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LPCD: A Parallel Candidate Deployment Strategy in Stateful Serverless Computing With Low Latency
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AI
DOI：10.1109/TNSM.2025.3611445.png)
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Abstract
En 中文
Serverless computing has been widely regarded as an ideal computing paradigm, enabling edge servers to host serverless functions. Due to its high scalability and usage-based pricing model, it provides efficient services across various applications. However, in the deployment process of serverless applications, past works lack considerations for the parallel relationships between stateful functions, which increases end to end latency. To leverage the parallel dependencies between functions, we propose a strategy for dependent function parallelization deployment, named LPCD (Low latency Parallel Candidate Deployment strategy). By partitioning the problem into inter-layer function deployment and analyzing optimal substructures, a heuristic algorithm is introduced to determine candidate deployment strategies for each layer of the users, which aims at identifying the optimal edge server for each function instance during deployment to enhance user satisfaction. Through simulation experiments, we evaluate the performance of the strategy. The experiments results indicate that the average latency was reduced by at least 41% compared with the state-of-the-art strategies.
Keywords:
Serverless computing
Parallel processing
Costs
Computational modeling
Heuristic algorithms
Runtime
Logic
Face recognition
Edge computing
Data centers
parallel dependent
latency modeling
candidate strategy
Journal
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5.4
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528
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