Peer-to-Peer Support for Massively Multiplayer Games

Peer-to-Peer Support for Massively Multiplayer

Games

Bjorn Knutsson, Honghui Lu, Wei Xu, Bryan Hopkins

Presented by Mohammed Alam (Shahed)

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Outline

• Introduction

• Overview of MMG

• Peer-to-Peer Infrastructure

• Distributed Game Design

• Game on P2P overlay

• Experimental Results

• Future Work and Discussion

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Outline

• Introduction

• Overview of MMG






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Introduction

• Proposes use of P2P overlays to support Massively multiplayer games (MMG)

• Primary contribution of paper:– Architectural (P2P for MMG)– Evaluative

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Introduction

PASTRY(P2P overlay)

SCRIBE(Multicast support)

MMG GAME

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Introduction

• Players contribute memory, CPU cycles and bandwidth for shared game state

• Three potential problems:

– Performance– Availability– security

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Outline

• Introduction

• Overview of MMG






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Overview of MMG

• Thousands of players co-exist in same game world

• Most MMG’s are role playing games (RPG) or real-time strategy(RTS) or hybrids

• Examples: Everquest, Ultima online, Sims online

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Overview of MMG

• GAME STATESWorld made up of - immutable landscape information (terrain)- Characters controlled by players- Mutable objects (food, tools, weapons)- Non-player characters (NPCs) controlled

by automated algorithms

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Overview of MMG

• GAME STATES (contd..)

– Game world divided into connected regions

– Regions on different servers• Regions further divided to keep data in memory

small

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Overview of MMG

• EXISTING SYSTEM SUPPORT– Client-server architecture

• Server responsible for– Maintain & disseminate game state– Account management & authentication

• Scalability achieved by– Dedicated servers– Clustering servers

» LAN or computing grid

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Overview of MMG

• Latency– Varies– Guiding ‘avatars’ tolerates more latency– First person shooter games (180

millisecond latency max)– Real time strategy (several seconds)

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Outline

• Introduction

• Overview of MMG






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Peer-to-Peer Infrastructure

PASTRY(P2P overlay)

SCRIBE(Multicast support)

MMG GAME

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Outline

• Introduction

• Overview of MMG






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Distributed Game Design

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• Persistent user state is centralized– Example: payment information, character

• Allows central server to delegate bandwidth and process intensive game state to peers

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• Game design based on fact that:– Players have limited movement speed– Limited sensing capability– Hence data shows temporal and spatial

localities– Use Interest Management

• Limit amount of state player has access to

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• Players in same region form interest group

• State updates relevant to group disseminated only within group

• Player changes group when going from region to region

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• GAME STATE CONSISTENCY– Must be consistent among players in a region– Basic approach: employ coordinators to resolve

update conflicts– Split game state management into three classes to

handle update conflicts:• Player state• Object state• The Map

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• Player state– Single writer multiple reader– Player-player interaction effects only the 2

players involved– Position change is most common event

• Use best effort multicast to players in same region

• Use dead reckoning to handle loss or delay

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• Object state– Use coordinator-based mechanism for

shared objects– Each object assigned a coordinator– Coordinator resolves conflicting updates

and keeps current value

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Outline

• Introduction

• Overview of MMG






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Game on P2P overlay

• Map game states to players– Group players & objects by region– Map regions to peers using pastry Key– Each region is assigned ID– Live Node with closest ID becomes coordinator– Random Mapping reduces chance of coordinator

becoming member of region (reduces cheating)– Currently all objects in region coordinated by one

Node– Could assign coordinator for each object

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Game on P2P overlay

• Shared state replication– Lightweight primary- backup to handle failures– Failure detected using regular game events– Dynamically replicate coordinator when failure

detected– Keep at least one replica at all times– Uses property of P2P (route message with

key K to node ID, N , closest to K)

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Game on P2P overlay

• Shared state replication (contd..)– The replica kept at M which is the next

closest to message or object K– If new node added which is closer to

message K than coordinator• Forwards to coordinator• Updates itself• Takes over as coordinator

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Game on P2P overlay

• Catastrophic failure– Both coordinator and replica dead

– Problem solved by cached information from nodes interested in area

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Outline

• Introduction

• Overview of MMG






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Experimental Results

• Prototype Implementation of “SimMud”• Used FreePastry (open source)• Maximum simulation size constrained

by memory to 4000 virtual nodes• Players eat and fight every 20 seconds• Remain in a region for 40 seconds• Position updates every 150 millisec by

multicast

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• Base Results– No players join or leave– 300 seconds of game play– Average 10 players per region– Link between nodes have random delay of

3-100 ms to simulate network delay

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Experimental Results(Base results)

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• 1000 to 4000 players with 100 to 400 regions

• Each node receives 50 –120 messages

• 70 update messages per second– 10 players * 7 position updates

• Unicast and multicast message take around 6 hops

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• Breakdown of type of messages– 99% messages are position updates– Region changes take most bandwidth– Message rate of object updates higher

than player-player updates• Object updates multicast to region• Object update sent to replica• Player player interaction effects only players

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• Effect of Population Growth– As long as average density remains same,

population growth does not make difference

• Effect of Population Density– Ran with 1000 players , 25 regions– Position updates increases linearly per node– Non – uniform player distribution hurts

performance

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• Three ways to deal with population density problem– Allow max number of players in region– Different regions have different size– System dynamically repartitions regions

with increasing players

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• Effect of message aggregation– Since updates are multicast, aggregate

them at root– Position update aggregated from all

players before transmit– Cuts bandwidth requirement by half– Nodes receive less messages

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• Effect of network dynamics– Nodes join and depart at regular intervals– Simulate one random node join and depart

per second– Per-node failure rate of 0.06 per minute– Average session length of 16.7 minutes

(close to 18 minutes for half life)– Average message rate increased from 24.12

to 24.52– Catastrophic failure every 20 hours

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Outline

• Introduction

• Overview of MMG






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Future Work

• Assumes uniform latency for now

• Testing games with more states and on global distributed network platforms

• Stop cheating by detection

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Discussion

• Assigning random coordinators could hurt in P2P (modem vs high-speed)

• How close can the results obtained in simulation on one machine work in real

• Given range of 7.2kB/sec – 22.34 KB/sec in easy game. What about games with more states

• How would aggregating messages be bad?– In their case waits for all messages to come

before sending? Latency issues?

Documents

Peer-to-Peer Support for Massively Multiplayer Games