By exploiting on-the-fly computation, Data Stream Processing (DSP) applications can process huge volumes of data in a near real-time fashion. Adapting the application parallelism at run-time is critical in order to guarantee a proper level of QoS in face of varying workloads. In this paper, we consider Reinforcement Learning based techniques in order to self-configure the number of parallel instances for a single DSP operator. Specifically, we propose two model-based approaches and compare them to the baseline Q-learning algorithm. Our numerical investigations show that the proposed solutions provide better performance and faster convergence than the baseline.
Cardellini, V., Lo Presti, F., Nardelli, M., Russo Russo, G. (2018). Auto-Scaling in Data Stream Processing Applications: A Model-Based Reinforcement Learning Approach. In InfQ 2017: New Frontiers in Quantitative Methods in Informatics (pp.97-110). Springer, Cham [10.1007/978-3-319-91632-3_8].
Auto-Scaling in Data Stream Processing Applications: A Model-Based Reinforcement Learning Approach
Cardellini, Valeria;Lo Presti, Francesco;Russo Russo, Gabriele
2018-05-01
Abstract
By exploiting on-the-fly computation, Data Stream Processing (DSP) applications can process huge volumes of data in a near real-time fashion. Adapting the application parallelism at run-time is critical in order to guarantee a proper level of QoS in face of varying workloads. In this paper, we consider Reinforcement Learning based techniques in order to self-configure the number of parallel instances for a single DSP operator. Specifically, we propose two model-based approaches and compare them to the baseline Q-learning algorithm. Our numerical investigations show that the proposed solutions provide better performance and faster convergence than the baseline.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
