Serverless computing has emerged as a promising alternative to infrastructure- (IaaS) and platform-as-a-service (PaaS)cloud platforms for applications with ample parallelism and intermittent activity. Serverless promises greater resource elasticity, significant cost savings, and simplified application deployment. All major cloud providers, including Amazon, Google, and Microsoft, have introduced serverless to their public cloud offerings. For serverless to reach its potential, there is a pressing need for programming frameworks that abstract the deployment complexity away from the user. This includes simplifying the process of writing applications for serverless environments, automating task and data partitioning, and handling scheduling and fault tolerance. We present Ripple, a programming framework designed to specifically take applications written for single-machine execution and allow them to take advantage of the task parallelism of serverless. Ripple exposes a simple interface that users can leverage to express the high-level dataflow of a wide spectrum of applications, including machine learning (ML) analytics, genomics, and proteomics. Ripple also automates resource provisioning, meeting user-defined QoS targets, and handles fault tolerance by eagerly detecting straggler tasks. We port Ripple over AWS Lambda and show that, across a set of diverse applications, it provides an expressive and generalizable programming framework that simplifies running data-parallel applications on serverless, and can improve performance by up to 80x compared to IaaS/PaaS clouds for similar costs.
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