Investigating Energy Consumption and Performance Trade-off for Interactive Cloud Application

Abstract : With the ever growing demand and popularity of cloud based services, data centers have to urgently face energy consumption issue. Similar to other large consumers of power, data centers find themselves increasingly pressured to reduce their carbon footprint. In response, cloud providers have started to set sustainability goals to reduce carbon emissions by using renewable sources to their services. Traditionally, batch processing cloud applications are deadline oriented, hence can be easily adapted with the different green energy profile. Whereas, interactive cloud applications are imposed with several performance criteria. This paper, the first of its kind, investigates a thorough analysis of energy consumption and performance trade-off by allowing smart usage of green energy for interactive cloud application. Moreover, we propose an auto-scaler, named as SaaScaler, that implements several control loop based application controllers to satisfy different performance (i.e., response time, availability and user experience) and resource aware metrics (i.e., quality of energy). Based on extensive experiments with RUBiS benchmark and real workload traces using single compute node in Openstack/Grid’5000, results suggest that 13% brown energy consumption can be reduced without deprovisioning any physical or virtual resources at IaaS layer while 29% more users can access the application by dynamically adjusting capacity requirements. Furthermore, our investigation verifies that, the energy consumption deviates as little as .07% when our approach is scaled using several physical nodes.
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Contributor : Thomas Ledoux <>
Submitted on : Thursday, June 15, 2017 - 6:25:45 PM
Last modification on : Friday, September 13, 2019 - 9:51:33 AM



Md Sabbir Hasan, Frederico Alvares, Thomas Ledoux, Jean-Louis Pazat. Investigating Energy Consumption and Performance Trade-off for Interactive Cloud Application. IEEE Transactions on Sustainable Computing, IEEE, 2017, 2 (2), pp.113 - 126. ⟨10.1109/TSUSC.2017.2714959⟩. ⟨hal-01540159⟩



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