Possible Futures for Cloud Cost Models

TL;DR

This paper explores the evolution and future prospects of cloud cost models, emphasizing their impact on scientific computing and the need for adaptation to support ongoing discovery.

Contribution

It analyzes the historical development and proposes future directions for cloud cost models tailored to scientific and AI/ML workloads.

Findings

Current cloud cost models favor AI/ML over scientific computing

Resource contention may hinder scientific research access

Future models need to balance AI/ML and scientific needs

Abstract

Cloud is now the leading software and computing hardware innovator, and is changing the landscape of compute to one that is optimized for artificial intelligence and machine learning (AI/ML). Computing innovation was initially driven to meet the needs of scientific computing. As industry and consumer usage of computing proliferated, there was a shift to satisfy a multipolar customer base. Demand for AI/ML now dominates modern computing and innovation has centralized on cloud. As a result, cost and resource models designed to serve AI/ML use cases are not currently well suited for science. If resource contention resulting from a unipole consumer makes access to contended resources harder for scientific users, a likely future is running scientific workloads where they were not intended. In this article, we discuss the past, current, and possible futures of cloud cost models for the continued support of discovery and science.