Mitigating Cold Start Problem in Serverless Computing with Function Fusion

As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due...

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Main Authors: Seungjun Lee, Daegun Yoon, Sangho Yeo, Sangyoon Oh
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/21/24/8416
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author Seungjun Lee
Daegun Yoon
Sangho Yeo
Sangyoon Oh
author_facet Seungjun Lee
Daegun Yoon
Sangho Yeo
Sangyoon Oh
author_sort Seungjun Lee
collection DOAJ
description As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution. The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows.
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spelling doaj.art-99e787dc0292452cb692f8bc1ddba69b2023-11-23T10:31:09ZengMDPI AGSensors1424-82202021-12-012124841610.3390/s21248416Mitigating Cold Start Problem in Serverless Computing with Function FusionSeungjun Lee0Daegun Yoon1Sangho Yeo2Sangyoon Oh3Department of Artificial Intelligence, Ajou University, Suwon 16499, KoreaDepartment of Artificial Intelligence, Ajou University, Suwon 16499, KoreaDepartment of Artificial Intelligence, Ajou University, Suwon 16499, KoreaDepartment of Artificial Intelligence, Ajou University, Suwon 16499, KoreaAs Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution. The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows.https://www.mdpi.com/1424-8220/21/24/8416serverless computingfunction fusionserverless workflow
spellingShingle Seungjun Lee
Daegun Yoon
Sangho Yeo
Sangyoon Oh
Mitigating Cold Start Problem in Serverless Computing with Function Fusion
Sensors
serverless computing
function fusion
serverless workflow
title Mitigating Cold Start Problem in Serverless Computing with Function Fusion
title_full Mitigating Cold Start Problem in Serverless Computing with Function Fusion
title_fullStr Mitigating Cold Start Problem in Serverless Computing with Function Fusion
title_full_unstemmed Mitigating Cold Start Problem in Serverless Computing with Function Fusion
title_short Mitigating Cold Start Problem in Serverless Computing with Function Fusion
title_sort mitigating cold start problem in serverless computing with function fusion
topic serverless computing
function fusion
serverless workflow
url https://www.mdpi.com/1424-8220/21/24/8416
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