Large-scale workflow placement in serverless computing using integer nonlinear programming

2026-08-14

AI Summary

This study addresses the joint optimization of cost and latency for large-scale workflow deployment in serverless edge computing by proposing an optimal placement model based on nonlinear integer programming. To overcome computational bottlenecks, we introduce a novel decomposition strategy that effectively balances scalability with problem-specific attributes. Experimental results demonstrate that the proposed method exhibits strong scalability and achieves an average performance improvement of 10% over conventional heuristic algorithms. By significantly reducing both user costs and execution latency, this work establishes a new paradigm for the efficient deployment of complex workflows in edge environments.

Abstract

Serverless edge computing has become a powerful cloud framework that enables the execution of large workflows without the need for the user to manage the underlying servers and edge devices. In this work, we address the challenge of deploying these workflows on a large number of different existing servers and edge devices such that monetary costs for the users and workflow evaluation times are minimized. To this end, the workflow and cloud node attributes are modeled in a mathematical framework. As a result, we present a novel model of the optimal placement problem as a nonlinear integer program. To solve both the issues of scaling towards a larger number of cloud/edge nodes as well as decomposed knowledge of node attributes, we propose a novel decomposition strategy. In a case study, we show the beneficial scaling properties of the decomposition approach and a mean improvement of 10% against a simple deployment heuristic.

Problem

• Serverless edge computing
• Workflow placement
• Cost minimization
• Execution time optimization

Innovation

• Nonlinear Integer Programming
• Decomposition Strategy
• Serverless Edge Computing
• Workflow Placement
• Scalability