Oltp in the cloud: architectures, tradeoffs, and cost

Abstract

What is the best architecture for cloud OLTP systems? How costly is it to run a specific workload? Which and how many hardware instances should be provisioned? To answer such questions systematically, we develop an analytical model framework for cloud OLTP. It enables the analysis of a wide variety of workloads and determines the cost-optimal architecture and hardware configuration for each. Workloads are specified in terms of dataset size, performance, latency, availability and durability requirements. System designs are evaluated based on the CPU, memory, storage, and network resources they require. We study a concrete model instance that is calibrated with the LeanStore storage engine and real-world hardware/service options and prices from AWS, one of the major cloud providers. Our analysis yields several observations on how to achieve fast, durable and cost-efficient OLTP in the cloud.