TY - GEN

T1 - Optimizing cost of serverless computing through function fusion and placement

AU - Elgamal, Tarek

AU - Sandur, Atul

AU - Nahrstedt, Klara

AU - Agha, Gul

N1 - Funding Information:
This work is supported by the National Science Foundation under grants NSF ACI 1659293, NSF ACI 1443013, NSF CCF 14-38982, and NSF CCF 16-17401.

PY - 2018/12/6

Y1 - 2018/12/6

N2 - Serverless computing has recently experienced significant adoption by several applications, especially Internet of Things (IoT) applications. In serverless computing, rather than deploying and managing dedicated virtual machines, users are able to deploy individual functions, and pay only for the time that their code is actually executing. However, since serverless platforms are relatively new, they have a completely different pricing model that depends on the memory, duration, and the number of executions of a sequence/workflow of functions. In this paper we present an algorithm that optimizes the price of serverless applications in AWS Lambda. We first describe the factors affecting price of serverless applications which include: (1) fusing a sequence of functions, (2) splitting functions across edge and cloud resources, and (3) allocating the memory for each function. We then present an efficient algorithm to explore different function fusion-placement solutions and find the solution that optimizes the application’s price while keeping the latency under a certain threshold. Our results on image processing workflows show that the algorithm can find solutions optimizing the price by more than 35%-57% with only 5%-15% increase in latency. We also show that our algorithm can find non-trivial memory configurations that reduce both latency and price.

AB - Serverless computing has recently experienced significant adoption by several applications, especially Internet of Things (IoT) applications. In serverless computing, rather than deploying and managing dedicated virtual machines, users are able to deploy individual functions, and pay only for the time that their code is actually executing. However, since serverless platforms are relatively new, they have a completely different pricing model that depends on the memory, duration, and the number of executions of a sequence/workflow of functions. In this paper we present an algorithm that optimizes the price of serverless applications in AWS Lambda. We first describe the factors affecting price of serverless applications which include: (1) fusing a sequence of functions, (2) splitting functions across edge and cloud resources, and (3) allocating the memory for each function. We then present an efficient algorithm to explore different function fusion-placement solutions and find the solution that optimizes the application’s price while keeping the latency under a certain threshold. Our results on image processing workflows show that the algorithm can find solutions optimizing the price by more than 35%-57% with only 5%-15% increase in latency. We also show that our algorithm can find non-trivial memory configurations that reduce both latency and price.

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U2 - 10.1109/SEC.2018.00029

DO - 10.1109/SEC.2018.00029

M3 - Conference contribution

AN - SCOPUS:85060200056

T3 - Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018

SP - 300

EP - 312

BT - Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018

Y2 - 25 October 2018 through 27 October 2018

ER -