  With its elastic power and a pay-as-you-go cost model, the deployment of deep
learning inference services (DLISs) on serverless platforms is emerging as a
prevalent trend. However, the varying resource requirements of different layers
in DL models hinder resource utilization and increase costs, when DLISs are
deployed as a single function on serverless platforms. To tackle this problem,
we propose a model partitioning framework called MOPAR. This work is based on
the two resource usage patterns of DLISs: global differences and local
similarity, due to the presence of resource dominant (RD) operators and layer
stacking. Considering these patterns, MOPAR adopts a hybrid approach that
initially divides the DL model vertically into multiple slices composed of
similar layers to improve resource efficiency. Slices containing RD operators
are further partitioned into multiple sub-slices, enabling parallel
optimization to reduce inference latency. Moreover, MOPAR comprehensively
employs data compression and share-memory techniques to offset the additional
time introduced by communication between slices. We implement a prototype of
MOPAR and evaluate its efficacy using four categories of 12 DL models on
OpenFaaS and AWS Lambda. The experiment results show that MOPAR can improve the
resource efficiency of DLISs by 27.62\% on average, while reducing latency by
about 5.52\%. Furthermore, based on Lambda's pricing, the cost of running DLISs
is reduced by about 2.58 $\times$ using MOPAR.
