Smart scene management for IoT-based constrained devices using checkpointing

Typical devices of the Internet of Things are usually under-powered, and have limited RAM. This is due to energy and cost concerns. Yet, IoT applications require increasingly complex programs with increasingly large amounts of data. In principle, an application could manage the increasing data within the limited RAM by saving and loading data from the file system as needed. But managing the use of RAM in this way is both time-consuming and error-prone for the code developer. We propose instead a novel architecture in which different semantic scenes are implemented as independent operating system processes. As the need arises to switch from one scene to another, the currently running process, which represents the current scene, is checkpointed and a process representing the new scene is restarted from a checkpoint image. This solution employs checkpointing to provide a simpler framework for the end programmer, while at the same time resulting in higher performance. For example, experiments show that restarting an old process from a checkpoint image is about 25 times faster than starting a new process. When using an mmap-based optimization (deferring the paging in of virtual memory pages until runtime), restarting an old process is about 500 times faster. Overall, checkpoint and restart each execute in less than 0.2 seconds on a Raspberry Pi B.