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Mobius: unified messaging and data serving for mobile apps

机译:mobius:为移动应用程序提供统一消息和数据服务

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摘要

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.
机译:由于以下几个原因,移动应用程序的开发具有挑战性:间歇性和有限的网络连接,严格的电源限制,服务器端可伸缩性问题以及许多容错问题。开发人员可以手工设计复杂的解决方案,其中包括客户端缓存,冲突解决,断开连接容限和后端数据库分片。为了简化移动应用程序的开发,我们介绍了Mobius,该系统可解决移动应用程序开发在消息传递和数据管理方面的挑战。 Mobius引入了MUD(与数据统一消息)。 MUD提供了跨越设备和云的逻辑数据表的编程抽象。使用Mobius的应用程序可以异步读取MUD表/向MUD表写入数据,并且当通过连续查询表更改表时也可以接收通知。该系统结合了动态客户端缓存(基于多个应用程序的使用模式,在服务器端选择了智能策略),通知服务,灵活的查询处理以及可扩展且高度可用的云存储系统。我们提供了一个初始原型来演示我们设计的可行性。即使在我们最初的原型中,与手动调整的解决方案相比,远程读写延迟开销也不到52%。与固定策略相比,我们的动态缓存将消息数量减少了4到8.5倍,从而减少了延迟,带宽,功耗和服务器负载成本,同时还减少了数据陈旧性。

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