Large-Scale Causality Discovery Analytics as a Service

Abstract

Data-driven causality discovery is a common way to understand causal relationships among different components of

a system. We study how to achieve scalable data-driven causal- ity discovery on Amazon Web Services (AWS) and Microsoft

Azure cloud and propose a causality discovery as a service

(CDaaS) framework. With this framework, users can easily re- run previous causality discovery experiments or run causality

discovery with different setups (such as new datasets or causality discovery parameters). Our CDaaS leverages Cloud Container

Registry service and Virtual Machine service to achieve scal- able causality discovery with different discovery algorithms. We

further did extensive experiments and benchmarking of our CDaaS to understand the effects of seven factors (big data engine parameter setting, virtual machine instance number, type, subtype, size, cloud service, cloud provider) and how to best provision cloud resources for our causality discovery service based on certain goals including execution time, budgetary cost and cost-performance ratio. We report our findings from the benchmarking, which can help obtain optimal configurations based on each application’s characteristics. The findings show proper configurations could lead to both faster execution time and less budgetary cost.