An Elastic Ephemeral Datastore using Cheap, Transient Cloud Resources

TL;DR

This paper introduces an elastic ephemeral datastore that leverages cheap, transient cloud spot instances to reduce costs in distributed data analytics, handling preemptions transparently and maintaining performance.

Contribution

It presents a novel elastic distributed ephemeral datastore that manages node preemptions and reduces costs for ephemeral data in cloud analytics workloads.

Findings

Achieves 60% cost reduction using spot instances

End-to-end execution time increases by only 2.1%

Handles node preemptions transparently to applications

Abstract

Spot instances are virtual machines offered at 60-90% lower cost that can be reclaimed at any time, with only a short warning period. Spot instances have already been used to significantly reduce the cost of processing workloads in the cloud. However, leveraging spot instances to reduce the cost of stateful cloud applications is much more challenging, as the sudden preemptions lead to data loss. In this work, we propose leveraging spot instances to decrease the cost of ephemeral data management in distributed data analytics applications. We specifically target ephemeral data as this large class of data in modern analytics workloads has low durability requirements; if lost, the data can be regenerated by re-executing compute tasks. We design an elastic, distributed ephemeral datastore that handles node preemptions transparently to user applications and minimizes data loss by redistributing data during node preemption warning periods. We implement our elastic datastore on top of the Apache Crail datastore and evaluate the system with various workloads and VM types. By leveraging spot instances, we show that we can run TPC-DS queries with 60\% lower cost compared to using on-demand VMs for the datastore, while only increasing end-to-end execution time by 2.1%.