AdaFuse: Adaptive Function Fusion for Dynamic Serverless Workflows

Abstract

Serverless workflows are commonly modeled as directed acyclic graphs (DAGs) composed of fine-grained functions. While this design promotes modularity and programming flexibility, it also incurs frequent inter-function communication. To alleviate this performance overhead, function fusion has been explored to co-locate and execute communicating functions within a shared container instance. However, existing approaches fail to account for the time-varying trade-off between communication latency reduction and resource overprovisioning arising from path uncertainty in dynamic serverless workflows, making it difficult to sustain high resource efficiency over time. In this paper, we propose a new adaptive function fusion strategy that dynamically reconstructs serverless workflows by adjusting communication schemes between functions in response to changing workflow invocations. Realizing this strategy, however, requires jointly optimizing function fusion and per-instance configuration, a problem with a combinatorial search space, while managing the substantial overhead of switching fusion schemes at runtime. To address these challenges, we design AdaFuse, a novel system for dynamic serverless workflows. AdaFuse jointly optimizes function fusion and instance reconfiguration through an efficient beam-search–based algorithm to optimize the end-to-end resource allocation. With the optimized fusion scheme, AdaFuse employs a fluid migration mechanism that incrementally transitions the workflow deployment while seamlessly redirecting incoming workloads. Evaluations across diverse benchmarks show that AdaFuse reduces resource allocation by up to 49.4% compared to baselines, without compromising SLO attainment.