TY - GEN
T1 - Estimating the capacities of function-as-a-service functions
AU - Jindal, Anshul
AU - Chadha, Mohak
AU - Benedict, Shajulin
AU - Gerndt, Michael
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/12/6
Y1 - 2021/12/6
N2 - Serverless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks. Serverless applications follow this model, where the application is decomposed into a set of fine-grained Function-as-a-Service (FaaS) functions. However, the obscurities of the underlying system infrastructure and dependencies between FaaS functions within the application pose a challenge for estimating the performance of FaaS functions. To characterize the performance of a FaaS function that is relevant for the user, we define Function Capacity (FC) as the maximal number of concurrent invocations the function can serve in a time without violating the Service-Level Objective (SLO). The paper addresses the challenge of quantifying the FC individually for each FaaS function within a serverless application. This challenge is addressed by sandboxing a FaaS function and building its performance model. To this end, we develop FnCapacitor - an end-to-end automated Function Capacity estimation tool. We demonstrate the functioning of our tool on Google Cloud Functions (GCF) and AWS Lambda. FnCapacitor estimates the FCs on different deployment configurations (allocated memory & maximum function instances) by conducting time-framed load tests and building various models using statistical: linear, ridge, and polynomial regression, and Deep Neural Network (DNN) methods on the acquired performance data. Our evaluation of different FaaS functions shows relatively accurate predictions with an accuracy greater than 75% using DNN for both cloud providers.
AB - Serverless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks. Serverless applications follow this model, where the application is decomposed into a set of fine-grained Function-as-a-Service (FaaS) functions. However, the obscurities of the underlying system infrastructure and dependencies between FaaS functions within the application pose a challenge for estimating the performance of FaaS functions. To characterize the performance of a FaaS function that is relevant for the user, we define Function Capacity (FC) as the maximal number of concurrent invocations the function can serve in a time without violating the Service-Level Objective (SLO). The paper addresses the challenge of quantifying the FC individually for each FaaS function within a serverless application. This challenge is addressed by sandboxing a FaaS function and building its performance model. To this end, we develop FnCapacitor - an end-to-end automated Function Capacity estimation tool. We demonstrate the functioning of our tool on Google Cloud Functions (GCF) and AWS Lambda. FnCapacitor estimates the FCs on different deployment configurations (allocated memory & maximum function instances) by conducting time-framed load tests and building various models using statistical: linear, ridge, and polynomial regression, and Deep Neural Network (DNN) methods on the acquired performance data. Our evaluation of different FaaS functions shows relatively accurate predictions with an accuracy greater than 75% using DNN for both cloud providers.
KW - function capacity
KW - function-as-a-service
KW - serverless computing
UR - http://www.scopus.com/inward/record.url?scp=85119575511&partnerID=8YFLogxK
U2 - 10.1145/3492323.3495628
DO - 10.1145/3492323.3495628
M3 - Conference contribution
AN - SCOPUS:85119575511
T3 - ACM International Conference Proceeding Series
BT - Companion Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2021
PB - Association for Computing Machinery
T2 - 14th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2021
Y2 - 6 December 2021 through 9 December 2021
ER -