Network traffic analysis and evaluation of a multi-user virtual environment

Experimental study and modelling of Networked Virtual Environment server traffic

“…In the testing environment defined in this work, human players have been replaced by scripts and the player behaviour has been simplified to maximise the interaction rate and its associated network traffic. Although this simplification may seem not representative of the traffic obtained during real gaming sessions, the results from [8] suggest that the impact of lower rates of activity in OWL sessions translates into greater inter departure and arrival times and therefore more heavytailed distributions while keeping an analogous nature to those proposed in this study.…”

“…The TCP server traffic is mostly reactive to OWL request, without signs of periodicity nor bursty behaviour [8]. This supposes a difference in relation to other games analyses in the previous literature such as [6] and [3].…”

“…Literature has covered broader spectrum topics about NVEs: usage of NVEs as basis for persuasive systems ( [1]), integration of NVEs with low cost sensors ( [2]). On the other hand, [8] has focused on the network traffic aspects of OWL, proposing several models for the outgoing traffic of OWL clients. To the best of our knowledge there is no work that deals with server traffic modelling for NVEs like the one presented in the present paper.…”

“…In [8], it was determined that the inactivity periods of the so-called "inactive" clients translated into absence of traffic from client to server. The most basic forms of interaction that a player can perform in OWL are avatar movement and voice transmission.…”

Complex adaptive communication networks and environments: Part 1

“…Such phenomena thus require modeling communication networks by considering them instead as artificial Complex Adaptive Systems (CAS), or generalized collectively as Complex Adaptive COmmunicatiOn Networks and environmentS (CACOONS). Recent examples of the manifestation of ideas of complexity and emergence in CACOONS include cascading failures reported in the Amazon.com cloud 3 , effects of viral and worm infections in large networks 4 , emergence of cascading faults in message queue–based financial transactions after New Year’s Day 5 , network congestion and queue sizes 6 , effects of torrent and other complex traffic on company intranets 7 , multiplayer gaming 8 , multiagent systems 9 , self-organization and self-assembly in sensor 10 and robotic communication networks and complex power networks 11 .…”