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 usesthe Gaussian process model to eliminate the negative influenceof 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 learningalgorithm to estimate the benefits of all configurations at a newrate based on existing models and then recommend the optimal one.
In this paper, we propose AuTraScale, an automated and transfer learning auto-scaling solution, to determine the appropriate parallelism and resource allocation that meet the latency and throughput targets. AuTraScale uses Bayesian optimization to adapt to the complex relationship between resources and QoS, minimizing the impact of resource interference on the prediction accuracy, and a new metric that measures the performance of operators for accurate optimization. Even when the input data rate changes, it can quickly adjust the parallelism of each operator in response, with a transfer learning algorithm.