Stream processing systems commonly work with
auto-scaling to ensure resource efficiency and quality of service
(QoS). Existing auto-scaling solutions lack accuracy in resource
allocation because they rely on static QoS-resource models that
fail to account for high workload variability and use indirect metrics
with much distractive information. Moreover, different types
of QoS metrics present different characteristics and thus need
individual auto-scaling methods. In this paper, we propose a versatile
auto-scaling solution for operator-level parallelism configuration,
called AuTraScale+, to meet the throughput, processingtime
latency, and event-time latency targets. AuTraScale+ follows
the Bayesian optimization framework to make scaling decisions.
First, it uses the Gaussian process model to eliminate the
negative influence of uncertain factors on the performance model
accuracy. Second, it leverages the expected improvement-based
(EI-based) acquisition function to search and recommend the
optimal configuration quickly. Besides, to make a more accurate
scaling decision when the new model is not ready, AuTraScale+
proposes a transfer learning algorithm to estimate the benefits
of all configurations at a new rate based on existing models and
then recommend the optimal one. We implement and evaluate
AuTraScale+ on the Flink platform. The experimental results
on three representative workloads demonstrate that compared
with the state-of-the-art methods, AuTraScale+ can reduce 66.6%
and 36.7% resource consumption, respectively, in the scale-down
and scale-up scenarios while achieving their throughput and
processing-time latency targets. Compared with other methods
of optimizing event-time latency, AuTraScale+ saves 26.9% of
resources on average.

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