On Developing and Operating of Data Elasticity Management Process

Abstract

The Data-as-a-Service (DaaS) model enables data analytics providers to provision and offer data assets to their consumers. To achieve quality of results for the data assets, we need to enable DaaS elasticity by trading off quality and cost of resource usage. However, most of the current work on DaaS is focused on infrastructure elasticity, such as scaling in/out data nodes and virtual machines based on performance and usage, without considering the data assets' quality of results. In this paper, we introduce an elastic data asset model for provisioning data enriched with quality of results. Based on this model, we present techniques to generate and operate data elasticity management process that is used to monitor, evaluate and enforce expected quality of results. We develop a runtime system to guarantee the quality of resulting data assets provisioned ondemand. We present several experiments to demonstrate the usefulness of our proposed techniques.

Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff

“…From the system viewpoint, to implement possible actions for changing pipelines, we should define primitive action models, in which actions can be mapped to tradeoff conditions. Initial work on primitive action models [5] could be a starting point.…”