About
The MobileScale project is a research project led by Tian Guo at Worcester Polytechnic Institute. We seek to provide cost and performance efficient resource management for mobile workload that differs significantly from traditional cloud workload.
Our work is supported in part by NSF grants CNS-1755659 and CNS-1815619, and Google Cloud Platform Research credits.
You can find more projects by Cake Lab at our Homepage or our GitHub.
News
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17 Dec 2018 - The GC performance paper was accepted to VLDB’19! - Our paper on GC inefficiency for big data applications has been accepted at VLDB’19. This was a collaboration with ISCAS. See publications page for more details!
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09 Jul 2018 - Presenting at HotEdge’18 - Sam’s presenting our paper MODI at USENIX HotEdge’18 in Boston, MA. See publications page for more details!
Publications
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Speeding up Deep Learning with Transient Servers. Shijian Li, Robert J. Walls, Lijie Xu, Tian Guo. arXiv:1903.00045.
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Cost optimization through unifying multi-cloud resources. Benjamin C. Nickerson, Elsa Luthi, Synella Gonzales, and Tian Guo. The major qualifying project (MQP’18).
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An Experimental Evaluation of Garbage Collectors on Big Data Applications. Lijie Xu,Tian Guo, Wensheng Dou, Wei Wang, and Jun Wei. The 45th International Conference on Very Large Data Bases (VLDB’19).
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MODI: Mobile Deep Inference Made Efficient by Edge Computing. Samuel S. Ogden and Tian Guo. The USENIX Workshop on Hot Topics in Edge Computing (HotEdge ‘18).
Tutorials
- Cost optimization through unifying multi-cloud resources. Benjamin C. Nickerson, Elsa Luthi, Synella Gonzales, and Tian Guo. The major qualifying project (MQP’18).
Videos
- Cost optimization through unifying multi-cloud resources. Benjamin C. Nickerson, Elsa Luthi, Synella Gonzales, and Tian Guo. The major qualifying project (MQP’18).