Cosmos: A Cost Model for Serverless Workflows in the 3D Compute Continuum

2025-04-28

AI Summary

Serverless workflow deployment in edge-cloud-air three-dimensional heterogeneous continuum faces challenges including unpredictable costs, coarse-grained resource abstraction, and poorly understood cost-driving mechanisms. Method: This paper proposes the first systematic cross-layer cost and performance–cost trade-off model for serverless workflows, introducing an empirical cost attribution methodology that integrates cross-cloud (AWS/GCP) workflow feature modeling with a load-aware cost decomposition framework to identify dominant cost drivers—namely data transfer, state management, and Backend-as-a-Service (BaaS) usage—under varying workloads. Contribution/Results: Experiments reveal that in data-intensive scenarios, data transfer and state management account for 75% (AWS) and 52% (GCP) of total costs; in compute-intensive AI inference tasks, BaaS costs dominate at 83% (AWS) and 97% (GCP). The framework provides interpretable, transferable theoretical foundations and practical guidelines for cost-aware resource scheduling in heterogeneous serverless environments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Planning with Language Models
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Due to the high scalability, infrastructure management, and pay-per-use pricing model, serverless computing has been adopted in a wide range of applications such as real-time data processing, IoT, and AI-related workflows. However, deploying serverless functions across dynamic and heterogeneous environments such as the 3D (Edge-Cloud-Space) Continuum introduces additional complexity. Each layer of the 3D Continuum shows different performance capabilities and costs according to workload characteristics. Cloud services alone often show significant differences in performance and pricing for similar functions, further complicating cost management. Additionally, serverless workflows consist of functions with diverse characteristics, requiring a granular understanding of performance and cost trade-offs across different infrastructure layers to be able to address them individually. In this paper, we present Cosmos, a cost- and a performance-cost-tradeoff model for serverless workflows that identifies key factors that affect cost changes across different workloads and cloud providers. We present a case study analyzing the main drivers that influence the costs of serverless workflows. We demonstrate how to classify the costs of serverless workflows in leading cloud providers AWS and GCP. Our results show that for data-intensive functions, data transfer and state management costs contribute to up to 75% of the costs in AWS and 52% in GCP. For compute-intensive functions such as AI inference, the cost results show that BaaS services are the largest cost driver, reaching up to 83% in AWS and 97% in GCP.

Problem

• Modeling cost-performance tradeoffs in serverless workflows across Edge-Cloud-Space Continuum
• Analyzing cost drivers for serverless functions in heterogeneous cloud environments
• Comparing cost impacts of data-intensive vs compute-intensive workloads in AWS/GCP

Innovation

• Cost-performance tradeoff model for serverless workflows
• Analyzes cost factors across Edge-Cloud-Space Continuum
• Classifies costs in AWS and GCP for diverse functions