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Edge-Fog Cloud

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A decentralized cloud for IoT computations. Presented at IEEE Cloudification of IoT (CIoT) - 2016 in Paris

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Edge-Fog Cloud

  1. 1. Edge-Fog Cloud: A Distributed Cloud for Internet of Things Computations Nitinder Mohan, Jussi Kangasharju Department of Computer Science, University of Helsinki, Finland {firstname.lastname@cs.helsinki.fi} Conference on Cloudification of Internet of Things (CIoT) – 2016 Paris
  2. 2. Rise of connected IoT devices Projected number of IoT devices Average cost of a sensor Broadband by the numbers (NCTA), https://www.ncta.com/broadband- by-the-numbers 2
  3. 3. Computational Data Centers https://cloud.google.com/about/locations/ 3
  4. 4. Problem: Network!  High transport cost  High data volume  High network latency https://cloud.google.com/about/locations/ 4 Computational Data Centers
  5. 5. Fog Cloud Computing Cloud Fog Devices Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., & Koldehofe, B. (2013). Mobile fog. Proceedings of the Second ACM SIGCOMM Workshop on Mobile Cloud Computing - MCC ’13 Processing-capable network resources augment the cloud 4
  6. 6. Edge Cloud Computing Processing-capable, voluntary, user-controlled devices augment the cloud Lopez, P. G., Montresor, A., Epema, D., Iamnitchi, A., Felber, P., & Riviere, E. (2015). Edge-centric Computing : Vision and Challenges. Acm Ccr, 45(5), 37–42. 5
  7. 7. Edge & Fog Cloud: Problem Computation requires routing data to a central cloud! Cloud Fog Devices 6
  8. 8. Edge-Fog Cloud
  9. 9. Architecture Data Store Fog Edge Edge  Collection of devices: i. Loosely-coupled ii. Voluntary iii. Human operated  1-2 hops away from sensors & clients  Ad-hoc device-to-device connectivity within layer  Varying processing capability e.g. desktops, laptops, workstations, nano data centers etc. 8
  10. 10. Data Store Fog Edge Architecture Fog  Network devices with high compute capability  Manufactured, managed and deployed by cloud vendors such as CISCO*  Lies farther from sensors but closer to core  Dense connectivity within layer  Reliable connectivity to Edge e.g. routers, switches etc. *CISCO, “Cisco fog computing solutions: Unleash the power of the Internet of Things (whitepaper),” 2015 8
  11. 11. Data Store Fog Edge Architecture Data Store  Data archival and storage  No computation on data  Reliability and ease-of-access to data in Edge and Fog layers 8
  12. 12. Data Store Fog Edge Benefits 1. Reduced network load 2. Native support for mobility 3. Context in computation 4. No single point-of-failure 9
  13. 13. Workload Assignment
  14. 14. D1 D2 D3 D4 D5 1 4 34 1 Edge-Fog Cloud J1 J2 J3 J4 J5 Job Graph *Haubenwaller, Andreas Moregård, and Konstantinos Vandikas. "Computations on the Edge in the Internet of Things." Procedia Computer Science 52 (2015) Network Only Cost Assignment* 11
  15. 15. J1 J2 J3 J4 J5 J4 J5 J3 J1 J2 J2 J3 J4 J1 J5 I. Naïve Implementation Iterative Search 𝒩 devices 𝒩 jobs Worst Case: O(𝒩!) D1 D2 D3 D4 D5 1 4 34 1 Network Only Cost Assignment 12
  16. 16. Network Only Cost Assignment D1 D2 D3 D4 D5 1 4 34 1 J1 J2 J3 J4 J5 99 1 8 4 5 1 99 7 5 4 8 7 99 4 3 4 5 4 99 1 5 4 3 1 99 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 Dconn[ i, j ] = Jconn[ i, j ] = 13
  17. 17. Network Only Cost Assignment 99 1 8 4 5 1 99 7 5 4 8 7 99 4 3 4 5 4 99 1 5 4 3 1 99 Dconn[ i, j ] = 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 Jconn[ i, j ] = II. Quadratic Assignment Problem Minimize: NP-hard! • Approximated using Kuhn- Munkres or GLB bounds • Optimal solution not guaranteed 𝑎(𝑖,𝑗)∈𝐴 𝐽𝑐𝑜𝑛𝑛 𝑖, 𝑗 ∗ 𝐷𝑐𝑜𝑛𝑛(𝑓 𝑖 , 𝑓(𝑗)) 14
  18. 18. Least Processing Cost First (LPCF) Device Processing Power [Dproc(i)] D1: 3 D2: 2 D3: 2 D4: 5 D5: 6 1 4 34 1 J1: 4 J2: 2 J3: 5 J4: 4 J5: 2 Job Size [Jsize(i)] 15 D1 D2 D3 D4 D5 1 4 34 1 J1 J2 J3 J4 J5
  19. 19. Least Processing Cost First (LPCF) D1:3 D2:2 D3:2 D4:5 D5:6 1 4 34 1 J1:4 J2:2 J3:5 J4:4 J5:2 3 2 2 5 6 4 2 5 4 2 Dproc [i] = Jsize [i] = 16
  20. 20. Least Processing Cost First (LPCF) 3 2 2 5 6 4 2 5 4 2 Dproc [i] = Jsize [i] = I. Optimize Processing Cost Minimize: Linear Assignment Problem • Solved using Kuhn-Munkres/ Hungarian algorithm • Optimal solution guaranteed in O(n3) 𝑖,𝑗∈𝐴 𝐶 𝐽𝑠𝑖𝑧𝑒(𝑖) 𝐷 𝑝𝑟𝑜𝑐(𝑗) 𝑥𝑖𝑗 16
  21. 21. Least Processing Cost First (LPCF) I. Optimize Processing Cost Minimize: Linear Assignment Problem • Solved using Kuhn-Munkres/ Hungarian algorithm • Optimal solution guaranteed in O(n3) 𝑖,𝑗∈𝐴 𝐶 𝐽𝑠𝑖𝑧𝑒(𝑖) 𝐷 𝑝𝑟𝑜𝑐(𝑗) 𝑥𝑖𝑗 D1:3 D2:2 D3:2 D4:5 D5:6 1 4 34 1 J1:4 J2:2 J5:2 J4:4 J3:5 Least Processing Cost: 4.966 16
  22. 22. Least Processing Cost First (LPCF) II. Create sub-problem space Edge-Fog Cloud composes of several homogeneous devices running homogeneous jobs New Assignment Calculation: 1. Same processing power → interchange jobs 2. Same job size → interchange devices D1:3 D2:2 D3:2 D4:5 D5:6 1 4 34 1 J1:4 J2:2 J5:2 J4:4 J3:5 Least Processing Cost: 4.966 J1:4 J5:2 J2:2 J4:4 J3:5 J4:4 J5:2 J2:2 J1:4 J3:5 17
  23. 23. Least Processing Cost First (LPCF) D1 D2 D3 D4 D5 1. J1 J2 J5 J4 J3 2. J1 J5 J2 J4 J3 3. J4 J5 J2 J1 J3 4. J4 J2 J5 J1 J3 Least Processing Cost: 4.966 17 II. Create sub-problem space Edge-Fog Cloud composes of several homogeneous devices running homogeneous jobs New Assignment Calculation: 1. Same processing power → interchange jobs 2. Same job size → interchange devices
  24. 24. Least Processing Cost First (LPCF) III. Account Network Cost 1. Compute network cost of each assignment 2. Choose the assignment with least network cost D1 D2 D3 D4 D5 1. J1 J2 J5 J4 J3 2. J1 J5 J2 J4 J3 3. J4 J5 J2 J1 J3 4. J4 J2 J5 J1 J3 𝐽𝑐𝑜𝑛𝑛 𝑖, 𝑗 ∗ 𝐷𝑐𝑜𝑛𝑛(𝑓 𝑖 , 𝑓(𝑗)) Least Processing Cost: 4.966 N/W 20 27 19 28 18
  25. 25. Least Processing Cost First (LPCF) Advantages 1. Computed assignment has least processing cost and almost-optimal network cost 2. Task assignment accounts for processing cost of task deployment 3. Assignment solution is guaranteed in polynomial time 19
  26. 26. Evaluation
  27. 27. Edge-Fog Cloud Simulator Python-based Edge-Fog Cloud Simulator 1. Generates: i. Edge and Fog node graphs with device processing and network costs ii. Job node graphs with variable job sizes 2. Incorporates LPCF for assignment computation 3. Open Source 21
  28. 28. LPCF vs NOC Least Processing Cost First Network Only Cost *solver available from QAPLIB, http://anjos.mgi.polymtl.ca/qaplib/ 22 Edge-Fog Cloud Simulator + LPCF Solver Edge-Fog Cloud Simulator + Kuhn-Munkres Solver*
  29. 29. LPCF vs NOC I. Assignment computation time 1 hour 23
  30. 30. LPCF vs NOC II. Network cost analysis No time bound Time bounded ~10% 24
  31. 31. LPCF vs NOC III. Processing cost analysis 25
  32. 32. Discussion
  33. 33. Q. How well connected should EF nodes be? ~21% ~17% ~9% 27
  34. 34. Q. How does deployed job impact overall cost? 28
  35. 35. Conclusion Our contributions in this work are: 1. Formal architecture of Edge-Fog cloud 2. LPCF algorithm for assigning tasks on EF cloud 3. Open source Edge Fog cloud simulator & LPCF solver 4. Deployment analysis of Edge Fog cloud Source code available at: www.github.com/nitinder-mohan/EdgeFogSimulator 29
  36. 36. Backup
  37. 37. LPCF Search Space Reduction Topology Size 5 10 15 30 60 100 150 Original Space 5! 10! 15! 30! 60! 100! 150! LPCF Space 1! 3! > 4! > 5! > 7! > 8! > 9! 37
  38. 38. EF Cloud Simulator Parameters Property Value Total number of devices/jobs Experiment Specific Number of Edge devices 60% of total Number of Fog devices 40% of total Processing power of an Edge device 2-5 Processing power of a Fog device 7-9 Connection density of Edge layer (0-1) 0.2 Connection density of Fog layer (0-1) 0.6 Connection density between Edge and Fog layer (0-1) 0.5 Lowest job size in pool 2 Highest job size in pool 6 Inter-dependence density between jobs (0-1) 0.2 38

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