Analyzing the effect of tcp and server population on massively multiplayer games

Many Massively Multiplayer Online Role-Playing Games (MMORPGs) use TCP flows for communication between the server and the game clients. The utilization of TCP, which was not initially designed for (soft) real-time services, has many implications for the competing traffic flows. In this paper we present a series of studies which explore the competition between MMORPG and other traffic flows. For that aim, we first extend a source-based traffic model, based on player’s activities during the day, to also incorporate the impact of the number of players sharing a server (server population) on network traffic. Based on real traffic traces, we statistically model the influence of the variation of the server’s player population on the network traffic, depending on the action categories (i.e., types of in-game player behaviour). Using the developed traffic model we prove that while server population only modifies specific action categories, this effect is significant enough to be observed on the overall traffic. We find that TCP Vegas is a good option for competing flows in order not to throttle the MMORPG flows and that TCP SACK is more respectful with game flows than other TCP variants, namely, Tahoe, Reno, and New Reno. Other tests show that MMORPG flows do not significantly reduce their sending window size when competing against UDP flows. Additionally, we study the effect of RTT unfairness between MMORPG flows, showing that it is less important than in the case of network-limited TCP flows

Analyzing the Effect of TCP and Server Population on Massively Multiplayer Games

Many Massively Multiplayer Online Role-Playing Games (MMORPGs) use TCP flows for communication between the server and the game clients. The utilization of TCP, which was not initially designed for (soft) real-time services, has many implications for the competing traffic flows. In this paper we present a series of studies which explore the competition between MMORPG and other traffic flows. For that aim, we first extend a source-based traffic model, based on player’s activities during the day, to also incorporate the impact of the number of players sharing a server (server population) on network traffic. Based on real traffic traces, we statistically model the influence of the variation of the server’s player population on the network traffic, depending on the action categories (i.e., types of in-game player behaviour). Using the developed traffic model we prove that while server population only modifies specific action categories, this effect is significant enough to be observed on the overall traffic. We find that TCP Vegas is a good option for competing flows in order not to throttle the MMORPG flows and that TCP SACK is more respectful with game flows than other TCP variants, namely, Tahoe, Reno, and New Reno. Other tests show that MMORPG flows do not significantly reduce their sending window size when competing against UDP flows. Additionally, we study the effect of RTT unfairness between MMORPG flows, showing that it is less important than in the case of network-limited TCP flows

On Money as a Means of Coordination between Network Packets

In this work, we apply a common economic tool, namely money, to coordinate network packets. In particular, we present a network economy, called PacketEconomy, where each flow is modeled as a population of rational network packets, and these packets can self-regulate their access to network resources by mutually trading their positions in router queues. Every packet of the economy has its price, and this price determines if and when the packet will agree to buy or sell a better position. We consider a corresponding Markov model of trade and show that there are Nash equilibria (NE) where queue positions and money are exchanged directly between the network packets. This simple approach, interestingly, delivers improvements even when fiat money is used. We present theoretical arguments and experimental results to support our claims

Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

Evaluation of the Impact of Packet Drops due to AQM over Capacity Limited Paths

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