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A Grid-enabled Multi-server Network Game Architecture Tianqi Wang, Cho-Li Wang, Francis C.M.Lau Department of Computer Science and Information Systems.

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Presentation on theme: "A Grid-enabled Multi-server Network Game Architecture Tianqi Wang, Cho-Li Wang, Francis C.M.Lau Department of Computer Science and Information Systems."— Presentation transcript:

1 A Grid-enabled Multi-server Network Game Architecture Tianqi Wang, Cho-Li Wang, Francis C.M.Lau Department of Computer Science and Information Systems The University of Hong Kong, Pokfulam Road, Hong Kong, China {tqwang, clwang, fcmlau} @csis.hku.hk

2 Outline   Motivation   Our approach  Experiments  Experiments and evaluations   Conclusion

3 Characteristics of a Multi-player Network Game System A kind of DVE system:  Distributed users  Real time interactions  A sense of realism An ideal MNG system has intensive requirements on both the computing power and network bandwidth.

4 P2P Architecture  Bandwidth consumption -> IP-multicast ;  Weak peer So such systems either limit the complexity of the world or restrict the total number of users. Examples:  MiMaze  MS AOE P2P: each user maintains its own copy of the virtual world.

5 CMS Architecture Server:  Process command packets from the clients  Synchronized execution  Calculate the world states  Area of Interest (AOI) management Client:  Dead reckoning and rendering complex world widely accessible Advantage: complex world widely accessible dynamic load sharing Challenge: dynamic load sharing CMS: several servers do most of the computation intensive jobs.

6 Problems of Existing Approaches   Ring   A virtual environment system by bell lab   Static partition   Cyber-walk   A distributed web walk through system   Partition is adjustable   MS Asheron’s call   Similar approach   Problems:   Several hotspots -> cascading effect   Not cost-effective ->servers can no come and go

7 Grid Computing   Scientific computing area:  Resources sharing and collaborations among organizations  Dynamic service deployment, discovery and creation   Several gird projects:  TeraGrid, European data grid, DOE science grid  Support large-scale scientific experiments and analysis   Butterfly grid:  Easy to use commercial computing grid environment for developers  High-performance networked servers for the publishers

8 Our Approach  Propose gamelet concept:  An execution abstraction within the partitioned virtual world  High mobility for supporting dynamic load sharing  Propose a multi-server architecture based on grid technology:  Dynamic computing power aggregation  Transparent load sharing

9 Gamelet Concept  Definition:  Execution abstraction  Logic partition  Structure:  Characteristics:  Load awareness  Remote control  Embedded Synchronization Gamelet { Data Component: Processing Component: } Fig. Gamelet Structure World Contents Performance Parameters Computation Part Control Part

10 Multi-server Model  Layered design:  Monitor Server  Worker Server  Communicator Server  Message route: … … … Fig. Multi-server Model : Worker Server : Client Network Connection : Monitor : Communicator Monitor Layer Gamelet Layer Communicator Layer LAN 1LAN 2

11 System Architecture   Based on GT3  Core services  Base services  Gamelet services  Interface  Invocation   Migration procedure:  GSHs registry  Cooperate with communicator TCP SOAP SOAP Network SOAP SOAP Naming / Service Data /Life Cycle Management GSI GRAM Gamelet Factory Service Gamelet Service Monitor Fig. Gamelet-based multi-server architecture. Grid Service Container Index Service Naming / Service Data / Life Cycle Management Gamelet Factory Service GSI Grid Service Container Index Service GRAM Gamelet Service

12 Prototype Design and Implementation  Simulate large-scale MNG systems:  Communication protocol  World size: 100*100*20  AOI: 10  Client simulator  Random movement (1/100ms)  Data packets (32 B/173 B)  Performance parameters  Lost rate (up to 50%)  Response time: RT*(1-LostRate) + (RT+TI)* LostRate Fig. AOI management AOI i j Fig. Gamelet partition

13 Testing Environment  Gamelet & monitor : GT3.0.1  Linux kernel 2.4.2, P3 733MHz CPU  256M RAM, 100Mbps Ethernet  Client simulator & communicator  Win2000 professional, P4 2.2GHz CPU  512M RAM, 100Mbps Ethernet  J2SE 1.4.2

14 Evaluation 1  Scheme one (no partition)  Scheme two (3 gamelets; statically in one server)  Analysis:  CPU graph  Threshold  Network load is: 1.9Mbps (96 clients) Fig. Performance evaluation 1. 032 1664 48 96 300 100 200 RT (ms) 400 0 32 16 64 48 96 75 25 50 Lost Rate (%) 100 0 32 16 64 48 96 75 25 50 CPU (%) 100 : Scheme 1 : Scheme 2 Clients Number

15 Evaluation 2  Scheme one (no partition)  Scheme three (dynamic + two servers at most)  Analysis:  Migration point (55) + Load balancing strategy  Migration influence  Gamelet creation time: 430ms ~ a few seconds Clients Number 0 32 16 N Nu m be r 64 48 96 300 100 200 RT (ms) 400 0 32 16 64 48 96 75 25 50 Lost Rate (%) 100 0 32 16 64 48 96 75 25 50 CPU (%) 100 Fig. Performance evaluation 2. : Scheme 1 : Scheme 3 128 128 Clients Number Clients Number 128 : Server 2 of Scheme 3 : Server 1 of Scheme 3

16 Evaluation 3  Scheme one (no partition)  Scheme three (dynamic + three servers at most)  Analysis:  Migration points  System throughput (64 ->130)  Dynamic + cost effective Clients Number 0 32 64 48 96 300 100 200 RT (ms) 400 0 32 160 64 48 96 75 25 50 Lost Rate (%) 100 0 32 160 64 48 96 90 80 CPU (%) 100 Fig. Performance evaluation 3. 128 Clients Number 128 : Server 1 of Scheme 3 : Server 2 of Scheme 3 : Server 3 of Scheme 3 160 70

17 New Data Using 16 Gamelets Analysis:  One communicator can support at most about 800- 900 clients.  16 gamelets can at most support about 1000 clients under our approach. (v.s. about 90 for one scheme one) Client #Communicator LR. CPU RTLost Rate Average Gamelet CPU 6001%. 89%9724%55% 8009%. 96%14837%61% 100032%. 100%20865%95% 120047%. 100%28475%100% 140057%. 100%35185%100%

18 Summary   Propose a multi-server architecture based on a gamelet concept and grid technology.  More dynamic and cost-effective MNGs.   Prove the effectiveness by detailed experiments of a 3D multi-player game prototype.  :  Future works:  Study the workload pattern of large scale distributed-virtual-environment systems.  Adaptive global load balancing algorithm for gamelets migration.

19 Thanks and Thanks and Any questions ? Any questions ?


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