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Adaptive container scheduling for serverless workflows in cloud-edge collaborative computing
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AI
DOI：10.1016/j.jpdc.2026.105308.png)
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Abstract
En 中文
Serverless applications typically follow a three-level hierarchy of workflows, tasks, and functions. Cloud-edge collaborative platforms efficiently execute such workflows through lightweight containers and fine-grained Function-as-a-Service (FaaS). Functions serve as the fundamental execution units and require effective scheduling to meet workflow response requirements and ensure user satisfaction. However, scheduling faces significant challenges due to stochastic workflow arrivals, complex task dependencies, parallel function execution, heterogeneous resource demands, and geographically distributed cloud-edge resources. In this paper, we propose an adaptive user-satisfaction-driven heuristic scheduling algorithm (AUHS) to maximize the mean user satisfaction (MUS) across all workflows. AUHS incorporates a cross-workflow iterative sorting mechanism to determine the batch-level optimal scheduling sequences for functions. The optimal scheduling plan of each function is automatically identified at runtime through an end-to-end non-execution latency-driven heuristic, which specifies the offloading location and execution container. AUHS is evaluated on a comprehensive set of random instances synthesized from real-world data. Experimental results show that AUHS outperforms the comparison algorithms in improving MUS by at least 4.60%, deadline satisfaction ratio by at least 15.14%, and scheduling efficiency by at least 10.13%, while reducing mean tardiness by at least 18.92%.
Keywords:
Serverless workflows
Container scheduling
Cloud-edge computing
User satisfaction
Heuristic
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