Modelling Energy-Aware Task Allocation in Mobile Workflows

Gao, Bo; He, Ligang

doi:10.1007/978-3-319-11569-6_8

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…There are the existing research studies [3,4,5] to investigate offloading strategies and frameworks. The research on the first aspect mainly focuses on the theoretical analysis of better offloading strategies [6,7,8,9], while the research on the latter aspect focuses on developing the application development frameworks that enable the applications to have an offloading capability.…”

Section: Related Workmentioning

confidence: 99%

Developing Offloading-Enabled Application Development Frameworks for Android Mobile Devices

Xia¹,

Wang

et al. 2018

2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference On

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Section: Related Workmentioning

confidence: 99%

Developing Offloading-Enabled Application Development Frameworks for Android Mobile Devices

Xia¹,

Wang

et al. 2018

2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference On

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“…For WLAN, we expect no dependance on cycle interval. In order to verify this, the experiment runner executed diffsync with different cycle intervals for 3G, GSM and WLAN network connection: The cycle intervals were 0, 1, 2, 3, 5, 6,7,8,9,10,11,12,13,20,30,60 seconds. The downlink bandwidths were 168,2 kbit/s for GSM, 2,4 mbit/s for 3G, and 2,8 mbit/s for WLAN averaged over all runs.…”

Section: Experiments Setupmentioning

confidence: 99%

“…This model has influenced other research e.g. for optimising computational complexity on the mobile device by offloading computation to the cloud [6,12]. Network energy consumption can sometimes be optimised by choosing suitably between push and pull communication paradigm, as has been discussed in [3] in connection with the Google cloud messaging system.…”

Section: Energy Optimisation On Mobile Devicesmentioning

confidence: 99%

Analysis of Differential Synchronisation’s Energy Consumption on Mobile Devices

Simon

Schmidt

Pammer-Schindler³

2017

EAI Endorsed Transactions on Collaborative Computing

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Synchronisation algorithms are central to collaborative editing software. As collaboration is increasingly mediated by mobile devices, the energy efficiency for such algorithms is interest to a wide community of application developers. In this paper we explore the differential synchronisation (diffsync) algorithm with respect to energy consumption on mobile devices. Discussions within this paper are based on real usage data of PDF annotations via the Mendeley iOS app, which requires realtime synchronisation. We identify three areas for optimising diffsync: a.) Empty cycles in which no changes need to be processed b.) tail energy by adapting cycle intervals and c.) computational complexity. Following these considerations, we propose a push-based diffsync strategy in which synchronisation cycles are triggered when a device connects to the network or when a device is notified of changes.

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“…Lin et al [7] proposed a task scheduling scheme that minimizes the energy consumption of mobile devices. An energy-aware dynamic task offloading algorithm for mobile cloud computing was also considered in [8]. Although such related work considers energy-efficient application offloading in cloudassisted mobile computing, holistic energy consumption optimization is largely ignored.…”

Section: Introductionmentioning

confidence: 99%