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
   

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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
   

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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
   

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4. 4
   Our approach: *collaborative* dataset
   

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5. 5
   Our approach: *collaborative* dataset
   

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6. 6
   Goal: insights
   

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7. 7
   Producing the insights...
   

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8. 8
   Insight: geographical distribution
   

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9. 9
   Insight: authors and terms
   

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10. 10
    Insight: term clouds
    

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

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

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13. 13
    More insights...
    Providers:
    ●
    Lambda 328x
    ●
    OpenWhisk 285x
    ●
    OpenLambda 88x
    Terms:
    ●
    function 2936x
    ●
    coldstart <200x
    ●
    stateless <200x
    ●
    handler <200x
    

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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}?
    

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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/)
    

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