TY - GEN
T1 - MAFF
T2 - 9th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2022
AU - Zubko, Tetiana
AU - Jindal, Anshul
AU - Chadha, Mohak
AU - Gerndt, Michael
N1 - Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.
PY - 2022
Y1 - 2022
N2 - Function-as-a-Service (FaaS), a key enabler of serverless computing, has been proliferating, as it offers a cheap alternative for application development and deployment. However, while offering many advantages, FaaS also poses new challenges. In particular, most commercial FaaS providers still require users to manually configure the memory allocated to the FaaS functions based on their experience and knowledge. This often leads to suboptimal function performance and higher execution costs. In this paper, we present a framework called MAFF that automatically finds the optimal memory configurations for the FaaS functions based on two optimization objectives: cost-only and balanced (balance between cost and execution duration). Furthermore, MAFF self-adapts the memory configurations for the FaaS functions based on the changing function inputs or other requirements, such as an increase in the number of requests. Moreover, we propose and implement different optimization algorithms for different objectives. We demonstrate the functionality of MAFF on AWS Lambda by testing on four different categories of FaaS functions. Our results show that the suggested memory configurations with the Linear algorithm achieve 90% accuracy with a speedup of 2x compared to the other algorithms. Finally, we compare MAFF with two popular memory optimization tools provided by AWS, i.e., AWS Compute Optimizer and AWS Lambda Power Tuning, and demonstrate how our framework overcomes their limitations.
AB - Function-as-a-Service (FaaS), a key enabler of serverless computing, has been proliferating, as it offers a cheap alternative for application development and deployment. However, while offering many advantages, FaaS also poses new challenges. In particular, most commercial FaaS providers still require users to manually configure the memory allocated to the FaaS functions based on their experience and knowledge. This often leads to suboptimal function performance and higher execution costs. In this paper, we present a framework called MAFF that automatically finds the optimal memory configurations for the FaaS functions based on two optimization objectives: cost-only and balanced (balance between cost and execution duration). Furthermore, MAFF self-adapts the memory configurations for the FaaS functions based on the changing function inputs or other requirements, such as an increase in the number of requests. Moreover, we propose and implement different optimization algorithms for different objectives. We demonstrate the functionality of MAFF on AWS Lambda by testing on four different categories of FaaS functions. Our results show that the suggested memory configurations with the Linear algorithm achieve 90% accuracy with a speedup of 2x compared to the other algorithms. Finally, we compare MAFF with two popular memory optimization tools provided by AWS, i.e., AWS Compute Optimizer and AWS Lambda Power Tuning, and demonstrate how our framework overcomes their limitations.
KW - Function-as-a-Service
KW - cost optimization
KW - duration optimization
KW - memory allocation
KW - memory optimization
KW - serverless
UR - http://www.scopus.com/inward/record.url?scp=85128973816&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-04718-3_9
DO - 10.1007/978-3-031-04718-3_9
M3 - Conference contribution
AN - SCOPUS:85128973816
SN - 9783031047176
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 137
EP - 154
BT - Service-Oriented and Cloud Computing - 9th IFIP WG 6.12 European Conference, ESOCC 2022, Proceedings
A2 - Montesi, Fabrizio
A2 - Papadopoulos, George Angelos
A2 - Zimmermann, Wolf
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 March 2022 through 24 March 2022
ER -