TY - GEN
T1 - Throughput-fairness tradeoffs in mobility platforms
AU - Balasingam, Arjun
AU - Gopalakrishnan, Karthik
AU - Mittal, Radhika
AU - Arun, Venkat
AU - Saeed, Ahmed
AU - Alizadeh, Mohammad
AU - Balakrishnan, Hamsa
AU - Balakrishnan, Hari
N1 - Funding Information:
We thank Songtao He, Favyen Bastani, Sam Madden, our shepherd, and the anonymous MobiSys reviewers for their helpful discussions and thoughtful feedback. This research was supported in part by the NSF under Graduate Research Fellowship grant #2389237. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The NASA University Leadership Initiative (grant #80NSSC20M0163) provided funds to assist the authors with their research, but this article solely reflects the opinions and conclusions of its authors and not any NASA entity.
Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/6/24
Y1 - 2021/6/24
N2 - This paper studies the problem of allocating tasks from different customers to vehicles in mobility platforms, which are used for applications like food and package delivery, ridesharing, and mobile sensing. A mobility platform should allocate tasks to vehicles and schedule them in order to optimize both throughput and fairness across customers. However, existing approaches to scheduling tasks in mobility platforms ignore fairness. We introduce Mobius, a system that uses guided optimization to achieve both high throughput and fairness across customers. Mobius supports spatiotemporally diverse and dynamic customer demands. It provides a principled method to navigate inherent tradeoffs between fairness and throughput caused by shared mobility. Our evaluation demonstrates these properties, along with the versatility and scalability of Mobius, using traces gathered from ridesharing and aerial sensing applications. Our ridesharing case study shows that Mobius can schedule more than 16,000 tasks across 40 customers and 200 vehicles in an online manner.
AB - This paper studies the problem of allocating tasks from different customers to vehicles in mobility platforms, which are used for applications like food and package delivery, ridesharing, and mobile sensing. A mobility platform should allocate tasks to vehicles and schedule them in order to optimize both throughput and fairness across customers. However, existing approaches to scheduling tasks in mobility platforms ignore fairness. We introduce Mobius, a system that uses guided optimization to achieve both high throughput and fairness across customers. Mobius supports spatiotemporally diverse and dynamic customer demands. It provides a principled method to navigate inherent tradeoffs between fairness and throughput caused by shared mobility. Our evaluation demonstrates these properties, along with the versatility and scalability of Mobius, using traces gathered from ridesharing and aerial sensing applications. Our ridesharing case study shows that Mobius can schedule more than 16,000 tasks across 40 customers and 200 vehicles in an online manner.
KW - aerial sensing
KW - mobility platforms
KW - optimization
KW - resource allocation
KW - ridesharing
KW - vehicle routing
UR - http://www.scopus.com/inward/record.url?scp=85110159560&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85110159560&partnerID=8YFLogxK
U2 - 10.1145/3458864.3467881
DO - 10.1145/3458864.3467881
M3 - Conference contribution
AN - SCOPUS:85110159560
T3 - MobiSys 2021 - Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services
SP - 363
EP - 375
BT - MobiSys 2021 - Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services
PB - Association for Computing Machinery, Inc
T2 - 19th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2021
Y2 - 24 June 2021 through 2 July 2021
ER -