ENDORSING PARTNERS

Bio-inspired costeffective access to big
data

The following are confirmed contributors to the busines...
Outlines

Problem statement

Bio-inspired cost-effective to access big data

Conclusion and future work

Summary

Bio-insp...
Outlines

Problem statement

Bio-inspired cost-effective to access big data

Conclusion and future work

Summary

Outline
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Problem statement

Bio-inspired cost-effective to access big data

Conclusion and future work

Summary

A few st...
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Bio-inspired cost-effective to access big data

Conclusion and future work

Summary

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Problem statement

Bio-inspired cost-effective to access big data

Conclusion and future work

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Basic co...
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Problem statement

Bio-inspired cost-effective to access big data

Conclusion and future work

Summary

Basic co...
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Problem statement

Bio-inspired cost-effective to access big data

Conclusion and future work

Summary

Basic co...
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Problem statement

Bio-inspired cost-effective to access big data

Conclusion and future work

Summary

Basic co...
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Problem statement

Bio-inspired cost-effective to access big data

Conclusion and future work

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Basic co...
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Bio-inspired cost-effective to access big data

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Service ...
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Bio-inspired cost-effective to access big data

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Optimiza...
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Bio-inspired cost-effective to access big data

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Bio-inspired cost-effective to access big data

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Why bio-...
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Bio-inspired cost-effective to access big data

Conclusion and future work

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Why bio-...
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Bio-inspired cost-effective to access big data

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Why bio-...
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Bio-inspired cost-effective to access big data

Conclusion and future work

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Bio-insp...
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Bio-inspired cost-effective to access big data

Conclusion and future work

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Bio-insp...
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Bio-inspired cost-effective to access big data

Conclusion and future work

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Bio-insp...
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Bio-inspired cost-effective to access big data

Conclusion and future work

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Case stu...
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Bio-inspired cost-effective to access big data

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Bio-inspired cost-effective to access big data

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GA and M...
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Bio-inspired cost-effective to access big data

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GA and M...
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Bio-inspired cost-effective to access big data

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ACS and ...
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Bio-inspired cost-effective to access big data

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ACS and ...
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Bio-inspired cost-effective to access big data

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MOACS an...
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Bio-inspired cost-effective to access big data

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Thank yo...
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SMART International Symposium for Next Generation Infrastructure: Bio-inspired cost effective access to big data

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A presentation conducted by Dr Jun Shen, School of Information Systems and Technology University of Wollongong.
Presented on Tuesday the 1st of October 2013

With the rapid proliferation of services and cloud computing, Big Data has become a significant phenomenon across many scientific disciplines and sectors of society, wherever huge amounts of data are generated and processed daily. End users will always seek higher-quality data access at lower prices. This demand poses challenges
to service composers, service providers and data providers, who should maintain their
service and data provision as cost-effectively as possible. This paper will apply bio inspired approaches to achieving equilibrium among the otherwise competitive stakeholders. In addition to novel models of cost for Big Data provision, bio-inspired algorithms will be developed and validated for dynamic optimisation. Furthermore, the optimised algorithms will also be applied in the data-mining research on the Alpha Magnetic Spectrometer (AMS) experiment, which is aiming to find dark matter in the universe. This experiment typically receives 200G and generates 700G data daily.

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SMART International Symposium for Next Generation Infrastructure: Bio-inspired cost effective access to big data

  1. 1. ENDORSING PARTNERS Bio-inspired costeffective access to big data The following are confirmed contributors to the business and policy dialogue in Sydney: • Rick Sawers (National Australia Bank) • Nick Greiner (Chairman (Infrastructure NSW) Monday, 30th September 2013: Business & policy Dialogue 3rd www.isngi.org Tuesday 1 October to Thursday, October: Academic and Policy Dialogue by: Dr Jun Shen, School of Information Systems and Technology Presented University of Wollongong, www.isngi.org
  2. 2. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Bio-inspired cost-effective access to big data Lijuan Wang Jun Shen School of Information Systems and Technology University of Wollongong, Australia lw840@uowmail.edu.au ISNGI 2013 Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  3. 3. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Outline Introduction Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  4. 4. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary A few streams of big data Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  5. 5. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Outline Introduction Problem statement Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  6. 6. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Basic concepts Services Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  7. 7. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Basic concepts Services Abstract services Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  8. 8. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Basic concepts Services Abstract services Concrete services Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  9. 9. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Basic concepts Services Abstract services Concrete services Quality of service (QoS) Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  10. 10. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Basic concepts Services Abstract services Concrete services Quality of service (QoS) Web service composition Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  11. 11. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Service and data usage and charging relationship Data Provider pay provide request charge data set Service Provider request pay provide elementary service charge Service Composer Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  12. 12. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Optimizations in data-intensive service composition optimisation point 1 optimisation point 2 concrete services data replicas replica 1 csn,1 replica 2 csn,2 optimisation point 3 abstract services datasets dataset 1 AS1 dataset 2 AS2 replica l-1 csn,m-1 replica l csn,m Application dataset k-1 ASn Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data dataset k University of Wollongong, Australia
  13. 13. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Outline Introduction Problem statement Bio-inspired cost-effective to access big data Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  14. 14. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Why bio-inspired algorithms Global optimization approach Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  15. 15. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Why bio-inspired algorithms Global optimization approach Less computation time Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  16. 16. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Why bio-inspired algorithms Global optimization approach Less computation time Features such as autonomy, scalability, adaptability and robustness Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  17. 17. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Bio-inspired algorithms Biological systems are autonomous entities and self-organized Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  18. 18. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Bio-inspired algorithms Biological systems are autonomous entities and self-organized Simplicity and rapid convergence Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  19. 19. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Bio-inspired algorithms Biological systems are autonomous entities and self-organized Simplicity and rapid convergence Strengths in optimizing dynamic negotiations Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  20. 20. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Case study: Alpha Magnetic Spectrometer (AMS) Monte Carlo Simulation Analog Detectors Simulation Data AMS-02 Package CEANT3 Data Capture AMS-02 Data reconstruction Raw Data Physical Analysis Result Storage Data reconstruction ROOT Query and Display Correction Data Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data Visualization University of Wollongong, Australia
  21. 21. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Outline Introduction Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  22. 22. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary GA and MIP 8000 Computation Time (msce) 7000 Genetic Algorithm Mixed Integer Programming 6000 5000 4000 3000 2000 1000 0 10 20 30 40 50 Number of abstract services Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  23. 23. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary GA and MIP Computation Time (msec) 1400 1200 Genetic Algorithm Mixed Integer Programming 1000 800 600 400 200 0 100 200 300 400 500 600 700 800 900 1000 Number of candidate services per class Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  24. 24. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary ACS and GA QWS 9000 Computation Time (msec) 8000 ACS GA 7000 6000 5000 4000 3000 2000 1000 0 10 20 30 40 50 Number of abstract services Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  25. 25. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary ACS and GA QWS 9000 8000 ACS GA Computation time (msec) 7000 6000 5000 4000 3000 2000 1000 0 100 200 300 400 500 600 700 800 900 1000 Number of candidate services per class Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia
  26. 26. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary MOACS and MOGA Median Summary Attainment Surface 5 5 x 10 MOACS:n30m50 MOGA:n30m50 Overall Execution Time 4.5 4 3.5 3 2.5 2 1.5 3.2 3.4 3.6 3.8 4 4.2 4.4 Overall Cost Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data 4.6 4.8 5 5.2 4 x 10 University of Wollongong, Australia
  27. 27. Outlines Problem statement Bio-inspired cost-effective to access big data Conclusion and future work Summary Thank you very much! Questions and suggestions are welcome. Lijuan Wang, Jun Shen Bio-inspired cost-effective access to big data University of Wollongong, Australia

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