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Efficient Placement of Edge Computing Devices for Vehicular Applications in Smart Cities

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Presentation slides of article at IEEE/IFIP NOMS 2018 (April 25, 2018). Article available at https://users.aalto.fi/~premsag1

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Efficient Placement of Edge Computing Devices for Vehicular Applications in Smart Cities

  1. 1. Efficient Placement of Edge Computing Devices for Vehicular Applications in Smart Cities Gopika Premsankar, Bissan Ghaddar, Mario Di Francesco, Rudi Verago
  2. 2. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 2/28 Connected vehicles in smart cities Autonomous cars Collision avoidance Dynamic real-time routing of vehicles Real-time computer vision applications
  3. 3. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 3/28 Connected vehicles: challenges Mobile vehicles need continuous connectivity Low latency communication Increasing amount of data to be processed
  4. 4. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 4/28 Edge computing in vehicular networks Smart road side units (RSUs) equipped with servers Co-located Server Cloud Roadside Unit Edge Computing Device Urban Area V2I V2I
  5. 5. Image adapted from: http://www.ntt.co.jp/news2014/1401e/140123a.html NOMS 2018 5/28 Edge computing CloudCloud REV 1.2 201 1-0 8-0 8 __ __ REV 1.2 201 1-0 8-0 8 __ __ REV 1.2 201 1-0 8-0 8 __ __ REV 1.2 201 1-0 8-0 8 __ __ LatencyNetwork Volume of data Network Edge computingCloud computing
  6. 6. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 6/28 Objective How to deploy a network of edge computing devices in a city for connected cars? Challenges: Mobile vehicles need continuous connectivity Dense and built-up environment Limited computing resources at edge
  7. 7. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 7/28 Urban area deployments
  8. 8. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 8/28 Contribution Propose a mixed integer linear programming formulation for efficient deployment of edge computing devices Features: Specify target network and computational demand coverage Accurately characterizes the effect of built-up environment on communications Uses open public data
  9. 9. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 9/28 Agenda State of the art Mixed Integer Linear Programming formulation Evaluation Future work
  10. 10. Optimal placement of edge computing devices
  11. 11. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 11/28 Optimal placement of RSUs So far in the literature: Placement strategies to ensure network coverage [Liang et al., VTC ’12, Aslam et al., ISCC ’12, Balouchzahi et al., 2015] Aim to reduce cost and number of RSUs deployed Our approach: Placement of RSUs in urban areas taking into account both network coverage and computation requirements Y. Liang, H. Liu, D. Rajan, “Optimal placement and configuration of roadside units in vehicular networks”, IEEE Vehicular Technology Conference, 2012 B. Aslam, F. Amjad, C. C. Zou, “Optimal roadside units placement in urban areas for vehicular networks”, 2012 ISCC, July 2012, pp. 423-429 N. M. Balouchzahi, M. Fathy, A. Akbari, “Optimal road side units placement model based on binary integer programming for efficient traffic information advertisement and discovery in vehicular environment”, IET Intelligent Transport Systems, vol. 9, no. 9, pp. 851-861, 2015
  12. 12. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 12/28 System model Candidate RSU location Area modelled as a set of cells C Each cell has a candidate location for installing an RSU RSUs can be deployed with different power levels P Wireless communication over IEEE 802.11p
  13. 13. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 13/28 Optimization problem: RSU-OPT A mixed integer linear programming model to minimize cost of deploying RSUs while ensuring: target network coverage γ target computational demand α Decision variables: yi Place RSU in cell i xi,k Transmit power level k to assign to RSU in cell i
  14. 14. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 14/28 RSU-OPT: objective minimize |C| i=1 cyi + |C| i=1 |C| j=1 ai,j zi,j + |P| k=1 |C| i=1 bk xi,k Fixed cost for each RSU Cost based on distance between RSU and covered cell Cost based on transmit power level
  15. 15. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 15/28 RSU-OPT: constraints for network coverage Pre-computed adjacency matrix Meet network coverage requirement for gamma percent of roads
  16. 16. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 16/28 RSU-OPT: constraints for computational demand Meet computational demand for alpha percentage of area Do not exceed available CPU cycles at each RSU
  17. 17. Evaluation
  18. 18. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 18/28 Evaluation: simulation settings Area: city center of Dublin (3.2 by 3.1 square kilometers) Under moderate and high traffic conditions Network simulations MILP solved using CPLEX Key simulation parameters: Parameter Value Edge server 1.2 GHz Computational availability 600 megacycles Computational requirement 18,000 CPU cycles/bit Application message frequency 1 Hz
  19. 19. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 19/28 Evaluation: comparison with other approaches Baseline approaches: Every 500 m apart (primarily at intersections) Every 1 km apart Heuristic based on traffic volume: Deploy RSUs in cells with high traffic volume Metrics: Number of RSUs deployed Message loss due to lack of network connectivity Message loss due to CPU capacity (cycles) exceeded at RSUs
  20. 20. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 20/28 Evaluation: RSUs deployed Approach Number of RSUs RSU-OPT (α = γ = 100%) 105 Traffic volume 84 Every 500 m 91 Every 1 km 42
  21. 21. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 21/28 Evaluation: message loss RSU-OPT Traffic Volume Uniform (500m) Uniform (1km) 0 5 10 15 20 25 30 35 Messageloss(%) moderate traffic heavy traffic RSU-OPT results in lowest message loss Uniform (500 m): almost double the loss despite having 91 RSUs Traffic volume heuristic: low message loss with only 84 RSUs
  22. 22. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 22/28 Evaluation: CPU capacity RSU-OPT Traffic Volume Uniform (500m) Uniform (1km) 0 2 4 6 8 10 Messageloss(%) moderate traffic heavy traffic RSU-OPT: lowest message loss, lowest spread Traffic volume heuristic: higher message loss despite placing RSUs in areas with high traffic volume
  23. 23. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 23/28 Evaluation: RSU-OPT settings γ (%) 60 65 70 75 80 85 90 95 100 α (%) 60 65 70 75 80 85 90 95 100 NumberofRSUs 30 40 50 60 70 80 90 100 110 Reducing network coverage (γ) and computational demand (α) by 5% results in only 85 RSUs Target network coverage (γ) is more stringent constraint
  24. 24. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 24/28 Evaluation: RSU-OPT settings γ =95 α =95 85 RSUs γ =90 α =90 73 RSUs γ =90 α =95 74 RSUs γ =90 α =97 74 RSUs γ =90 α =100 104 RSUs 0 2 4 6 8 10 12 Messageloss(%) γ=100, α=100 Traffic Volume Uniform (500m) γ =95 α =95 85 RSUs γ =90 α =90 73 RSUs γ =90 α =95 74 RSUs γ =90 α =97 74 RSUs γ =90 α =100 104 RSUs 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Messageloss(%) γ=100, α=100 Traffic Volume Uniform (500m) RSU-OPT with α = γ = 95: similar to traffic volume heuristic RSU-OPT with γ = 90 and 74 RSUs: lower message loss than Uniform (500 m) with 91 RSUs
  25. 25. Conclusion
  26. 26. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 26/28 Summary and Future Work Proposed a MILP to efficiently deploy edge computing devices in a dense urban scenario What next? Examine effect of different thresholds for delay Extend static deployment to a dynamic scenario Different wireless communication technologies
  27. 27. Thank you. Questions?
  28. 28. Efficient placement of edge computing devices for vehicular applications G. Premsankar, B. Ghaddar, M. Di Francesco, R. Verago NOMS 2018 28/28 Overview of evaluation

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