Abstract-Recent advances in mobile technologies have enabled a plethora of new applications. The hardware capabilities of mobile devices, however, are still insuf cient for real-time stream data processing (e.g., real-time video stream). In order to process real-time streaming data, most existing applications of oad the data and computation to a remote cloud service, such as Apache Storm or Apache Spark Streaming. Of oading streaming data, however, has high costs for users, e.g., signi cant service fees and battery consumption. To address these challenges, we design, implement and evaluate Mobile Storm, the rst stream processing platform for mobile clouds, leveraging clusters of local mobile devices to process real-time stream data. In Mobile Storm, we model the work ow of a real-time stream processing job and decompose it into several tasks so that the job can be executed concurrently and in a distributed manner on multiple mobile devices. Mobile Storm was implemented on Android phones and evaluated extensively through a real-time HD video processing application. The result shows that Mobile Storm effectively processes HD Video Stream in a mobile cloud, which would be impossible on a single mobile device.
There is also work that took advantage of cell phones to build mobile clouds called Mobile Storm REF , in which the work flow of a real-time stream processing job was modeled, then decomposed into several tasks so that the job can be executed concurrently and in a distributed manner on multiple mobile devices.
Mobile storm: Distributed real-time stream processing for mobile clouds
2015 IEEE 4th International Conference on Cloud Networking (CloudNet)
2015 IEEE 4th International Conference on Cloud Networking (CloudNet)
Computer Science