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Towards a Bell-Curve Calculus For e-Science

Towards a Bell-Curve Calculus For e-Science. Lin Yang Supervised by Alan Bundy, Dave Berry and Conrad Hughes. Content. Background Bell-Curve Calculus (BCC) Importance Definition Methodology Result and Analysis Evaluation Future Work and Conclusion. Background. Why QoS Properties

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Towards a Bell-Curve Calculus For e-Science

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  1. Towards a Bell-Curve Calculus For e-Science LinYang Supervised by Alan Bundy, Dave Berry and Conrad Hughes

  2. Content Background Bell-CurveCalculus(BCC) Importance Definition Methodology ResultandAnalysis Evaluation Future Work and Conclusion

  3. Background WhyQoSProperties Prediction,descriptionandevaluation Likelihoodofeachdatavalue Aims Define a suitable calculus for runtime Apply it to e-Science workflows

  4. BCC(1)–Importance WhyBellCurve An average case analysis: likely or unlikely Easy to store,calculate and propagate Deal with complex workflows efficiently

  5. BCC(2)–Importance Bell Curve = Normal Distribution Commonly occurs in the real world Evidence Experimental evidence Central Limit Theorem

  6. BCC(3)–Definition BellCurve

  7. BCC(4)–Definition QoSProperty: Runtime FourwaysofcombiningGridServices Sequential Parallel_All Parallel_First Conditional Fourfundamentalcombinationfunctions: sum, max, min & cond

  8. BCC(5)–Definition–FourCombinations Sequential (sum) Parallel_All (max) Parallel_First (min) Conditional (cond)

  9. BCC(6)–Methodology Twoinputbellcurves and Oneoutputbellcurve Thecombinationmethod = ParametersCalculation e.g.and

  10. BCC(7)–Methodology Twomaintasks Tofindasatisfactoryformula foreachcombinationmethod Toevaluateaccuracy and efficiency Agrajag DevelopedbyConradHughes Define classic distribution functions, operations and numeric approximation of function combinations

  11. BCC(8)–Result&Analysis–Max

  12. BCC(9)–Result&Analysis–Max

  13. BCC(10)–Result&Analysis–Refine Perfectparameters DefinedinAgrajag Approximatetheperfectvalues Fixoneparameter Uselinearfunctiontoapproachtheperfectvaluesintermsoftheotherparameterastheasymptote Derivethelinearparameters

  14. BCC(11)–Result&Analysis–Refine Useexponentialcompensation Getexponentialparameters Findtheregularpatternofthelinearandexponentialparametersintermsofthefirstparameter Combineanddescribetheperfectvalues intermsofthetwoBCCparameters

  15. BCC(12)–Result&Analysis–Max–Ref

  16. BCC(13)–Result&Analysis–Max–Ref

  17. BCC (14) – Result & Analysis – Max – Ref

  18. BCC (15) – Result & Analysis – Max -- Ref

  19. BCC (16) – Evaluation Two Methods Comparison with Agrajag Accuracy Efficiency Apply to use cases Brain atlas Extended workflows

  20. BCC (17) – Evaluation

  21. BCC (18) – Evaluation – Accuracy

  22. BCC (19) – Evaluation – Efficiency

  23. BCC (20) – Evaluation – Extended Use Case sum0 max0 sum1 max2 cond1 sum4 sum2 +perc*softmean0 +(1-perc)*softmean1 max1 sum3 warp+reslice sum8 sum5 min0 sum9 sum6 min1 sum10 sum7 +convert +slicer

  24. BCC (21) – Evaluation – Extended Use Case

  25. Future Work (1) Embed In Frameworks More Evaluation More complex workflows Real data More Calculi e.g. log-normal distribution More QoS Properties e.g. accuracy and reliability

  26. Future Work (2) Extended TwelveFundamentalCombinationFunctions

  27. TheEnd

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