evidence-aware mobile computational offloading

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  1. 1. Crowdsensing the opportunistic context of mobile devices huber.flores@helsinki.fi Helsinki, Finland. EVIDENCE-AWARE MOBILE COMPUTATIONAL OFFLOADING Huber Flores, Pan Hui, Petteri Nurmi, Eemil Lagerspetz, Sasu Tarkoma, Jukka Manner, Vassilis Kostakos, Yong Li and Xiang Su
  2. 2. Outline Background Mobile code offloading Motivation Problem statement Evidence-aware mobile computational offloading Implications for the edge Conclusions Helsinki, Finland. 2
  3. 3. Background Opportunistic augmentation of resources Helsinki, Finland. [IEEE Communications] Flores, H., Hui, P., Tarkoma, S., Li, Y., Srirama, S., & Buyya, R. (2015). Mobile code offloading: from concept to practice and beyond. IEEE Communications Magazine, 53(3), 80-88. 3
  4. 4. Motivation The offloading outcome is diverse due to many parameters Latency Code profiling Device workload Server processing the task Type of device Etc. Initial idea. Can we do better? . Tuning parameters Helsinki, Finland. 4
  5. 5. Problem? It is not that easy For a single device Is it possible to crowdsource the problem? Helsinki, Finland. 5
  6. 6. Crowdsensing characterization Helsinki, Finland. 6
  7. 7. Crowdsensing characterization Helsinki, Finland. [IEEE Communications] Flores, H., Hui, P., Tarkoma, S., Li, Y., Srirama, S., & Buyya, R. (2015). Mobile code offloading: from concept to practice and beyond. IEEE Communications Magazine, 53(3), 80-88. 7
  8. 8. Crowdsensing characterization Helsinki, Finland. 8
  9. 9. Crowdsensing characterization Helsinki, Finland. 9
  10. 10. Crowdsensing characterization Helsinki, Finland. 10
  11. 11. Crowdsensing characterization Helsinki, Finland. [ICDCS] Flores, Huber, et al. Modeling Mobile Code Acceleration in the Cloud" , Proceeding of ICDCS 2017, Atlanta, USA, June 5-8, 2017. 11
  12. 12. Crowdsensing support LAPSI Helsinki, Finland. 12
  13. 13. EMCO framework Helsinki, Finland. 13
  14. 14. Crowd evaluation Helsinki, Finland. 14
  15. 15. Crowd evaluation Helsinki, Finland. 15
  16. 16. Off-the-shelf applications Helsinki, Finland. 16
  17. 17. Implications Combine cloud/edge provisioning Dynamic allocation of resources Self-organizing systems (end points at the edge) Offloading Sensing Networking Storage And so on Helsinki, Finland. 17
  18. 18. Implications Edge infrastructure Helsinki, Finland. 18
  19. 19. Implications Edge infrastructure Helsinki, Finland. Task 19
  20. 20. Summary Context characterization is really important, but it is not a task a single device can perform We demonstrate how context adaptation improves offloading A methodology for context reconstruction from passive data (datasets) Helsinki, Finland. 20
  21. 21. QUESTIONS Helsinki, Finland. 21

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