Mobius: unified messaging and data serving for mobile apps
Name
Madden_Mobius unified.pdf
Size
1.33 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Chun, Byung-Gon
Curino, Carlo
Sears, Russell
Shraer, Alexander
Madden, Samuel R.
Ramakrishnan, Raghu
Date Issued
June 2012
Journal
Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services - MobiSys '12
Publisher
Association for Computing Machinery
Citation
Chun, Byung-Gon, Carlo Curino, Russell Sears, Alexander Shraer, Samuel Madden, and Raghu Ramakrishnan. “Mobius: unified messaging and data serving for mobile apps.” Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services - MobiSys ’12 (2012), June 25–29, 2012, Low Wood Bay, Lake District, UK. ACM New York, NY, USA, p.141-154.
Version
Author's final manuscript
Abstract
Mobile application development is challenging for several reasons: intermittent and limited network connectivity, tight power constraints, server-side scalability concerns, and a number of fault-tolerance issues. Developers handcraft complex solutions that include client-side caching, conflict resolution, disconnection tolerance, and backend database sharding. To simplify mobile app development, we present Mobius, a system that addresses the messaging and data management challenges of mobile application development. Mobius introduces MUD (Messaging Unified with Data). MUD presents the programming abstraction of a logical table of data that spans devices and clouds. Applications using Mobius can asynchronously read from/write to MUD tables, and also receive notifications when tables change via continuous queries on the tables. The system combines dynamic client-side caching (with intelligent policies chosen on the server-side, based on usage patterns across multiple applications), notification services, flexible query processing, and a scalable and highly available cloud storage system. We present an initial prototype to demonstrate the feasibility of our design. Even in our initial prototype, remote read and write latency overhead is less than 52% when compared to a hand-tuned solution. Our dynamic caching reduces the number of messages by a factor of 4 to 8.5 when compared to fixed strategies, thus reducing latency, bandwidth, power, and server load costs, while also reducing data staleness.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2307636.2307650
