1,828 research outputs found
Serving deep learning models in a serverless platform
Serverless computing has emerged as a compelling paradigm for the development
and deployment of a wide range of event based cloud applications. At the same
time, cloud providers and enterprise companies are heavily adopting machine
learning and Artificial Intelligence to either differentiate themselves, or
provide their customers with value added services. In this work we evaluate the
suitability of a serverless computing environment for the inferencing of large
neural network models. Our experimental evaluations are executed on the AWS
Lambda environment using the MxNet deep learning framework. Our experimental
results show that while the inferencing latency can be within an acceptable
range, longer delays due to cold starts can skew the latency distribution and
hence risk violating more stringent SLAs
SEUSS: rapid serverless deployment using environment snapshots
Modern FaaS systems perform well in the case of repeat executions when function working sets stay small. However, these platforms are less effective when applied to more complex, large-scale and dynamic workloads. In this paper, we introduce SEUSS (serverless execution via unikernel snapshot stacks), a new system-level approach for rapidly deploying serverless functions. Through our approach, we demonstrate orders of magnitude improvements in function start times and cacheability, which improves common re-execution paths while also unlocking previously-unsupported large-scale bursty workloads.Published versio
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