A Systematic and Open Exploration of FaaS Research

A Systematic and Open Exploration of FaaS Research

Research interest in Function-as-a-Service (FaaS) development, execution and ecosystems is growing. Consequently, an increasing body of literature focusing on FaaS and cloud services is evolving. While the field is still young, we propose a community-maintained and curated open dataset which uniquely references relevant articles in order to derive comparable bibliometric data and statistics. The dataset supports the generation of knowledge about the evolving history, research trends and significance. This survey paper introduces the 60-article dataset, explains the governance model and benefits, and shows first insights derived by a literature analysis. We argue that along with accelerating technological trends, fresh research method flavours assist in faster and more comprehensive knowledge exploration and dissemination.

Transcript

  1. Zürcher Fachhochschule A Systematic and Open Exploration of FaaS Research

    Mohammed Al-Ameen and Josef Spillner Service Prototyping Lab (blog.zhaw.ch/splab) + University of Sharjah, United Arab Emirates December 21, 2018 | ESSCA 2018, Zurich, CH
  2. 2 Traditional surveys 1)Identification of interesting field with insufficient knowledge

    1)e.g., FaaS/serverless computing 2)Scanning of literature 1)Selection, filtering, statistical methods 2)Creation of body of knowledge? replicability? 3)Reading selected works 4)Commonalities, differences, trends... 5)Writing survey paper 6)Go back to 1) FaaS: prime time for first surveys
  3. 3 Our approach: dataset SLL-base.json DOI SLL-bibliography.json populate.py analysischeck.py SLL-analysis.json

    SLL-technologies.json stats.py tagcloud.py venn.py venue.py DBLP other sources
  4. 4 Our approach: *collaborative* dataset

  5. 5 Our approach: *collaborative* dataset

  6. 6 Goal: insights

  7. 7 Producing the insights...

  8. 8 Insight: geographical distribution

  9. 9 Insight: authors and terms

  10. 10 Insight: term clouds

  11. 11 Insight: covered technologies * dark: author == developer

  12. 12 Insight: industry-academia mismatch * dark: experienced developers

  13. 13 More insights... Providers: • Lambda 328x • OpenWhisk 285x

    • OpenLambda 88x Terms: • function 2936x • coldstart <200x • stateless <200x • handler <200x
  14. 14 Future insights... Bibliometrics... • Most productive researchers? institutions? Timelines?

    • e.g. function workflows, inexistent at first... • configurations (e.g. 128 MB first, 8192 MB in 2020?) Open {data, source, experiment notebooks}?
  15. 15 Conclusion This work is: • not a survey, but

    rather a dataset with kind-of-survey as side effect • collaborative & long-lasting • hopefully insightful to you! Links: • https://github.com/serviceprototypinglab/serverless-literature-dataset • https://zenodo.org/record/1436432 • (https://zenodo.org/communities/serverless/)