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Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms

Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms

Presentation slides of our paper "Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms" at MOBILESoft 2021.
Presentation: https://youtu.be/91b3juLFbeU

Yixue Zhao

May 05, 2021
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  1. Assessing the Feasibility of
    Web-Request Prediction Models on
    Mobile Platforms
    Yixue Zhao1, Siwei Yin2, Adriana Sejfia3, Marcelo Schmitt Laser3,
    Haoyu Wang2, Nenad Medvidović3
    MOBILESoft 2021, Virtual Event
    1 2 3

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  2. PhD
    Thesis
    How to speed up
    mobile apps
    using prefetching?
    2
    Reducing User-Perceived
    Latency in Mobile Apps via
    Prefetching and Caching
    Yixue Zhao
    tinyurl.com/yixuedissertation

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  3. History-based
    Prefetching
    ▪ Input:
    user’s historical requests
    ▪ Method:
    prediction model
    ▪ Output:
    user’s future request(s)
    3

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  4. History-based
    Prefetching
    ▪ Input:
    user’s historical requests
    ▪ Method:
    prediction model
    ▪ Output:
    user’s future request(s)
    4
    Biggest
    Challenge!

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  5. Why
    no dataset?
    5
    Privacy!

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  6. Public
    Dataset: LiveLab
    6
    Ref: Shepard et al. LiveLab: measuring wireless
    networks and smartphone users in the field.
    SIGMETRICS Performance Evaluation Review. 2011
    ▪ Subject:
    25 iPhone users
    ▪ Size:
    an entire year
    ▪ Time:
    2011 (a decade ago)

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  7. ▪ Subject:
    25 iPhone users
    ▪ Size:
    an entire year
    ▪ Time:
    2011 (a decade ago)
    Public
    Dataset: LiveLab
    7

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  8. ▪ Subject:
    25 iPhone users
    ▪ Size:
    an entire year
    ▪ Time:
    2011 (a decade ago)
    Public
    Dataset
    8
    Small
    models!

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  9. Easier said than done…
    9

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  10. ICSE 2018, Gothenburg, Sweden
    10
    Co-author to-
    be: Haoyu
    PhD advisor:
    Neno
    Me after my
    ICSE talk

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  11. We got
    data!
    after tons of paper work, back and
    forth, ethical considerations etc…
    11

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  12. LiveLab
    ▪ An entire year
    LiveLab vs.
    Our dataset
    Our Dataset
    ▪ A random day (24hrs)
    12
    400X shorter time

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  13. LiveLab
    ▪ An entire year
    ▪ 25 iPhone-using undergraduates
    at Rice university
    LiveLab vs.
    Our dataset
    Our Dataset
    ▪ A random day (24hrs)
    ▪ 10K+ diverse mobile users
    at BUPT university
    13
    400X more users

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  14. LiveLab
    ▪ An entire year
    ▪ 25 iPhone-using undergraduates
    at Rice university
    ▪ Hire participants
    LiveLab vs.
    Our dataset
    Our Dataset
    ▪ A random day (24hrs)
    ▪ 10K+ diverse mobile users
    at BUPT university
    ▪ No contact with users
    14

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  15. 3 Research
    Questions
    Possibility?
    Do small prediction
    models work?
    à repetitive
    requests
    Existing solution?
    Can we reuse
    existing algorithms?
    à accuracy of DG,
    PPM, MP, Naïve
    (baseline)
    Even smaller?
    Can we reduce
    training size even
    more?
    à good & enough
    training data
    15

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  16. 3 Research
    Questions
    Possibility?
    Do small prediction
    models work?
    à repetitive
    requests
    Existing solution?
    Can we reuse
    existing algorithms?
    à accuracy of DG,
    PPM, MP, Naïve
    (baseline)
    Even smaller?
    Can we reduce
    training size even
    more?
    à good & enough
    training data
    16

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  17. 3 Research
    Questions
    Possibility?
    Do small prediction
    models work?
    à repetitive
    requests
    Existing solution?
    Can we reuse
    existing algorithms?
    à accuracy of DG,
    PPM, MP, Naïve
    (baseline)
    Even smaller?
    Can we reduce
    training size even
    more?
    à good & enough
    training data
    17

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  18. HiPHarness framework
    18

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  19. HiPHarness framework
    19
    Per user
    15 million requests
    à 7 million models
    Prediction accuracy

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  20. Results
    of 7+ million models for individual users
    20

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  21. Results (RQ2)
    21
    Existing solution?
    Can we reuse
    existing algorithms?
    à accuracy of DG,
    PPM, MP, Naïve
    (baseline)

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  22. Results (RQ2)
    22
    Existing solution?
    Can we reuse
    existing algorithms?
    à accuracy of DG,
    PPM, MP, Naïve
    (baseline)

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  23. Results (RQ2)
    23
    Existing solution?
    Can we reuse
    existing algorithms?
    à accuracy of DG,
    PPM, MP, Naïve
    (baseline)
    MP Static Precision: 0.16
    [Wang et al. WWW’12]

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  24. Results (RQ2)
    24
    Existing solution?
    Can we reuse
    existing algorithms?
    à accuracy of DG,
    PPM, MP, Naïve
    (baseline)
    MP Static Precision: 0.16
    [Wang et al. WWW’12]
    Small
    models
    are
    promising!

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  25. Results (RQ2)
    25
    Existing solution?
    Can we reuse
    existing algorithms?
    à accuracy of DG,
    PPM, MP, Naïve
    (baseline)
    Static Precision: 0.478
    [Zhao et al. ICSE’18]

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  26. Results (RQ2)
    26
    Existing solution?
    Can we reuse
    existing algorithms?
    à accuracy of DG,
    PPM, MP, Naïve
    (baseline)
    Static Precision: 0.478
    [Zhao et al. ICSE’18]
    Existing
    algorithms
    are
    promising!

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  27. Even smaller?
    Can we reduce
    training size even
    more?
    à good & enough
    training data
    Results (RQ3)
    27

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  28. Results (RQ3)
    28
    Even smaller?
    Can we reduce
    training size even
    more?
    à good & enough
    training data

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  29. Results (RQ3)
    29
    Even smaller?
    Can we reduce
    training size even
    more?
    à good & enough
    training data
    200 400 600 800 1000
    Static Precision trend w.r.t. #request

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  30. Results (RQ3)
    30
    Even smaller?
    Can we reduce
    training size even
    more?
    à good & enough
    training data
    200 400 600 800 1000
    Static Precision trend w.r.t. #request
    Cut-off point?

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  31. Results (RQ3)
    31
    ▪ Sliding-Window approach to explore cut-off points
    ▪ 11 window sizes (50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000)
    ▪ ANOVA post-hoc test (pair-wise comparison)

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  32. Results (RQ3)
    32
    ▪ Sliding-Window approach to explore cut-off points
    ▪ 11 window sizes (50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 10,000)
    ▪ ANOVA post-hoc test (pair-wise comparison)

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  33. Results (RQ3)
    33
    ▪ Sliding-Window approach to explore cut-off points
    ▪ 11 window sizes (50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 10,000)
    ▪ ANOVA post-hoc test (pair-wise comparison)

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  34. Takeaways
    34
    ▪ Small models work!
    ▪ We can reuse existing solutions
    ▪ Less is more (reduce size AND improve accuracy)
    ▪ Challenged prior conclusion
    ▪ Re-open this area

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  35. Acknowledgement
    Co-authors: Siwei Yin, Adriana Sejfia, Marcelo
    Schmitt Laser, Haoyu Wang, Nenad Medvidović
    35

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  36. Thanks!
    36
    Any questions?
    [email protected]
    @yixue_zhao
    https://people.cs.umass.edu/~yixuezhao/

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