  Transformer model empowered architectures have become a pillar of cloud
services that keeps reshaping our society. However, the dynamic query loads and
heterogeneous user requirements severely challenge current transformer serving
systems, which rely on pre-training multiple variants of a foundation model,
i.e., with different sizes, to accommodate varying service demands.
Unfortunately, such a mechanism is unsuitable for large transformer models due
to the additional training costs and excessive I/O delay. In this paper, we
introduce OTAS, the first elastic serving system specially tailored for
transformer models by exploring lightweight token management. We develop a
novel idea called token adaptation that adds prompting tokens to improve
accuracy and removes redundant tokens to accelerate inference. To cope with
fluctuating query loads and diverse user requests, we enhance OTAS with
application-aware selective batching and online token adaptation. OTAS first
batches incoming queries with similar service-level objectives to improve the
ingress throughput. Then, to strike a tradeoff between the overhead of token
increment and the potentials for accuracy improvement, OTAS adaptively adjusts
the token execution strategy by solving an optimization problem. We implement
and evaluate a prototype of OTAS with multiple datasets, which show that OTAS
improves the system utility by at least 18.2%.
