Online parameter optimization for elastic data stream processing
Thomas Heinze, Lars Roediger, Andreas Meister, Yuanzhen Ji, Zbigniew Jerzak, Christof W. Fetzer · 2015
Elastic scaling allows data stream processing systems to dynamically scale in and out to react to workload changes. As a consequence, unexpected load peaks can be handled and the extent of the overprovisioning can be reduced. However, the strategies used for elastic scaling of such systems need to be tuned manually by the user. This is an error prone and cumbersome task, because it requires a detailed knowledge of the underlying system and workload characteristics. In addition, the resulting quality of service for a specific scaling strategy is unknown a priori and can be measured only during runtime.