FML Framework: Function-Triggered ML-as-a-Service for IoT Cloud Applications

Shajulin Benedict; Rachit Verma; M. Bhagyalakshmi

doi:10.1007/978-981-19-1018-0_7

FML Framework: Function-Triggered ML-as-a-Service for IoT Cloud Applications

Shajulin Benedict, Rachit Verma, M. Bhagyalakshmi

Indian Institute of Information Technology Kottayam

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

IoT cloud applications, including social good applications such as air quality or water quality analysis/predictions, have increased in recent years with the emphasis on promoting real-time delivery of scalable services. The traditional approach of powering on cloud instances for the entire duration of IoT applications is no more an efficient solution. This paper proposed a function-triggered machine learning (FML) framework where ML algorithms that are hosted on cloud storage units are executed on cloud instances. Experiments were carried out at the IoT cloud Research laboratory using Amazon lambda services to trigger ML algorithms on EC2 instances. The results of the FML framework delivered a cost efficiency of over 27% for the ML services.

Original language	English
Title of host publication	Advances in Distributed Computing and Machine Learning - Proceedings of ICADCML 2022
Editors	Rashmi Ranjan Rout, Soumya Kanti Ghosh, Prasanta K. Jana, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Kuan-Ching Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	71-81
Number of pages	11
ISBN (Print)	9789811910173
DOIs	https://doi.org/10.1007/978-981-19-1018-0_7
State	Published - 2022
Externally published	Yes
Event	3rd International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2022 - Warangal, India Duration: 15 Jan 2022 → 16 Jan 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	427
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	3rd International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2022
Country/Territory	India
City	Warangal
Period	15/01/22 → 16/01/22

Keywords

Applications
Cloud services
IoT
ML-as-a-service
Serverless
Social good

Access to Document

10.1007/978-981-19-1018-0_7

Cite this

Benedict, S., Verma, R., & Bhagyalakshmi, M. (2022). FML Framework: Function-Triggered ML-as-a-Service for IoT Cloud Applications. In R. R. Rout, S. K. Ghosh, P. K. Jana, A. K. Tripathy, J. P. Sahoo, & K.-C. Li (Eds.), Advances in Distributed Computing and Machine Learning - Proceedings of ICADCML 2022 (pp. 71-81). (Lecture Notes in Networks and Systems; Vol. 427). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-1018-0_7

Benedict, Shajulin ; Verma, Rachit ; Bhagyalakshmi, M. / FML Framework : Function-Triggered ML-as-a-Service for IoT Cloud Applications. Advances in Distributed Computing and Machine Learning - Proceedings of ICADCML 2022. editor / Rashmi Ranjan Rout ; Soumya Kanti Ghosh ; Prasanta K. Jana ; Asis Kumar Tripathy ; Jyoti Prakash Sahoo ; Kuan-Ching Li. Springer Science and Business Media Deutschland GmbH, 2022. pp. 71-81 (Lecture Notes in Networks and Systems).

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title = "FML Framework: Function-Triggered ML-as-a-Service for IoT Cloud Applications",

abstract = "IoT cloud applications, including social good applications such as air quality or water quality analysis/predictions, have increased in recent years with the emphasis on promoting real-time delivery of scalable services. The traditional approach of powering on cloud instances for the entire duration of IoT applications is no more an efficient solution. This paper proposed a function-triggered machine learning (FML) framework where ML algorithms that are hosted on cloud storage units are executed on cloud instances. Experiments were carried out at the IoT cloud Research laboratory using Amazon lambda services to trigger ML algorithms on EC2 instances. The results of the FML framework delivered a cost efficiency of over 27% for the ML services.",

keywords = "Applications, Cloud services, IoT, ML-as-a-service, Serverless, Social good",

author = "Shajulin Benedict and Rachit Verma and M. Bhagyalakshmi",

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Benedict, S, Verma, R & Bhagyalakshmi, M 2022, FML Framework: Function-Triggered ML-as-a-Service for IoT Cloud Applications. in RR Rout, SK Ghosh, PK Jana, AK Tripathy, JP Sahoo & K-C Li (eds), Advances in Distributed Computing and Machine Learning - Proceedings of ICADCML 2022. Lecture Notes in Networks and Systems, vol. 427, Springer Science and Business Media Deutschland GmbH, pp. 71-81, 3rd International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2022, Warangal, India, 15/01/22. https://doi.org/10.1007/978-981-19-1018-0_7

FML Framework: Function-Triggered ML-as-a-Service for IoT Cloud Applications. / Benedict, Shajulin; Verma, Rachit; Bhagyalakshmi, M.
Advances in Distributed Computing and Machine Learning - Proceedings of ICADCML 2022. ed. / Rashmi Ranjan Rout; Soumya Kanti Ghosh; Prasanta K. Jana; Asis Kumar Tripathy; Jyoti Prakash Sahoo; Kuan-Ching Li. Springer Science and Business Media Deutschland GmbH, 2022. p. 71-81 (Lecture Notes in Networks and Systems; Vol. 427).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Benedict S, Verma R, Bhagyalakshmi M. FML Framework: Function-Triggered ML-as-a-Service for IoT Cloud Applications. In Rout RR, Ghosh SK, Jana PK, Tripathy AK, Sahoo JP, Li KC, editors, Advances in Distributed Computing and Machine Learning - Proceedings of ICADCML 2022. Springer Science and Business Media Deutschland GmbH. 2022. p. 71-81. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-19-1018-0_7