Senior Design Project 2 nd Semester Update

1. Senior Design Project2nd Semester Update Chris Klumph and Kody Willman Spring 2008

2. Resource Allocation in a MMOG • in a massive multiplayer online game (MMOG) multiple players are simultaneously connected to a virtual game world at any given time • this heterogeneous system has • varying computer capabilities (e.g. processor, memory, etc.) • varying communication times between players’ computers

3. Pn Pn-1 Pn P1 … Pn-1 Main Server (MS) Main Server (MS) P2 P6 P1 … P2 P6 P3 P5 P3 P5 P4 P4 Problem Statement all players connected directly to main server (MS) vs. players connected to MS directly or through secondary server (SS) SS2 SS6

4. Terminology and Goals of Project Goals Terminology • given that 200 players are connected directly to the MS • reconnect players to either MS or SS to minimize RTmax • use multiple heuristics to find valid mappings • must determine three things: • number of SSs • who becomes an SS • what players connect to those SSs • RTmax • maximum response time between players’ computers and MS • heuristic • way to allocate resources (servers) • problem analogous to mapping tasks to machines (NP complete) • chromosome • represents a mapping of players and servers (who’s connected to whom)

5. Chromosome Example MS SS(P6) SS (P2) players 1 and 3 are connected to SS (P2) players 4 and 5 are connected directly to MS players 7 and 8 are connected to SS (P6) P4 P1 P8 P5 P7 P3

6. Genitor Overview genitor_heuristic() { generate_initial_population; ranking; while(stopping criteria not met) { selection; crossover; mutation; ranking; } output best solution found; } • selection: selects 2 random parents using roulette wheel selection • crossover: picks 2 random points within parents and swaps all data elements within those points between parents to create offspring • mutation: randomly mutates elements within offspring with predetermined probability • ranking: evaluates new chromosomes based on RTmax and sorts them into ranked list • stopping criteria: 10,000 iterations with no change in the (current) best solution or 10 minutes

7. Changes in Genitor • objective function • old: RTmax = 2 x Comm(Px, SSy) + Comp(SSy) + 2 x Comm(SSy, MS) + Comp(MS) • new: RTmax = Comm(Px, SSy) + Comp(SSy) + Comm(SSy, MS) + 2∙Comp(MS) + Comm(MS, SSy) + Comm(SSy, Px) • crossover fixing • old: invalid mappings randomly assigned to new SSs • new: invalid mappings assigned to achieve lowest RTmax

8. K-Optimized Dual Iterative Minimization KODI • 200 users • k initial SS created • for 10-30 iterations • run Phase 1 • run Phase 2 • compare RTmax times at each Phase • output best solution

9. Formulas RTmax Pct • Px: player x • SSa: secondary server a • RTmax = Comm(Px, SSa) + Comp(SSa) + Comm(SSa, MS) + 2∙Comp(MS) + Comm(MS, SSa) + Comm(SSa, Px) • DCU: directly connected user • Px: player x • S: server (can be SS or MS) • Pct (player connection time) = Comm(Px, S) + Comm(S, Px)

10. Min-Min RT • given a predetermined set of DCU’s, all players that are not in the set of DCU’s are marked as unassigned • for each unassigned player, the server (MS or DCU) that gives the minimum RT is determined • the pair, <player, server> with the smallest RT among those generated in step 2 is selected • the player in the best pair selected from step 3 is then assigned to the corresponding server • steps 2 through 4 repeated until all players assigned

11. DCU Min-Min • given a predetermined set of DCU’s, all players that are not in the set of DCU’s are marked as unassigned • for each unassigned player, the server (DCU only) that provides the minimum RT is determined • the pair, <players, server> with the smallest RT among all generated in step 2 is selected • the player in the best pair selected is then assigned to the corresponding server • steps 2 through 4 repeated until all players assigned

12. Phase 1 • iteratively adjusts the number of DCUs to minimize RTmax • randomly pick k, between 1 and 200, for initial DCUs • randomly assign k players as DCU • finish assignment of 200-k players using DCU Min-Min • find player with RTmax and if • player: unmap, and remap using Min-Min RT • DCU: (no players connected) unmap, and remap using Min-Min RT • repeat 5 for 1000 iterations or no improvement

13. Phase 2 • iteratively adjusts the connection times of players to minimize the RTmax • find player that has the largest connection time Pct • for that Pct player if it is • DCU: unmap it, and all players connected through it, and remap using Min-Min RT • player: unmap, and remap using Min-Min RT • check if new mapping results in lower RTmax time otherwise keep previous • repeat steps 2 and 3 for 1000 iterations or no improvement is found in the solution

14. Results from 1st Semester learned about frequently-used heuristics implemented, debugged, and simulated the following: genetic algorithm genitor kodi (k-optimized dual iterative) minimization obtained results from all heuristics, focused on genitor and kodi minimization part 1 of project does not consider robustness

15. Goals for 2nd Semester changes in simulation model objective function asymmetric communication times other heuristics must be adapted to RTmax changes tabusearch dual-iterative minimization discrete particle swarm optimization adapt heuristics to part 2 of project by introducing robustness