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Mitigating Cold Start Problem in Serverless Computing with Function Fusion.

Seungjun Lee1, Daegun Yoon1, Sangho Yeo1

  • 1Department of Artificial Intelligence, Ajou University, Suwon 16499, Korea.

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|December 28, 2021
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Summary
This summary is machine-generated.

Function fusion mitigates serverless computing cold start latency in AI workflows. This approach optimizes response times by intelligently merging functions, reducing delays without negatively impacting parallel execution.

Keywords:
function fusionserverless computingserverless workflow

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Serverless computing is crucial for cloud-based AI services due to its simplicity and scalability.
  • The cold start problem in serverless computing introduces latency, impacting AI workflow performance.
  • Existing solutions like function fusion can inadvertently increase response times for parallel functions.

Purpose of the Study:

  • To present an approach for mitigating cold start latency in serverless AI workflows.
  • To optimize workflow response time by intelligently applying function fusion.
  • To address the challenge of increased latency when fusing parallel functions.

Main Methods:

  • Identifying three key latencies affecting workflow response time.
  • Developing a workflow response time model that incorporates these latencies.
  • Proposing an efficient method to find function fusion solutions that minimize cold start latency.

Main Results:

  • The proposed method significantly reduces workflow response time.
  • Response times were reduced to 28-86% of the original workflow's response time across five test cases.
  • The approach effectively balances cold start mitigation with the execution of parallel functions.

Conclusions:

  • Function fusion is a viable strategy to reduce cold start latency in serverless AI workflows.
  • The developed model and optimization method provide an effective solution for improving serverless AI performance.
  • This research offers practical insights for designing efficient serverless AI applications.