Energy Consumption Comparison of Interactive Cloud-Based and Local Applications

Interactive cloud computing and cloud-based applications are a rapidly growing sector of the expanding digital economy because they provide access to advanced computing and storage services via simple, compact personal devices. Recent studies have suggested that processing a task in the cloud is more energy-efficient than processing the same task locally. However, these studies have generally ignored the power consumption of the network and end-user devices when accessing the cloud. In this paper, we develop a power consumption model for interactive cloud applications that includes the power consumption of end-user devices and the influence of the applications on the power consumption of the various network elements along the path between the user and the cloud data centre. As examples, we apply our model to Google Drive and Microsoft Skydrive's word processing, presentation and spreadsheet interactive applications. We demonstrate via extensive packet-level traffic measurements that the volume of traffic generated by a session of the application vastly exceeds the amount of data keyed in by the user. This has important implications on the overall power consumption of the service. We show that using the cloud to perform certain tasks consumes more power (by a watt to 10 watts depending on the scenario) than performing the same tasks locally on a low-power consuming computer and a tablet.

[1]  Tansu Alpcan,et al.  Energy consumption of interactive cloud-based document processing applications , 2013, 2013 IEEE International Conference on Communications (ICC).

[2]  Daniel R. Williams,et al.  Impact of office productivity cloud computing on energy consumption and greenhouse gas emissions. , 2013, Environmental science & technology.

[3]  Rajkumar Buyya,et al.  Introduction to Cloud Computing , 2011, CloudCom 2011.

[4]  Mufajjul Ali,et al.  Green Cloud on the Horizon , 2009, CloudCom.

[5]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[6]  Liang Liu,et al.  GreenCloud: a new architecture for green data center , 2009, ICAC-INDST '09.

[7]  Bu-Sung Lee,et al.  GMoCA: Green mobile cloud applications , 2012, 2012 First International Workshop on Green and Sustainable Software (GREENS).

[8]  Bharat K. Bhargava,et al.  A Survey of Computation Offloading for Mobile Systems , 2012, Mobile Networks and Applications.

[9]  Daniel Kharitonov,et al.  Time-Domain Approach to Energy Efficiency: High-Performance Network Element Design , 2009, 2009 IEEE Globecom Workshops.

[10]  Odlyzko Andrew Data Networks are Lightly Utilized, and Will Stay That Way , 1999 .

[11]  Karim Habak,et al.  COSMOS: computation offloading as a service for mobile devices , 2014, MobiHoc '14.

[12]  Rodney S. Tucker,et al.  Modeling Energy Consumption in High-Capacity Routers and Switches , 2014, IEEE Journal on Selected Areas in Communications.

[13]  Y. Jading,et al.  INFSO-ICT-247733 EARTH Deliverable D 2 . 3 Energy efficiency analysis of the reference systems , areas of improvements and target breakdown , 2012 .

[14]  Rodney S. Tucker,et al.  Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport , 2011, Proceedings of the IEEE.

[15]  J. Wenny Rahayu,et al.  Mobile cloud computing: A survey , 2013, Future Gener. Comput. Syst..

[16]  J. Dale Prince,et al.  Introduction to Cloud Computing , 2011 .