What the senior design students have been doing

1. What the senior design students have been doing By Chris Klumph and Kody Willman

2. Resource allocation in a MMOG • in a massive multiplayer online game (MMOG) multiple users are simultaneously connected at any given time. • this is a heterogeneous system • varying computer capacity • varying communication times • implementing static resource allocation heuristics for this type of heterogeneous system is what the senior design students are doing.

3. For this problem Chris has worked on Kody has worked on • Genitor heuristic • Min-Min heuristic

4. Previously implemented GA Overview GA_matching_scheduling() { initial population generation; evaluation; while(stopping criteria not met) { selection; crossover; mutation; evaluation; } output best solution found; } • population of 200 players by 100 chromosomes • evaluation using Min-Min • stopping criteria of 1000 iterations or no change in solution • two parent selection by 50 times each iteration to generate new population • two point crossover • 1%-3% chance of mutation • output best solution

5. GA Original • initial population generation • 1-Secondary Server • 0-Player • evaluation • Min-Min • while stopping criteria not met

6. GA original Point 1 Point 2 • selection • crossover • two point Swap the 3-6 portion

7. GA original • mutation • evaluation • Min-Min Randomly Swap numbers

8. GA original • output best solution however this solution does not guarantee the optimal solution is even considered therefore we decided to remake it

9. New: Genitor GA • initial population generation • -2 – connected to main server(MS) as a player • -1 – a secondary server(SS) • 1-8 – connected to that SS • evaluation • solve each line by giving Rank(R) • while stopping criteria not met

10. Genitor GA • selection • crossover • two point Point 1 Point 2 swap the 3-6 portion

11. Genitor GA in each line do the players match up with the actual SS? • fixing – line not always valid randomly assign the players to the known SS or randomly make new SS

12. Genitor GA • mutation • fixing – line not always valid • evaluation • solve each line Randomly Swap numbers Evaluate rank, then sort into mapping

13. Genitor GA • drop lowest ranking • repeat until criteria are met • output best solution at location Rank 1 Questions?

14. Min-Min Overview considering all unmapped tasks while(there are unmapped tasks) { find task with overall minimum completion time; assign task to corresponding machine; update machines and completion times; } • ours involves • 200 players or tasks • run a Min-Min • run Phase1 • run Phase2 • compare completion times • output best solution

15. Min-Min • no players connected to MS, only SS • 3 types of players • SS – Secondary Servers • MP – Mapped player • UP – Unmapped player find minimum connection time connect update repeat for all unmapped players

16. Min-Min Phase1 • start with all users unmapped • randomly pick k users (between 1-10) to be initial SS • finish the mapping with the Min-Min heuristic

17. Min-Min Phase1 • find the user with the largest round trip time • if user is SS • no players connected • remap as player with better connection time • if user is MP • remap as player to different SS with better connection time or • remap as SS with better connection time • repeat for 1000 iterations or no update

18. Min-Min Phase2 • start with the end mapping from Phase1 • find the user with the largest round trip time • if user is SS • means no players connected • consider swapping user in as its SS and making it a player with better overall connection time or • consider swapping with all other SS with smaller overall round trip time

19. Min-Min Phase2 • if user is MP • consider swapping this user with all other non-SS users • always keep smallest overall round trip time • repeat 1000 iterations or no improvement • output best solution Questions?