Function Reuse-Driven Serverless Workflow Provisioning with Constrained Bayesian Optimization

Quanwang Wu

doi:10.1109/tc.2026.3724178

Function Reuse-Driven Serverless Workflow Provisioning with Constrained Bayesian Optimization

2026-08-14

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DOI：10.1109/tc.2026.3724178

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Serverless computing is gaining popularity, and serverless workflows stand out as its key workload form. Since resource configurations in serverless workflows are typically manually specified, considerable research has focused on automating resource allocation optimization. However, conventional studies generally follow a coupled CPU–memory allocation, thereby limiting cost-effectiveness, or assume that accurate function performance profiles are known in advance. Moreover, they do not explicitly address the impact of function cold starts on workflow performance. To address these limitations, this paper introduces a serverless workflow provisioning model that jointly supports decoupled resource allocation and function container reuse among workflow tasks implementing identical functionality. Based on this model, we propose a structure-aware trust-region Bayesian optimization (STBO) method to optimize resource configurations for minimizing cost under deadline constraints. The proposed approach is implemented in OpenWhisk, and evaluation experiments demonstrate that, compared with traditional methods, STBO increases the constraint satisfaction rate and reduces execution cost by over 17% on average.

Keywords:

Bayesian Optimization

Function Reuse

Resource Provisioning

Serverless Computing

Workflow

Function Reuse-Driven Serverless Workflow Provisioning with Constrained Bayesian Optimization

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