We collected mobility traces of 84,208 avatars spanning 22 regions over two months in Second Life, a popular networked virtual environment. We analyzed the traces to characterize the dynamics of the avatars mobility and behavior, both temporally and spatially. We discuss the implications of the our findings to the design o f peer-to-peer networked virtual environments, interest management, mobility modeling of avatars, server load balancing and zone partitioning, client-side caching, and prefetching.
We now describe previous efforts in collecting avatar traces from networked virtual environments and games. @cite_13 collected a 5-hour trace of 400 players from an online game called FreeWar. @cite_12 collected a trace of 28 players from a game they developed called Orbius. The focus of their work is not on the trace, but rather, the trace is a way to evaluate their proposed algorithms. use their trace to evaluate a load balancing scheme, while use their trace to evaluate different interest management algorithms. Beside traces collected from games, both works use randomly generated movements in their evaluation, and both observe significant differences in their results evaluated using the traces and using generated movements. Their results highlight the importance of having real mobility traces for researchers to evaluate their work.
@cite_13 @cite_12
2146603609 2129879174 2083589344 2136433906
Network, Web, and disk I O traffic are usually bursty and self-similar and therefore cannot be modeled adequately with Poisson arrivals. However, we wish to model these types of traffic and generate realistic traces, because of obvious applications for disk scheduling, network management, and Web server design. Previous models (like fractional Brownian motion and FARIMA, etc.) tried to capture the 'burstiness'. However, the proposed models either require too many parameters to fit and or require prohibitively large (quadratic) time to generate large traces. We propose a simple, parsimonious method, the b-model, which solves both problems: it requires just one parameter, and can easily generate large traces. In addition, it has many more attractive properties: (a) with our proposed estimation algorithm, it requires just a single pass over the actual trace to estimate b. For example, a one-day-long disk trace in milliseconds contains about 86 Mb data points and requires about 3 minutes for model fitting and 5 minutes for generation. (b) The resulting synthetic traces are very realistic: our experiments on real disk and Web traces show that our synthetic traces match the real ones very well in terms of queuing behavior. In this paper, we present a semi-automatic approach for summarizing the content of large execution traces. Similar to text summarization, where abstracts can be extracted from large documents, the aim of trace summarization is to take an execution trace as input and return a summary of its main content as output. The resulting summary can then be converted into a UML sequence diagram and used by software engineers to understand the main behavioural aspects of the system. Our approach to trace summarization is based on the removal of implementation details such as utilities from execution traces. To achieve our goal, we have developed a metric based on fan-in and fan-out to rank the system components according to whether they implement key system concepts or they are mere implementation details. We applied our approach to a trace generated from an object-oriented system called Weka that initially contains 97413 method calls. We succeeded to extract a summary from this trace that contains 453 calls. According to the developers of the Weka system, the resulting summary is an adequate high-level representation of the main interactions of the traced scenario. This paper proposes the Networked Game Mobility Model (NGMM), for synthesising mobility in First-Person-Shooter (FPS) networked games. Current networked game research focuses on modelling low-level aspects, such as packet inter-arrival times and packet sizes, to optimise network traffic and efficient use of gaming servers. Due to the increasing popularity of multiplayer online games, the need has arisen to develop more realistic models. NGMM is such a model that utilises application level aspects of networked game traces to statistically model FPS games. It is believed that an understanding of the application level aspect (e.g. mobility and user actions) of the network is necessary to derive the causality of increasing workloads on the servers, particularly in response to increasing online game popularity. To evaluate the performance of the model simulations, comparisons are made between the original game traces, the Random Way Point Model and NGMM. Analyses of the comparative simulation results show that NGMM is capable of closely matching actual game traces. The incorporation of application level knowledge, performance boundaries of current optimisation techniques, including dead-reckoning and interest management, are also effectively ascertained in this research. This is particularly significant as current models are unable to evaluate their impact with optimisation techniques. Recent advances in network traffic capturing techniques have made it feasible to record full traffic traces, often for extended periods of time. Among the applications enabled by full traffic captures, being able to automatically reconstruct user-browser interactions from archived web traffic traces would be helpful in a number of scenarios, such as aiding the forensic analysis of network security incidents. Unfortunately, the modern web is becoming increasingly complex, serving highly dynamic pages that make heavy use of scripting languages, a variety of browser plugins, and asynchronous content requests. Consequently, the semantic gap between user-browser interactions and the network traces has grown significantly, making it challenging to analyze the web traffic produced by even a single user. In this paper, we propose ClickMiner, a novel system that aims to automatically reconstruct user-browser interactions from network traces. Through a user study involving 21 participants, we collected real user browsing traces to evaluate our approach. We show that, on average, ClickMiner can correctly reconstruct between 82 and 90 of user-browser interactions with false positives between 0.74 and 1.16 , and that it outperforms reconstruction algorithms based solely on referrer-based approaches. We also present a number of case studies that aim to demonstrate how ClickMiner can aid the forensic analysis of malware downloads triggered by social engineering attacks.