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Statically Inferring Usage Bounds for Infrastructure as Code

Feitong Qiao, Aryana Mohammadi, Jürgen Cito, Mark Santolucito

TL;DR

The paper addresses the difficulty of estimating cloud usage and costs for Infrastructure as Code by exploiting the deployment topology to derive bounds on usage with formal guarantees. It models IaC deployments as a dataflow resource graph and encodes inter-resource interactions as composable SMT constraints over node and edge variables. The authors implement an analysis tool, evaluate it on a benchmark of over 1000 real-world CloudFormation templates, and show that the approach yields feasible bounds and scales to typical IaC sizes, with rapid runtimes. This topology-aware, constraint-based framework enables principled feasibility checks on user-provided usage estimates and supports safer cost planning in IaC workflows.

Abstract

Infrastructure as Code (IaC) has enabled cloud customers to have more agility in creating and modifying complex deployments of cloud-provisioned resources. By writing a configuration in IaC languages such as CloudFormation, users can declaratively specify their infrastructure and CloudFormation will handle the creation of the resources. However, understanding the complexity of IaC deployments has emerged as an unsolved issue. In particular, estimating the cost of an IaC deployment requires estimating the future usage and pricing models of every cloud resource in the deployment. Gaining transparency into predicted usage/costs is a leading challenge in cloud management. Existing work either relies on historical usage metrics to predict cost or on coarse-grain static analysis that ignores interactions between resources. Our key insight is that the topology of an IaC deployment imposes constraints on the usage of each resource, and we can formalize and automate the reasoning on constraints by using an SMT solver. This allows customers to have formal guarantees on the bounds of their cloud usage. We propose a tool for fine-grained static usage analysis that works by modeling the inter-resource interactions in an IaC deployment as a set of SMT constraints, and evaluate our tool on a benchmark of over 1000 real world IaC configurations.

Statically Inferring Usage Bounds for Infrastructure as Code

TL;DR

The paper addresses the difficulty of estimating cloud usage and costs for Infrastructure as Code by exploiting the deployment topology to derive bounds on usage with formal guarantees. It models IaC deployments as a dataflow resource graph and encodes inter-resource interactions as composable SMT constraints over node and edge variables. The authors implement an analysis tool, evaluate it on a benchmark of over 1000 real-world CloudFormation templates, and show that the approach yields feasible bounds and scales to typical IaC sizes, with rapid runtimes. This topology-aware, constraint-based framework enables principled feasibility checks on user-provided usage estimates and supports safer cost planning in IaC workflows.

Abstract

Infrastructure as Code (IaC) has enabled cloud customers to have more agility in creating and modifying complex deployments of cloud-provisioned resources. By writing a configuration in IaC languages such as CloudFormation, users can declaratively specify their infrastructure and CloudFormation will handle the creation of the resources. However, understanding the complexity of IaC deployments has emerged as an unsolved issue. In particular, estimating the cost of an IaC deployment requires estimating the future usage and pricing models of every cloud resource in the deployment. Gaining transparency into predicted usage/costs is a leading challenge in cloud management. Existing work either relies on historical usage metrics to predict cost or on coarse-grain static analysis that ignores interactions between resources. Our key insight is that the topology of an IaC deployment imposes constraints on the usage of each resource, and we can formalize and automate the reasoning on constraints by using an SMT solver. This allows customers to have formal guarantees on the bounds of their cloud usage. We propose a tool for fine-grained static usage analysis that works by modeling the inter-resource interactions in an IaC deployment as a set of SMT constraints, and evaluate our tool on a benchmark of over 1000 real world IaC configurations.
Paper Structure (18 sections, 3 figures)

This paper contains 18 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Motivating Example
  3. System Overview
  4. Building the resource graph
  5. Node variables and edge variables
  6. Constraint generation
  7. IaC Layer Constraints
  8. Application Layer Constraints
  9. Evaluation
  10. Quantitative Analysis
  11. Case Study
  12. Related Work
  13. Discussion
  14. Limitations
  15. Application Layer Constraints from Program Analysis
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Motivating example
  • Figure 2: System diagram
  • Figure 3: Number of constraints for the benchmark samples (top 100).