Data-driven serverless functions for object storage

Traditionally, active storage techniques have been proposed to move computation tasks to storage nodes in order to exploit data locality. However, we argue in this paper that active storage is ill-suited for cloud storage for two reasons: 1. Lack of elasticity: Computing can only scale out with the number of storage nodes; and 2. Resource Contention: Sharing compute resources can produce interferences in the storage system. Serverless computing is now emerging as a promising alternative for ensuring painless scalability, and also, for simplifying the development of disaggregated computing tasks. Here we present an innovative data-driven serverless computing middleware for object storage. It is a lightweight compute solution that allows users to create small, stateless functions that intercept and operate on data flows in a scalable manner without the need to manage a server or a runtime environment. We demonstrate through different use cases how our solution scales with minimal overhead, while getting rid of the resource contention problems incurred by active storage tasks.