%0 Conference Paper %1 che21ITC33 %A Cheirmpos, Georgios %A Karaliopoulos, Merkouris %A Koutsopoulos, Iordanis %B 2021 33rd International Teletraffic Congress (ITC-33) %D 2021 %K Clustering_algorithms Costs Distance_measurement Internet Partitioning_algorithms Prediction_algorithms Sensitivity Shared_cellular_data_plans clustering itc itc33 mobile_data pricing %P 1-9 %T Optimizing shared data plans for mobile data access %U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc33/che21ITC33.pdf?inline=true %X The demand for mobile data has been steadily increasing over the last decade, forming an ever increasing portion of the overall Internet traffic. A considerable portion of this demand is served through capped cellular data plans that charge a fixed fee for data consumption respecting the cap and a typically higher penalty rate for additional consumption. Although shared data plans have been identified as a way to better utilize capacity that is paid for but not used, they are largely restricted to closed groups (e.g., family members) or the devices of a single user.In this paper we advocate the extension of shared data plans towards more open groups of users. We take the viewpoint of a platform that seeks to recommend optimal data plans to users subscribing to it and address the two main algorithmic tasks it faces: the partitioning of users to subscription groups and the selection of data plans that maximize their cost savings. We devise three algorithms that leverage clustering techniques. One of them addresses simultaneously the two tasks, whereas the other two decompose the problem and solve the two tasks sequentially. Our evaluation results suggest that the savings in subscription charges with shared plans are significant, ranging from 20% up to 80% of what users would pay with the cost-optimal individual data plans. They also highlight properties of the three algorithms and trade-offs they present involving the achieved cost savings, the intensity of under utilization and their sensitivity to deviations from the predicted users’ data consumption.

@inproceedings{che21ITC33, abstract = {The demand for mobile data has been steadily increasing over the last decade, forming an ever increasing portion of the overall Internet traffic. A considerable portion of this demand is served through capped cellular data plans that charge a fixed fee for data consumption respecting the cap and a typically higher penalty rate for additional consumption. Although shared data plans have been identified as a way to better utilize capacity that is paid for but not used, they are largely restricted to closed groups (e.g., family members) or the devices of a single user.In this paper we advocate the extension of shared data plans towards more open groups of users. We take the viewpoint of a platform that seeks to recommend optimal data plans to users subscribing to it and address the two main algorithmic tasks it faces: the partitioning of users to subscription groups and the selection of data plans that maximize their cost savings. We devise three algorithms that leverage clustering techniques. One of them addresses simultaneously the two tasks, whereas the other two decompose the problem and solve the two tasks sequentially. Our evaluation results suggest that the savings in subscription charges with shared plans are significant, ranging from 20% up to 80% of what users would pay with the cost-optimal individual data plans. They also highlight properties of the three algorithms and trade-offs they present involving the achieved cost savings, the intensity of under utilization and their sensitivity to deviations from the predicted users’ data consumption.}, added-at = {2022-02-04T14:01:50.000+0100}, author = {Cheirmpos, Georgios and Karaliopoulos, Merkouris and Koutsopoulos, Iordanis}, biburl = {https://www.bibsonomy.org/bibtex/2ceda70286f781f3f18652005ff682150/itc}, booktitle = {2021 33rd International Teletraffic Congress (ITC-33)}, interhash = {ae5c6873e551f08a5809c69f49463673}, intrahash = {ceda70286f781f3f18652005ff682150}, keywords = {Clustering_algorithms Costs Distance_measurement Internet Partitioning_algorithms Prediction_algorithms Sensitivity Shared_cellular_data_plans clustering itc itc33 mobile_data pricing}, month = Aug, pages = {1-9}, timestamp = {2022-02-04T14:01:50.000+0100}, title = {Optimizing shared data plans for mobile data access}, url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc33/che21ITC33.pdf?inline=true}, year = 2021 }