Current serverless offerings give users limited flexibility for configuring the resources allocated to their function invocations. This simplifies the interface for users to deploy server-less computations but creates deployments that are resource inefficient. In this paper, we take a principled approach to the problem of resource allocation for serverless functions, analyzing the effects of automating this choice in a way that leads to the best combination of performance and cost. In particular, we systematically explore the opportunities that come with decoupling memory and CPU resource allocations and also enabling the use of different VM types, and we find a rich trade-off space between performance and cost. The provider can use this in a number of ways, e.g., exposing all these parameters to the user; eliding preferences for performance and cost from users and simply offer the same performance with lower cost; or exposing a small number of choices for users to trade performance for cost. Our results show that, by decoupling memory and CPU allocation, there is the potential to have up to 40% lower execution cost than the preset coupled configurations that are the norm in current serverless offerings. Similarly, making the correct choice of VM instance type can provide up to 50% better execution time. Furthermore, we demonstrate that providers have the flexibility to choose different instance types for the same functions to maximize resource utilization while providing performance within 10--20% of the best resource configuration for each respective function.
Bibliographical noteKAUST Repository Item: Exported on 2023-05-23
Acknowledgements: M. Bilal currently with Unbabel. Work done in part while author was interning at KAUST. We thank our shepherds, Redha Gouicem and John Wilkes, and the anonymous reviewers for their feedback. M. Bilal was supported by a fellowship from the Erasmus Mundus Joint Doctorate in Distributed Computing (EMJD-DC) program funded by the European Commission (FPA 2012-0030). This work was supported by Fundação para a Ciência e a Tecnologia, under grants UIDB/50021/2020, PTDC/CCIINF/6762/2020.