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 chromosome player 12345678 101010001 210000000 300100100 401010000 511111000 600000001 701010100 811010000 900001010 1000001110

6. GA original Chromo- some player 12345678 101010001 210000000 300100100 401010000 511111000 600000001 701010100 811010000 900001010 1000001110 selection crossover ▫two point chromosome player 12345678 300100100 701010100 Swap the 3-6 portion chromosome player 12345678 300010100 701100100 Point 1 Point 2

7. GA original mutation evaluation ▫Min-Min chromosome12345678 301010010100 701100100101 Randomly Swap numbers

8. GA original chrom osome player 12345678 101010001 210000000 310010100 401010000 511111000 600000001 701100101 811010000 900001010 1000001110 output best solution chrom osome player 12345678 900001010 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 Rank # Player # 12345678 14-24888 2 7227 7 3566-2 6-2 45 555-25 5-213 6-2 633-2 88 72 2-2722 8 111 555 94 4 24-22 10333-2 33

10. selection crossover ▫two point R12345678 3566-2 6-2 722-2722 swap the 3-6 portion R12345678 3562-2726 726-2 2 Point 1Point 2 Genitor GA R#R# Player # 12345678 14-24888 2 7227 7 3566-2 6-2 45 555-25 5-213 6-2 633-2 88 72 2-2722 8 111 555 94 4 24-22 10333-2 33

11. fixing – line not always valid Genitor GA R12345678 3562-2726 726-2 2 in each line do the players match up with the actual SS? randomly assign the players to the known SS or randomly make new SS R12345678 335-235-2 726-2 2 R#R# Player # 12345678 14-24888 2 7227 7 3566-2 6-2 45 555-25 5-213 6-2 633-2 88 72 2-2722 8 111 555 94 4 24-22 10333-2 33

12. mutation fixing – line not always valid evaluation ▫solve each line Randomly Swap numbers Evaluate rank, then sort into mapping Genitor GA R12345678 335-2-253 726-2 2 R12345678 935 -2-253 226-2 2 R#R# Player # 12345678 14-24888 22 6-2 2 3 7227 7 4566-2 6-2 55 555-25 6-213 6-2 733-2 88 82 2-2722 935 -2-253 10111 555 1144 24-22 12333-2 33

13. drop lowest ranking repeat until criteria are met output best solution at location Rank 1 Genitor GA P# R12345678 114-24888 R#R# Player # 12345678 14-24888 22 6-2 2 3 7227 7 4566-2 6-2 55 555-25 6-213 6-2 733-2 88 82 2-2722 935 -2-253 10111 555 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 P#12345678910 TypeUP P#12345678910 TypeMPSSMP SS MP find minimum connection time connect update repeat for all unmapped players

16. Min-Min Phase1 P#12345678910 TypeUP start with all users unmapped randomly pick k users (between 1-10) to be initial SS finish the mapping with the Min-Min heuristic P#12345678910 TypeUP SSUP SSUP SS P#12345678910 TypeMP SSMP SSMP SS

17. 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 Min-Min Phase1 P#12345678910 TypeMP SSMP SSMP SS P#12345678910 TypeMPSSMPSSMP SSMP SS

18. 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 Min-Min Phase2 P#12345678910 TypeMPSSMPSSMP SSMP SS P#12345678910 TypeMPSSMPSSMP SSMP SS

19. 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 Min-Min Phase2 P#12345678910 TypeMPSSMPSSMP SSMP SS Questions?