1. Cost-Efficient Hosting and Load Balancing of Massively Multiplayer Online Games
Vlad Nae, Radu Prodan, Thomas Fahringer
Institute of Computer Science
University of Innsbruck
11/16/2018

2. MMOG Subscriptions (last 10 years)
1. Introduction
MMOG Subscriptions (last 10 years)
24 million subscribers
Source:
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3. Entertainment Industries’ Sizes
1. Introduction
Entertainment Industries’ Sizes
Entertainment Software Association (ESA)
Size: 7 billion $
Dynamic: +300% in the last 10 years
Motion Picture Association of America (MPAA)
Size: 8.99 billion $
Dynamic: +50% in the last 10 years
Recording Industry Association of America (RIAA)
Size: 12.3 billion $
Dynamic: -2% in the last 10 years (stagnant)
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4. OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions
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5. OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions
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6. Games’ computational model
GAME LOOP
Game-world update
Interaction computation
Entity states update
Single sequential loop
3 steps in each loop:
Game-world state update
Entity interaction computation (dominant for MMOGs)
Entity state updates
Load generated by (2) is non-deterministic  human factor
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7. Game parallelization models
Zoning: huge game-world division into geographical sub-zones – each zone is handled by different machines
Replication: the same game-world handled by different machines, each one handling a subset of the contained entities (synchronized states)
Instancing: multiple instances of the same zone with independent states. (World of Warcraft, Runescape,..)
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8. Dynamic resource provisioning
2. Model
Dynamic resource provisioning
Massive leave
Massive join
Massive join
Main advantages:
Significantly lower over-provisioning
Efficient coverage of the world is possible
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9. Static vs. Dynamic allocation
2. Model
Static vs. Dynamic allocation
What is the incentive for using dynamic allocation for operating MMOGs?
Reduce the resource waste for hosting MMOGs to as low as 10 times the one of static allocation
250%
25%
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10. OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions
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11. OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions
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12. Resource provisioning
3. Method
Resource provisioning
Game operators:
Predicted loads  requests
Data centers and Cloud providers:
Time-space-(virtualization) renting policy  offers
Resource allocation: request – offer matching
Resource locality
Resource fitness
Resource bulk
size/proportionality
virtualization overhead
Time bulk size
REQ … 4
OFFER … 12
OFFER … 3
VIRT 1
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13. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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14. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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15. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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16. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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17. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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18. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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19. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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20. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
11/16/2018

21. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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22. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
11/16/2018

23. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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24. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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25. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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26. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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27. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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28. Load balancing 3. Method Possible actions: Replication
Client migration
De-replication
Server migration
Instancing
De-instancing
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29. OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions
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30. OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions
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31. Setup: Load balancing experiment
4. Experiments
Setup: Load balancing experiment
Demonstrator application
First Person Shooter – game type (FPS)
Based on Real-Time Framework (RTF)
Graphical interface utilises OGRE
Supports zoning and replication techniques
Testbed
7 machines from Amis (Slovenia) – game servers & resource allocation service
10 machines from University of Innsbruck –automated clients (bots)
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32. Demonstrator application
4. Experiments
Demonstrator application
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33. Results: Load balancing experiment
4. Experiments
Results: Load balancing experiment
Metric
Dynamic res. alloc. method
Client number threshold method
40 clients/server
50 clients/server
Under-allocation (avg.)
0.66%
0.86%
8.69%
Resource utilisation
83.3%
100%
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34. Setup: Cloud hosting experiment
4. Experiments
Setup: Cloud hosting experiment
Traces from the 2nd most popular MMOG, RuneScape
1M paying accounts
135M registered accounts since 2001
7M total playing (~6M free)
Input:
Trace composition:
7 countries, 3 continents
More than 130 game worlds
Consisting of:
Geographical location
Number of clients
Over 10,000 samples at 2 min. interval, 2 weeks
Cloud resource types:
Amazon EC2 standard small $0.085/hour
Flexiscale 2GB $0.159/hour
NewServers Medium $0.170/hour
Report by Geoff Iddison
Leipzig Games Convention
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35. Results: Cloud hosting experiment
4. Experiments
Results: Cloud hosting experiment
Load
Estimated yearly MMOG hosting costs [$]
Amazon EC2
Flexiscale
NewServers
Dynamic
Static 0%
101,266
189,426
202,531
20%
23,326
40,920
38,468
50%
57,345
100,404
97,495
60%
57,830
101,829
98,179
70%
66,299
116,458
114,775
80%
75,709
133,111
129,119
90%
84,007
147,055
142,578
95%
88,199
155,039
149,793
@ 50% average load: –47% yearly hosting expenses
@ 90% average load: –23% yearly hosting expenses
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36. OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions
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37. OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions
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38. Conclusions MMOG: application with >24 million user base
Dynamic hosting model
Investigated:
Load balancing techniques
Impact of using Cloud resources on hosting expenses
Current work:
Investigating a new business model for MMOG operation
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39. edutain@grid – http://edutaingrid.eu/ on-going research – FWF project
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