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

Bongjun Kim, Seonyeong Heo, Gyeongmin Lee, Seungbin Song, Jong Kim, Hanjun Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationCC 2019 - Proceedings of the 28th International Conference on Compiler Construction
EditorsMilind Kulkarni, J. Nelson Amaral
PublisherAssociation for Computing Machinery
Pages87-98
Number of pages12
ISBN (Electronic)9781450362771
DOIs
Publication statusPublished - 2019 Feb 16
Event28th International Conference on Compiler Construction, CC 2019 - Washington, United States
Duration: 2019 Feb 162019 Feb 17

Publication series

NameACM International Conference Proceeding Series

Conference

Conference28th International Conference on Compiler Construction, CC 2019
CountryUnited States
CityWashington
Period19/2/1619/2/17

Fingerprint

Fog
Servers
Actuators
Communication
Response time (computer systems)
Intelligent buildings
Sensors
Industrial plants
Internet of things

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Kim, B., Heo, S., Lee, G., Song, S., Kim, J., & Kim, H. (2019). Spinal code: Automatic code extraction for near-user computation in fogs. In M. Kulkarni, & J. N. Amaral (Eds.), CC 2019 - Proceedings of the 28th International Conference on Compiler Construction (pp. 87-98). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3302516.3307356
Kim, Bongjun ; Heo, Seonyeong ; Lee, Gyeongmin ; Song, Seungbin ; Kim, Jong ; Kim, Hanjun. / Spinal code : Automatic code extraction for near-user computation in fogs. CC 2019 - Proceedings of the 28th International Conference on Compiler Construction. editor / Milind Kulkarni ; J. Nelson Amaral. Association for Computing Machinery, 2019. pp. 87-98 (ACM International Conference Proceeding Series).
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Kim, B, Heo, S, Lee, G, Song, S, Kim, J & Kim, H 2019, Spinal code: Automatic code extraction for near-user computation in fogs. in M Kulkarni & JN Amaral (eds), CC 2019 - Proceedings of the 28th International Conference on Compiler Construction. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 87-98, 28th International Conference on Compiler Construction, CC 2019, Washington, United States, 19/2/16. https://doi.org/10.1145/3302516.3307356

Spinal code : Automatic code extraction for near-user computation in fogs. / Kim, Bongjun; Heo, Seonyeong; Lee, Gyeongmin; Song, Seungbin; Kim, Jong; Kim, Hanjun.

CC 2019 - Proceedings of the 28th International Conference on Compiler Construction. ed. / Milind Kulkarni; J. Nelson Amaral. Association for Computing Machinery, 2019. p. 87-98 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Kim B, Heo S, Lee G, Song S, Kim J, Kim H. Spinal code: Automatic code extraction for near-user computation in fogs. In Kulkarni M, Amaral JN, editors, CC 2019 - Proceedings of the 28th International Conference on Compiler Construction. Association for Computing Machinery. 2019. p. 87-98. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3302516.3307356