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Serverless computing has emerged as a promising alternative toinfrastructure- (IaaS) and platform-as-a-service (PaaS)cloud platforms forapplications with ample parallelism and intermittent activity. Serverlesspromises greater resource elasticity, significant cost savings, and simplifiedapplication deployment. All major cloud providers, including Amazon, Google,and Microsoft, have introduced serverless to their public cloud offerings. Forserverless to reach its potential, there is a pressing need for programmingframeworks that abstract the deployment complexity away from the user. Thisincludes simplifying the process of writing applications for serverlessenvironments, automating task and data partitioning, and handling schedulingand fault tolerance.  We present Ripple, a programming framework designed to specifically takeapplications written for single-machine execution and allow them to takeadvantage of the task parallelism of serverless. Ripple exposes a simpleinterface that users can leverage to express the high-level dataflow of a widespectrum of applications, including machine learning (ML) analytics, genomics,and proteomics. Ripple also automates resource provisioning, meetinguser-defined QoS targets, and handles fault tolerance by eagerly detectingstraggler tasks. We port Ripple over AWS Lambda and show that, across a set ofdiverse applications, it provides an expressive and generalizable programmingframework that simplifies running data-parallel applications on serverless, andcan improve performance by up to 80x compared to IaaS/PaaS clouds for similarcosts.

Ripple: A Practical Declarative Programming Framework for Serverless Compute