among the three main environment change scenarios. The exploratory analysis [17] and causal analysis [35] give us insights into performance prediction across environment change, but no transfer scheme is given in these studies. [PERSON] [36] places emphasis on identifying suitable sources to construct transfer learner. Other work [15; 34] has made some discussion about the version change scenario, but they have not conducted in-depth research and experimental verification on this issue.