Efficient Methods for Improving Scalability and Playability of Massively Multiplayer Online Game (MMOG)

Abstract

The popularity of Massively Multiplayer Online Games (MMOG) is increasing rapidly these days with more players prefering to play with or against human players. Having more than one player in the game at one time enables people to use MMOGs to socialize with others while at the same time getting the enjoyment of playing games. However, game service providers often face the challenges caused by network latency and player’s behaviours. Despite of having fast development in internet technology, network latency is still one of the main problems in networking part of MMOGs. Network latency disrupts gameplayexperience by causing game state inconsistencies amongst game server and players machine which in the end, it discourages players to play the MMOGs. Meanwhile, cheating in MMOGs has been a constant problem which also causes game state inconsistencies with the game mechanics. There are various ways to cheat in MMOG and one of them is using bot to automatically control player to do certain tasks. In the end, cheating using bot affects playability of an MMOGs by disrupting game balance and eliminating one of the main purposes of playing a MMOG: to play against other player. Research in MMOG is a relatively new field, with few proposals to improve scalability and address cheating problems. There are ways to improve scalability such as by improving the internet itselfor adding more game servers and increasing bandwidth. However, this kind of solution often incurs additional operational costs for game service provider and takes time to be implemented in internet standard. Also, this solution does not solve the problem in player’s side where the same problem by network latency could happen. Meanwhile, researches to prevent cheating in MMOG have resulted in few proposals about how to detect cheaters and prevent them to play the game. There are also other methods devised by game service provider to deter cheaters before they even login into the game. However, the success oftheir methods is somewhat limited and lack of flexibility. Therefore, there are potentials for improvement for existing research or to solve the problem through another perspective. This thesis proposes a combination of solutions to improve scalability and playability of MMOGs. To improve scalability, this research presents Game World Partition (GWP) which categorized as a part of Interest Management System (IMS). GWP is a method commonly implemented in MMOG where it divides game world into partitions and manage the communication between players. Player in one partition does not need to communicate with other players in different partitions. This method improves scalability by reducing the number of packets transferred during gameplay and thus, allows more players to play in one game session. The research work proposes a new GWP method which is simple to implement but still offers improvement in scalability. This research also proposes a network workload evaluation method that could assist game service provider or programmer in evaluating and predicting their network resource requirements. Furthermore, this research proposes the use of Artificial Neural Network (ANN) for Dead Reckoning (DR) in MMOG. DR is a mathematical model that can be used to extrapolate player’s location based on the previous locations. DR improves playability by providing smoother gameplay whenever network latency occurs. To address cheating problems in MMOG, this research proposes an extension of ANN for DR to detect player movement generated by bot. The bot detection system analyse one player’s movement and determine the possibility of a player being controlled by bot or human. Results from experiments are presented in each of the solutions described in this thesis. The experiment uses random-generated data and data from real games whenever possible. Comparisons with commonly implemented method in MMOG shows how the solutions proposed in this thesis perform through simulations.

Date of Award	9 Oct 2010
Original language	English
Supervisor	Zheng Da Wu (Supervisor)