Neural networks for pattern recognition
Neural networks for pattern recognition
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Matchmaking: An extensible framework for distributed resource management
Cluster Computing
A Neural Network Approach for Dynamic Load Balancing In Homogeneous Distributed Systems
HICSS '97 Proceedings of the 30th Hawaii International Conference on System Sciences: Software Technology and Architecture - Volume 1
An architecture to support scalable distributed virtual environment systems on grid
The Journal of Supercomputing
Rokkatan: scaling an RTS game design to the massively multiplayer realm
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
ASKALON: A Grid Application Development and Computing Environment
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Colyseus: a distributed architecture for online multiplayer games
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
RTF: a real-time framework for developing scalable multiplayer online games
Proceedings of the 6th ACM SIGCOMM workshop on Network and system support for games
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
A Case Study on Using RTF for Developing Multi-player Online Games
Euro-Par 2008 Workshops - Parallel Processing
Toward real-time, many-task applications on large distributed systems
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
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Massively multiplayer online games (MMOG) are an innovative and challenging class of applications for Grid computing that require large amounts of computational resources for providing a responsive and scalable gameplay for concurrently participating players connected via Internet. We present our Real-Time Framework (RTF)--- a Grid-based middleware for scaling game sessions through a variety of parallelization and distribution techniques. RTF is described within a novel multi-layer service-oriented architecture that comprises three advanced services --- monitoring, capacity planning, and runtime steering --- that use the potential of Grid computing to provide pervasive access to a potentially unbounded number of resources. We report experimental results on the quality of our capacity planning and scalability of the RTF distribution mechanism.