An empirical characterization of IFTTT: Ecosystem, usage, and performance

Xianghang Mi, Ying Zhang, Feng Qian, Xiaofeng Wang

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

32 Scopus citations


IFTTT is a popular trigger-action programming platform whose applets can automate more than 400 services of IoT devices and web applications. We conduct an empirical study of IFTTT using a combined approach of analyzing data collected for 6 months and performing controlled experiments using a custom testbed. We profile the interactions among different entities, measure how applets are used by end users, and test the performance of applet execution. Overall we observe the fast growth of the IFTTT ecosystem and its increasing usage for automating IoT-related tasks, which correspond to 52% of all services and 16% of the applet usage. We also observe several performance inefficiencies and identify their causes.

Original languageEnglish (US)
Title of host publicationIMC 2017 - Proceedings of the 2017 Internet Measurement Conference
PublisherAssociation for Computing Machinery
Number of pages7
ISBN (Electronic)9781450351188
StatePublished - Nov 1 2017
Externally publishedYes
Event2017 ACM Internet Measurement Conference, IMC 2017 - London, United Kingdom
Duration: Nov 1 2017Nov 3 2017

Publication series

NameProceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
VolumePart F131937


Other2017 ACM Internet Measurement Conference, IMC 2017
CountryUnited Kingdom


  • IoT
  • Measurement

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