Bayesian-Driven Automated Scaling in Stream Computing With Multiple QoS Targets

Stream processing systems commonly work with auto-scaling for efficient resource utilization, though it is still a great challenge to dynamically optimize the fine-grained resource configuration, which requires accurate estimation of the system QoS. Existing auto-scaling solutions suffer from a lack of accuracy due to the static QoS-resource models that neglect high workload variability and indirect metrics that contain much distractive information. Moreover, different types of QoS metrics present different QoS 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, processing-time 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 quickly search and recommend the optimal configuration. 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 an average of 26.9% resources for the Yahoo streaming job.