Recently, edge servers located closer than the cloud have become expected for the purpose of processing the large amount of sensor data generated by IoT devices such as robots. Research has been proposed to improve responsiveness as a cache server by applying KVS (Key-Value Store) to the edge as a method for obtaining high responsiveness. Above all, a hybrid-KVS server that uses both DRAM and NVMM (NonVolatile Main Memory) devices is expected to achieve both responsiveness and reliability. However, its effectiveness has not been verified in actual applications, and its effectiveness is not clear in terms of its relationship with the cloud. The purpose of this study is to evaluate the effectiveness of hybrid-KVS servers using the SLAM (Simultaneous Localization and Mapping), which is a widely used application in robots and autonomous driving. It is appropriate for applying an edge server and requires responsiveness and reliability. SLAM is generally implemented on ROS (Robot Operating System) middleware and communicates with the server through ROS middleware. However, if we use hybrid-KVS on the edge with the SLAM and ROS, the communication could not be achieved since the message objects are different from the format expected by KVS. Therefore, in this research, we propose a mechanism to apply the ROS memory object to hybrid-KVS by designing and implementing the data serialization function to extend ROS. As a result of the proposed fogcached-ros and evaluation, we confirm the effectiveness of low API overhead, support for data used by SLAM, and low latency difference between the edge and cloud.
- Edge computing
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence