Spinal code: automatic code extraction for near-user computation in fogs

Abstract

In the Internet of Things (IoT) environments, cloud servers integrate various IoT devices including sensors and actuators, and provide new services that assist daily lives of users interacting with the physical world. While response time is a crucial factor of quality of the services, supporting short response time is challenging for the cloud servers due to a growing number and amount of connected devices and their communication. To reduce the burden of the cloud servers, fog computing is a promising alternative to offload computation and communication overheads from the cloud servers to fog nodes. However, since existing fog computing frameworks do not extract codes for fog nodes fully automatically, programmers should manually write and analyze their applications for fog computing. This work proposes Spinal Code, a new compiler-runtime framework for near-user computation that automatically partitions an original cloud-centric program into distributed sub-programs running over the cloud and fog nodes. Moreover, to reduce response time in the physical world, Spinal Code allows programmers to annotate latency sensitive actuators in a program, and optimizes the critical paths from required sensors to the actuators when it generates the sub-programs. This work implements 9 IoT programs across 4 service domains: healthcare, smart home, smart building and smart factory, and demonstrates that Spinal Code successfully reduces 44.3% of response time and 79.9% of communication on the cloud compared with a cloud-centric model.