Reproducible and Portable Big Data Analytics in the Cloud
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Cloud computing has become a major approach to help reproduce computational experiments because it supports on-demand hardware and software resource provisioning. Yet there are still two main difficulties in reproducing big data applications in the cloud. The first is how to automate end-to-end execution of analytics including environment provisioning, analytics pipeline description, pipeline execution and resource termination. The second is an application developed for one cloud, is difficult to be reproduced in another cloud, a.k.a. vendor lock-in problem. To tackle these problems, we leverage serverless computing and containerization techniques for automatic scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds. We propose and develop an open-source toolkit that supports 1) fully automated end-to-end execution and reproduction via a single command, 2) automatic data and configuration storage for each execution, 3) flexible client modes based on user preferences, 4) execution history query, and 5) simple reproduction of existing executions in the same environment or a different environment. We did extensive experiments on both AWS and Azure using three big data analytics applications that run on a virtual CPU/GPU cluster. The experiments show our toolkit can achieve good execution performance, scalability and efficient reproducibility for cloud-based big data analytics.
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