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Locality Aware Dynamic Load Management for Massively Multiplayer Games

This paper addresses the challenge of scheduling game regions across multiple servers in a massively parallel multiplayer game environment. It discusses the locality and load balancing issues, evaluates existing techniques, and proposes a new solution: Locality Aware Dynamic Load Management. This approach aims to balance server load while preserving region locality, thereby reducing inter-server communication and enhancing user satisfaction. Experimental results indicate that dynamic scheduling is superior to static methods in maintaining performance and user experience.

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Locality Aware Dynamic Load Management for Massively Multiplayer Games

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  1. Locality Aware Dynamic Load Management for Massively Multiplayer Games Jin Chen, Baohua Wu, Margaret Delap, Bjorn Knutson, Honghui Lu and Cristina Amza presented by Sagnik Nandy

  2. Basic Idea • How to schedule game regions across multiple servers in a massively parallel multiplayer game environment?

  3. Overview • Problem Description • Existing Techniques • Suggested Solution • Experimental Results • Conclusion

  4. Overview • Problem Description • Existing Techniques • Suggested Solution • Experimental Results • Conclusion

  5. Problem Description • How do you map various regions of a multiplayer game across different servers?

  6. Issue 1 - Locality 1 1

  7. Issue 1 - Locality 1 1

  8. Issue 2 – Load balancing 2 1 3 4 1 2 4 3

  9. Problem Statement • Balance server load by replicating existing game world partitions across several servers • Decrease inter-server communication by maintaining locality of adjacent regions

  10. Overview • Problem Description • Existing Techniques • Suggested Solution • Experimental Results • Conclusion

  11. Existing Solutions • Built-in load balancing in the game concept (e.g. countries, airports etc.) • Static Partitioning – row based, column based, cyclic, etc. • Dynamic Uniform Load Spread (Spread) • Tries to minimize the difference between most and least loaded nodes • Doesn’t consider locality

  12. Existing Solutions (contd.) • Dynamic Load Shedding to Lightest Loaded Node (Lightest) • Choose loaded server and shed load to system-wide lightest loaded node • Locality is not an objective (but can get maintained)

  13. Suggested Solution (Locality Aware Dynamic Load Management) • SLA violation • 90% users exceed update interval • Overload threshold • load (# users) for which violation happens • Safe load threshold • max load for which all users meet SLA • Light load • 2*safe_load – over_load

  14. Objectives • Meet SLA (= load balancing) • Happy users • Maintain locality of game regions • Reduce transition time • Minimize # of region migrations • Reduce inter-server communication

  15. Overview • Problem Description • Existing Techniques • Suggested Solution • Experimental Results • Conclusion

  16. Suggested Approach • Load shedding algorithm • How to distributed load and meet SLA requirements • Load aggregation algorithm • Help restore locality • Help in future load shedding

  17. Load Shedding Algorithm • If load > over_load • While load > over_load • Find lightest (neighbor < safety_load) and shed load • If no neighbor exists then do this globally across system

  18. Shed Load • How to choose a component to shed? • Given a neighbor Sj • Choose a boundary node for Sj • With node as root • Find strongly connected cluster using BFS as long cluster weight within bounds

  19. Load Aggregation • Reasons • Load can be shed to remote server • Load can be shed across multiple neighbors • Tries to reduce number of boundaries • For each neighbor of Si • Find partition such that new_load < safe_load • Transfer cluster if boundaries reduce

  20. Overview • Problem Description • Existing Techniques • Suggested Solution • Experimental Results • Conclusion

  21. Experiments • First did single server and a smaller cluster based experiment • Used results to simulate more comprehensive system • Simulated for CPU and network usage • Simulated for a LAN and WAN setting

  22. Real Experiments (single server)

  23. Real Experiments (multiple server)

  24. Simulation results (LAN)

  25. Simulation results (WAN)

  26. Conclusions • The paper introduces the issue of locality into scheduling • Dynamic scheduling is better than static scheduling • Locality is more important as the network spreads out (curious to know effect on Internet scale games) • Aggregation didn’t help much

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