Cost-Aware Query Optimization during Cloud-Based Complex Event Processing

Abstract

Complex Event Processing describes the problem of timely and continuous processing of event streams. The load of Complex Event Processing systems can vary (e.g., event rates). Static resource provision leads to higher monetary costs because enough resources have to be provided to efficiently handle peak loads. Therefore, most of the time the resources will not be fully utilized. One way to achieve scalable processing and elastical resource allocation fitting varying requirements is to use Cloud Computing. Properties of Cloud Computing are the pay-as-you-go-payment model and high availability. These properties can be used in Complex Event Processing systems to minimize the monetary costs of systems while satisfying Service Level Agreements. Complex Event Processing systems must continuously optimize the event processing to adapt to varying loads without violation of Service Level Agreements. To guarantee efficiency, the optimization cost must be considered, leading to cost savings without violating the Service Level Agreements. In this work, we discuss factors, which should be considered during the optimization of cloud-based Complex Event Processing systems that use the pause-train-resume strategy to migrate operators. Furthermore, we propose heuristics to estimate the cost of these factors. In our experiments, the cost could be decreased by 15 \% by using a cost-aware optimizer. This proofs that the costs of cloud-based Complex Event Processing systems can be further decreased if optimization is cost-aware.