  While (1) serverless computing is emerging as a popular form of cloud
execution, datacenters are going through major changes: (2) storage
dissaggregation in the system infrastructure level and (3) integration of
domain-specific accelerators in the hardware level. Each of these three trends
individually provide significant benefits; however, when combined the benefits
diminish. Specifically, the paper makes the key observation that for serverless
functions, the overhead of accessing dissaggregated persistent storage
overshadows the gains from accelerators. Therefore, to benefit from all these
trends in conjunction, we propose Domain-Specific Computational Storage for
Serverless (DSCS-Serverless). This idea contributes a serverless model that
leverages a programmable accelerator within computational storage to conjugate
the benefits of acceleration and storage disaggregation simultaneously. Our
results with eight applications shows that integrating a comparatively small
accelerator within the storage (DSCS-Serverless) that fits within its power
constrains (15 Watts), significantly outperforms a traditional disaggregated
system that utilizes the NVIDIA RTX 2080 Ti GPU (250 Watts). Further, the work
highlights that disaggregation, serverless model, and the limited power budget
for computation in storage require a different design than the conventional
practices of integrating microprocessors and FPGAs. This insight is in contrast
with current practices of designing computational storage that are yet to
address the challenges associated with the shifts in datacenters. In comparison
with two such conventional designs that either use quad-core ARM A57 or a
Xilinx FPGA, DSCS-Serverless provides 3.7x and 1.7x end-to-end application
speedup, 4.3x and 1.9x energy reduction, and 3.2x and 2.3x higher cost
efficiency, respectively.
