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D. Moldovan, H. Truong, S. Dustdar:
"Cost-Aware Scalability of Applications in Public Clouds";
Vortrag: IEEE International Conference on Cloud Engineering, IC2E 2016, Berlin, Germany; 04.04.2016 - 08.04.2016; in: "Proceedings of the IEEE International Conference on Cloud Engineering, IC2E 2016", IEEE Computer Society, (2016), ISBN: 978-1-5090-1961-8; S. 79 - 88.

Scalable applications deployed in public clouds can be built from a combination of custom software components and public cloud services. To meet performance and/or cost requirements, such applications can scale-out/in their components during run-time. When higher performance is required, new component instances can be deployed on newly allocated cloud services (e.g., virtual machines). When the instances are no longer needed, their services can be deallocated to decrease cost. However, public cloud services are usually billed over predefined time and/or usage intervals, e.g., per hour, per GB of I/O. Thus, it might not be cost efficient to scale-in public cloud applications at any moment in time, without considering their billing cycles. In this work we aid developers of scalable applications for public clouds to monitor their costs, and develop cost-aware scalability controllers. We introduce a model for capturing the pricing schemes of cloud services. Based on the model we determine and evaluate the application's costs depending on its used cloud services and their billing cycles. We further evaluate cost efficiency of cloud applications, analyzing which application component is cost efficient to deallocate and when. We evaluate our approach on a scalable platform for IoT, deployed in Flexiant1, one of the leading European public cloud providers. We show that cost-aware scalability can achieve higher application stability and performance, while reducing its operation costs.

Cloud, Scalability, Run-Time Control, Cost Efficiency, Elasticity

http://dx.doi.org/10.1109/IC2E.2016.23

Projektleitung Schahram Dustdar:
Automatic Elasticity Provisioning Platform for Cloud Applications