Dynamic QoS Optimization Architecture for Cloud-based DDDAS

Abstract

An emerging class of Dynamic Data Driven application systems heavily depends on cloud and Big Data. We refer to this class of DDDAS as cloud-based DDDAS. Despite the growing interest in marrying DDDAS with the cloud, there is a general lack for architectural frameworks explicating the cloud requirements, which can support cloud-based DDDAS. Given the unpredictable, dynamic and on-demand nature of the cloud, cloud-based DDDAS requires novel approaches for dynamic Quality of Service (QoS) optimization. This is important for providing timely and reliable predictions and for ensuring higher dependability in the solution, as it would be unrealistic to assume that optimal QoS can be achieved at design time. We propose a decentralized architectural style for cloud-based DDDAS, where dynamic QoS optimization is in the heart of the symbiotic adaptation. The architecture leverages on the classical DDDAS primitives to reach a refined decentralized style suited for the dynamic requirements of the cloud. We formulate the QoS optimization problem as a dynamic multi-objective optimization problem. We use a scenario to exemplify and evaluate the effectiveness of the style.