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AMUSE: Empowering Users for Cost-Aware Offloading with Throughput-Delay Tradeoffs
2016
IEEE Transactions on Mobile Computing
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Users face a complex, multi-dimensional tradeoff between cost, throughput, and delay in making their offloading decisions: while they may save money and receive a higher throughput by waiting for WiFi ...
Thus, AMUSE users can optimize their bandwidth rates according to their own cost-throughput-delay tradeoff without relying on support from different apps' content servers. ...
To fully exploit the benefits of WiFi offloading, a user must balance these competing cost, throughput quality, and delay tradeoffs for different applications. ...
doi:10.1109/tmc.2015.2456881
fatcat:s4bz3ovauvarxe3osxcev5ccju
AMUSE: Empowering users for cost-aware offloading with throughput-delay tradeoffs
2013
2013 Proceedings IEEE INFOCOM
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Mobile users face a tradeoff between cost, throughput, and delay in making their offloading decisions. ...
To navigate this tradeoff, we propose AMUSE (Adaptive bandwidth Management through USer-Empowerment), a practical, costaware WiFi offloading system that takes into account a user's throughput-delay tradeoffs ...
To fully exploit the benefits of WiFi offloading, a user must balance these competing cost, throughput quality, and delay tradeoffs for different applications. ...
doi:10.1109/infcom.2013.6566810
dblp:conf/infocom/ImJHSKC13
fatcat:tcvwgs6g5bcdnc3c34qkvugueq
Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption
[article]
2019
arXiv
pre-print
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2017 pre-print arXiv:1711.03771v1 fatcat:idoyk6cwwzgcro7csspoitwf74
This paper investigates the power minimization problem for the mobile devices by data offloading in multi-cell multi-user OFDMA mobile edge computing networks. ...
A promising technique to provide mobile applications with high computation resources is to offload the processing task to the cloud. ...
Chiang et al., “Amuse: Empowering users for cost-aware offloading with throughput-
delay tradeoffs,” IEEE Transactions on Mobile Computing, vol. 15, no. 5, pp. 1062–1076, 2016.
[23] X. Chen, L. ...
arXiv:1711.03771v2
fatcat:7i5tvealv5fmlptlzdaehpryha
Machine Learning for 5G Mobile Networks: a Pragmatic Essay on where, how and why
[chapter]
2019
Zenodo
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We follow a pragmatic approach, leveraging our recent research work, which encompasses algorithms for energy efficiency, Quality of Service (QoS) enhancements and the use of inference tools for the analysis ...
Our objective is to demonstrate where (which applications), how (in combination with which techniques) andwhy (the benefits) ML may be useful, with an emphasis on the how, i.e., what are the usage models ...
to assess which baseband functions (or part of) are to be executed locally, and which ones shall be offloaded to network servers to strike the right balance between processing delay and overall energy ...
doi:10.5281/zenodo.3675362
fatcat:kkzk6ho4urcv3igzs3233yzzei
Improving trust in cloud, enterprise, and mobile computing platforms
[article]
2013
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For mobile platforms, we present the Trusted Language Runtime (TLR), a software system for hosting mobile apps with stringent security needs (e.g., e-wallet). ...
In addition to the close collaboration with my advisor, this thesis is the fruit of teamwork with other people. ...
Our findings show that the sealing cost grows linearly with the number of attributes. The cost of sealing for a policy with 10 attributes was about 128 milliseconds. ...
doi:10.22028/d291-25366
fatcat:lprbelewircbfnerabtey2ua2y
Organic Computing
2024
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Data augmentation techniques are employed for stable learning. Preliminary results demonstrate promising potential for classifying and forecasting physiological signals in plants. ...
Martin Tröschel, who came up with ideas for the observer implementation and helped a lot by supervising the work. Additionally, the author would like to thank Prof. Dr. ...
I thank Heiko Hamann, Professor for Cyber-physical Systems, University of Konstanz for his ideas and comments that greatly improved the manuscript. ...
doi:10.17170/kobra-202402269661
fatcat:a6eospr2kjcp5gzfa2dj2n7yje