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Current serverless offerings give users a limited degree of flexibility forconfiguring the resources allocated to their function invocations by eithercoupling memory and CPU resources together or providing no knobs at all. Theseconfiguration choices simplify resource allocation decisions on behalf ofusers, but at the same time, create deployments that are resource inefficient.  In this paper, we take a principled approach to the problem of resourceallocation for serverless functions, allowing this choice to be made in anautomatic way that leads to the best combination of performance and cost. Inparticular, we systematically explore the opportunities that come withdecoupling memory and CPU resource allocations and also enabling the use ofdifferent VM types. We find a rich trade-off space between performance andcost. The provider can use this in a number of ways: from exposing all theseparameters to the user, to eliciting preferences for performance and cost fromusers, or by simply offering the same performance with lower cost. Thisflexibility can also enable the provider to optimize its resource utilizationand enable a cost-effective service with predictable performance.  Our results show that, by decoupling memory and CPU allocation, there ispotential to have up to 40% lower execution cost than the preset coupledconfigurations that are the norm in current serverless offerings. Similarly,making the correct choice of VM instance type can provide up to 50% betterexecution time. Furthermore, we demonstrate that providers can utilizedifferent instance types for the same functions to maximize resourceutilization while providing performance within 10-20% of the best resourceconfiguration for each respective function.

With Great Freedom Comes Great Opportunity: Rethinking Resource Allocation for Serverless Functions