How Green Are Cloud Patterns? A Case Study of Energy Consumption. Abtahizadeh, S. A., Khomh, F., & Gu�h�neuc, Y. In Ren, K. & Melodia, T., editors, Proceedings of the 34<sup>th</sup> International Performance Computing and Communications Conference (IPCCC), pages 1–8, December, 2015. IEEE CS Press. 8 pages.
How Green Are Cloud Patterns? A Case Study of Energy Consumption [pdf]Paper  abstract   bibtex   
Cloud Patterns are abstract solutions to recurrent design problems in the cloud. Previous work has shown that these patterns can improve the Quality of Service (QoS) of cloud applications but their impact on energy consumption is still unknown. Yet, energy consumption is the biggest challenge that cloud computing systems (the backbone of today's high-tech economy) face today. In fact, 10% of the world's electricity is now being consumed by servers, laptops, tablets and smartphones. Energy consumption has complex dependencies on the hardware platform, and the multiple software layers. The hardware, its firmware, the operating system, and the various software components used by a cloud application, all contribute to determining the energy footprint. Hence, even though increasing a data center efficiency will eventually improve energy efficiency, the internal design of cloud-based applications can be improved to lower energy consumption. In this paper, we conduct an empirical study on a RESTful multi-threaded application deployed in the cloud, to investigate the individual and the combined impact of three cloud patterns (e.g., Local Database proxy, Local Sharding Based Router and Priority Queue) on the energy consumption of cloud based applications. We measure the energy consumption using Power-API; an application programming interface (API) written in Java to monitor the energy consumed at the processlevel. Results show that cloud patterns can effectively reduce the energy consumption of a cloud application, but not in all cases. In general, there appear to be a trade-off between an improved response time of the application and the energy consumption. Developers and software architects can make use of these results to guide their design decisions.

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