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Vlad Nae, Radu Prodan, Thomas Fahringer Institute of Computer Science

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Presentation on theme: "Vlad Nae, Radu Prodan, Thomas Fahringer Institute of Computer Science"— Presentation transcript:

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: 11/16/2018

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) 11/16/2018

4 OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions 11/16/2018

5 OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions 11/16/2018

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 11/16/2018

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,..) 11/16/2018

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 11/16/2018

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% 11/16/2018

10 OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions 11/16/2018

11 OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions 11/16/2018

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 11/16/2018

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

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

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

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

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

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

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

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 11/16/2018

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 11/16/2018

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

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

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

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

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

29 OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions 11/16/2018

30 OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions 11/16/2018

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) 11/16/2018

32 Demonstrator application
4. Experiments Demonstrator application 11/16/2018

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% 11/16/2018

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 11/16/2018

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 11/16/2018

36 OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions 11/16/2018

37 OUTLINE Introduction Resource Provisioning Model Method Experiments
Conclusions 11/16/2018

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 11/16/2018

39 edutain@grid – http://edutaingrid.eu/ on-going research – FWF project
11/16/2018


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