Video Oyunlarında Bulanık Mantık ile Ağ Sınıfı Değerlendirilmesi

Year 2020, , 163 - 173, 15.01.2020

Tunç Uzlu Ediz Şaykol

https://doi.org/10.17714/gumusfenbil.518689

Abstract

Çok oyunculu bir oyun ortamında, bir oyunun verimliliği; oyunun eşleme kodu, ağ bağlantısı ve oyun kurucunun yanıt süresi gibi çeşitli faktörlere bağlıdır. Kötü ağ koşulu, bağlantıların kesilmesi ve eşlemenin bozulması çok oyunculu oyunlarda büyük bir sorun oluşturur. Bunu önlemek için önceki uygulamalar, bağlantı istatistiklerini temel alarak oyuncuları ayırmayı ve sınıflandırmayı içerir. Bununla birlikte, genellikle kullanılan ticari yöntem, sabit sınır değerlerinden faydalanır ve çoğunlukla bağlantı kesme kararı uygular. Oyuncuların orta ve kötü durumdaki ağ sınıfında bulunmaları ve oyun dışı bırakılmaları finansal açıdan verimli bir uygulama değildir. Günümüzde hızlı CPU ve grafik işlemciler, oyunların tatminkâr seviyede hesaplanmasını sağlamakta ve oyunun verimi katılımcıların ağ bağlantısına bağlı kalmaktadır. Genellikle oyunlar yaygın bilinen Unix aracı olan ping'e benzer paketler göndererek sunucu ile istemci oyuncu arasındaki gecikmeyi ölçmeyi tercih eder. Ayrıca oyun dışı bırakılma kararı bir yönetici tarafından değerlendirilir. Bu makalede, oyun dışı bırakma kararını ağ istatistikleri ve örüntü tanıma tekniklerinden faydalanarak otomatik olarak değerlendirmeyi ele alıyoruz.

Keywords

Fuzzy logic, Gaming, Latency analysis, Pattern recognition

Evaluating a Player’s Network Class in a Multiplayer Game with Fuzzy Logic

Abstract

In a multiplayer game environment, smoothness of a game depends on factors such as game’s netcode, player’s hardware, network connection and server’s response time. Players with bad network conditions and spiking network synchronization is always a problem for multiplayer games for both PCs and gaming devices based interactive sessions. Previous implementations to prevent such occasions involve disconnection based on their connection statistics. However usually used schema involves constant boundary values and mostly biased for disconnection decision. Disconnecting players with medium to worse network status is typically not an optimal practice as rendering the game unplayable for the person as if we think about player’s hardly earned money. Today, fast CPU and graphics processors offer rendering of game and receiving the game state with high frame rates. Hence, quality of the game depends on network connection of participants. Games prefer measuring latency between the server and the player by sending simple ping packets similar to well-known Unix tool. An administrator evaluates this value whether the player should be disconnected or not. In this paper, we discuss evaluating the player’s complex network statistics and automatically deciding for absence of the player with pattern recognition techniques.

Keywords

Bulanık mantık, Video oyunu, Gecikme analizi, Örüntü tanıma

References

• Bernier, Y.W., 2001. Latency Compensating Methods in Client/Server In-game Protocol Design and Optimization. Proceedings of the Game Developers Conference, GDC ’01, February 2001.
• Chan, L., Yong, J., Bai, J., Leong, B. and Tan, R., 2007. Hydra: a massively multiplayer peer-to-peer architecture for the game developer. Proceedings of the 6th ACM SIGCOMM workshop on Network and system support for games, NetGames '07, September 19-20, 2007, Melbourne, Australia, p.37-42.
• Chen, J., Wu, B., Delap, M., Knutsson, B., Lu, H. and Amza, C., 2005. Locality aware dynamic load management for massively multiplayer games. Proceedings of the Tenth ACM SIGPLAN symposium on Principles and practice of parallel programming, PPoPP '05, June 15-17, 2005, Chicago IL, USA, p.289-300.
• Chen, K.T., Huang, P., Huang, C.Y. and Lei, C.L., 2005. Game traffic analysis: an MMORPG perspective. Proceedings of the international workshop on Network and operating systems support for digital audio and video, NOSSDAV '05, June 13-14, 2005, Stevenson WA, USA, p.19-24.
• Claypool, M., 2005. The effect of latency on user performance in Real-Time Strategy games. Computer Networks, Volume 49, Issue 1, September 2005, Amsterdam, Netherlands, p.52-70.
• Cordeiro, D., Goldman, A. and Silva, D.D., 2007. Load balancing on an interactive multiplayer game server. Proceedings of the 13th International Euro-Par Conference, Euro-Par ‘07, August 28-31 2007, Rennes, France, p.184-194.
• Harcsik, S., Petlund, A., Griwodz, C. and Halvorsen, P., 2007. Latency Evaluation of Networking Mechanisms for Game Traffic. Proceedings of the Sixth Workshop on Network and System Support for Games, NETGAMES ‘07, September 19-20, 2007, Melbourne, Australia, p.129-134.
• Henderson, T., 2001. Latency and user behaviour on a multiplayer game server. Proceedings of the Third International COST264 Workshop on Networked Group Communication, NGC '01, November 07-09, 2001, London, UK, p.1-13.
• Joe, I., 1996. Packet loss and jitter control for real-time MPEG video communications. Computer Communications, Volume 19, Issue 11, September 1996, Amsterdam, Netherlands, p.901-914.
• Lebres, I., Rita, P., Moro, S. and Ramos, P., 2018. Factors determining player drop-out in Massive Multiplayer Online Games. Enterteinment Computing, Volume 26, May 2018, p.153-162.
• Lee, B. and Chang, J., 2016. Packet loss concealment based on deep neural networks for digital speech transmission. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 24, Issue 2, February 2016, Piscataway NJ, USA, p.378-387.
• Lee, K., Ko, B. and Calo, S., 2005. Adaptive server selection for large scale interactive online games. Computer Networks, Volume 49, Issue 1, September 2005, Amsterdam, Netherlands, p.84-102.
• Mohanty, R., Ravi, V. and Patra, M.R., 2010. Web-services classification using intelligent techniques. Expert Systems with Applications, Volume 37, Issue 7, July 2010, Tarrytown NY, USA, p.5484-5490.
• Munro, J., Appiah, K. and Dickinson, P., 2014. Investigating informative performance metrics for a multicore game world server. Enterteinment Computing, Volume 5, Issue 1, January 2014, Amsterdam, Netherlands, p.1-17.
• Reid, N.P. and Seide R., 2002. Wi-Fi (802.11) Network Handbook, First edition, Berkeley CA, USA, McGraw-Hill Osborne Media.
• Rumelhart, D.E., Hinton, G.E. and Williams, R.J., 1986. Learning internal representations by error propagation. Parallel distributed processing: explorations in the microstructure of cognition (MIT Press), Volume 1, July 1986, Cambridge MA, USA, p.318-362.
• Shanahan, J.G., 2000. Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features, First edition, Meylan, France, The Springer International Series in Engineering and Computer Science.
• URL-1, 2004. Voice and Video Enabled IPSec VPN Solution Reference Network Design, Cisco Systems, http://www.cisco.com/application/pdf/en/us/guest/netsol/ns171/c649/ccmigration_09186a008074f2d8.pdf
• URL-2, 2005. Star Wars® Battlefront IITM Server Manager Users Guide, Black Bag Operations and Lucasfilm Ltd., https://hiddenarrows.com/sites/default/files/utilities/SWBF2_BBO_SM-RM_User_guide.pdf
• Zander, S., Leeder, I. and Armitage, G., 2005. Achieving Fairness in Multiplayer Network Games through Automated Latency Balancing. Proceedings of the ACM SIGCHI International Conference on Advances in computer entertainment technology, ACE '05, June 15-17 2005, Valencia, Spain, p.117-124.