1. Challenges of Large-scale Vehicular & Mobile Ad hoc Network Simulation Thomas D. Hewer, Maziar Nekovee, Radhika S. Saksena and Peter V. Coveney t.hewer@cs.ucl.ac.ukt.hewer@cs.ucl.ac.uk maziar.nekovee@bt.com maziar.nekovee@bt.com t.hewer@cs.ucl.ac.ukmaziar.nekovee@bt.com Centre for Computational Science, University College London and BT Research

2. Vehicular Networking Vehicular networks are formed when radio-equipped vehicles come within range of one another or roadside equipment Vehicular networks are formed when radio-equipped vehicles come within range of one another or roadside equipment They can be used to disseminate traffic information, perform collision avoidance, reduce congestion and improve the flow of traffic through a system They can be used to disseminate traffic information, perform collision avoidance, reduce congestion and improve the flow of traffic through a system The latest telecommunications protocols and mechanisms are being implemented into cars and equipment and must be tested and validated The latest telecommunications protocols and mechanisms are being implemented into cars and equipment and must be tested and validated Simulation allows for inexpensive research and development Simulation allows for inexpensive research and development

3. Network Simulations Congestion reduction using inter-vehicular ad hoc communication to warn of an approaching obstacle

4. Problem Current network simulation tools available require many CPU hours to run even small simulations on a single-processor machine Current network simulation tools available require many CPU hours to run even small simulations on a single-processor machine Given the applications required by vehicular network simulations, the results are time-dependent and often urgent Given the applications required by vehicular network simulations, the results are time-dependent and often urgent To this end a requirement exists to run complex simulations more quickly than is achievable using off- the-shelf software and single-processors To this end a requirement exists to run complex simulations more quickly than is achievable using off- the-shelf software and single-processors HPC can enable this reduction in simulation runtime.... HPC can enable this reduction in simulation runtime....

5. Vehicular Modelling Car following model (IDM from Treiber et al.(2000)) Calculate the gain from a lane change MyAdv = a new − a old + bias where a is the acceleration and bias applies road rules (i.e. drive in the right-hand lane where possible) Calculate the disadvantage to users in the new lane if change occurs OthDisAdv = a behind(old) − a behind(new) the effects of this realistically model the risk assessment of changing lane THRESHOLD?(MyAdv - p) * OthDisAdv > Lane-changing algorithm using acceleration gain method

6. Network Simulations Nodes handling - movement and object parameters Packet handling - RAM considerations Models - complexity and variation Radiowave propagation - complexity vs. computation cluster edge message source Nekovee and Bogason, 2007.

7. Field of Study - M25 London M25 London Orbital 121.5 miles long Longest ring road in the world 31 junctions 9 motorway interchanges Junction 15 to 14 carries 165000 cars per day Simulating just Junction 15 to 14 for 24 hours would take over a year to achieve on a single processor machine

8. Results - Benchmarking The CPU time taken to run a one hour simulation with flooding algorithm

9. Results - Extrapolation Extrapolation of NS-2 simulation CPU consumption

10. Challenges Packet Handling - system memory requirements increase exponentially as the number of messages sent/received/in transit increases Boundary Conditions - complex algorithms to allow seamless decomposition across different boundaries and high amounts of communication overhead HPC Infrastructure - network, disk, file I/O considerations Global Knowledge Requirements - radiowave propagation and pathloss models require long-distance calculations over the number of nodes to accurately measure SNR, reception probability etc.

11. Decomposition Techniques Domain and task farming algorithms for vehicular simulations require much boundary communication, at every timestep of the simulation (a million events per second) Component decomposition allows us to keep this boundary communication low but still keep the simulation free of causality/synchronisation problems The processors can operate asynchronously for events that occur only in their own scope, and can perform mass-transit data shifts at greater time intervals Technique: split the nodes into lists (per processor available), create a global simulation object on each processor and perform all processing for each node on it’s home processor, then update the global object How we split the nodes into the component lists heavily influences the efficiency of the simulation performance, so node clusters should be assigned to the same processor where possible Hierarchical binning methods can be useful here, especially where nodes are constrained to roadways

12. Parameter Search One useful method of simulation for vehicular networks is to search through all the various parameters of the models involved With so many parameters and scenarios the ability to run each parameter ‘set’ on an individual processor is highly desirable Processors may operate completely independently and still share a central dataset, leading to much more efficient use of processing power The stochastic analysis of different settings in a simulation is useful for decision- making systems It also allows us to test new theories regarding power, coding techniques etc.

13. Looking Forward Multi-resolution simulations Multi-resolution simulations Visualisation Visualisation Steering Steering Intelligent Transport Systems Intelligent Transport Systems Collision Avoidance Collision Avoidance Emission Monitoring/Reduction Emission Monitoring/Reduction

14. Thankyou