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
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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. Deep networks are extremely hungry for data. They devour hundreds of thousands of labeled images to learn robust and semantically meaningful feature representations. Current networks are so data hungry that collecting labeled data has become as important as designing the networks themselves. Unfortunately, manual data collection is both expensive and time consuming. We present an alternative, and show how ground truth labels for many vision tasks are easily extracted from video games in real time as we play them. We interface the popular MicrosoftÂ® DirectXÂ® rendering API, and inject specialized rendering code into the game as it is running. This code produces ground truth labels for instance segmentation, semantic labeling, depth estimation, optical flow, intrinsic image decomposition, and instance tracking. Instead of labeling images, a researcher now simply plays video games all day long. Our method is general and works on a wide range of video games. We collected a dataset of 220k training images, and 60k test images across 3 video games, and evaluate state of the art optical flow, depth estimation and intrinsic image decomposition algorithms. Our video game data is visually closer to real world images, than other synthetic dataset. The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambiguities. To surmount these difficulties, we introduce a vision system that is capable of learning, detecting and tracking the objects of interest. The system is demonstrated in the context of tracking hockey players using video sequences. Our approach combines the strengths of two successful algorithms: mixture particle filters and Adaboost. The mixture particle filter [17] is ideally suited to multi-target tracking as it assigns a mixture component to each player. The crucial design issues in mixture particle filters are the choice of the proposal distribution and the treatment of objects leaving and entering the scene. Here, we construct the proposal distribution using a mixture model that incorporates information from the dynamic models of each player and the detection hypotheses generated by Adaboost. The learned Adaboost proposal distribution allows us to quickly detect players entering the scene, while the filtering process enables us to keep track of the individual players. The result of interleaving Adaboost with mixture particle filters is a simple, yet powerful and fully automatic multiple object tracking system. Programmers are writing a rapidly growing number of programs in object-oriented languages, such as Java and C#, that require garbage collection. Garbage collection traces and simulation speed up research by enabling deeper understandings of object lifetime behavior and quick exploration and design of new garbage collection algorithms. When generating perfect traces, the brute-force method of computing object lifetimes requires a whole-heap garbage collection at every potential collection point in the program. Because this process is prohibitively expensive, researchers often use granulated traces by collecting only periodically, for example, every 32 KB of allocation.We extend the state of the art for simulating garbage collection algorithms in two ways. First, we develop a systematic methodology for simulation studies of copying garbage collection and present results showing the effects of trace granularity on these simulations. We show that trace granularity often distorts simulated garbage collection results compared with perfect traces. Second, we present and measure the performance of a new algorithm called Merlin for computing object lifetimes. Merlin timestamps objects and later uses the timestamps of dead objects to reconstruct when they died. The Merlin algorithm piggybacks on garbage collections performed by the base system. Experimental results show that Merlin can generate traces over two orders of magnitude faster than the brute-force method which collects after every object allocation. We also use Merlin to produce visualizations of heap behavior that expose new object lifetime behaviors. We present a study of a mobile mixed reality game called Can You See Me Now? in which online players are chased through a virtual model of a city by ‘runners’ (professional performers equipped with GPS and WiFi technologies) who have to run through the actual city streets in order to catch the players. We present an ethnographic study of the game as it toured through two different cities and draws upon video recordings of online players, runners, technical support crew, and also on system logs of text communication. Our study reveals the diverse ways in which online players experienced the uncertainties inherent in GPS and WiFi, including being mostly unaware of them, but sometimes seeing them as problems, or treating the as a designed feature of the game, and even occasionally exploiting them within gameplay. In contrast, the runners and technical crew were fully aware of these uncertainties and continually battled against them through an ongoing and distributed process of orchestration. As a result, we encourage designers to deal with such uncertainties as a fundamental characteristic of location-based experiences rather than treating them as exceptions or bugs that might be ironed out in the future. We argue that designers should explicitly consider four potential states of being of a mobile participant: connected and tracked, connected but not tracked, tracked but not connected, and neither connected nor tracked. We then introduce five strategies that might be used to deal with uncertainty in these different states for different kinds of participant: remove it, hide it, manage it, reveal it, and exploit it. Finally, we present proposals for new orchestration interfaces that reveal the ‘seams’ in the underlying technical infrastructure by visualizing the recent performance of GPS and WiFi and predicting the likely future performance of GPS. Due to the popularization of location based services (LBSs), preserving user's location privacy has become a significant issue. In this paper, we assume that users are traveling by car and propose a location anonymization method which mainly aims at reducing traceability of user's movement trajectory. Since the movement patterns of car-driving users have some special characteristics due to various factors such as traffic rules, generating dummies in an artificially-synthesized manner is not practical. Therefore, in our method, we use real car trace data which were obtained from probe cars to generate dummies so that the generated dummies look like real users. In doing so, our method tries to find real traces which can cross with the user or another dummy at an intersection in order to reduce the user's traceability. Through simulations, we confirm that our method can reduce the traceability of the user's trajectory compared with some naive approaches. Ns-3 is a widely used as a the network simulator of choice by researchers. It contains many well tested and high quality models of network protocols. However, the application layer models of ns-3 are very simplistic, and do not capture all aspects of real life applications. As a result, there is often a huge gap between the results of real experiments and the corresponding simulations. This problem is particularly exacerbated for wireless simulations, where many networking phenomena like wireless channel contention crucially depend on the application traffic characteristics. One way to bridge the gap between experiments and simulations is to incorporate knowledge from network traces into simulations. To this end, our work builds a trace-based application layer simulator in ns-3. Given a network trace collected from a user, our TraceReplay application layer model automatically generates traffic that is faithful to the real application in the ns-3 simulator. TraceReplay infers and replays only application layer delays like user think times, lets the simulator control the lower layer phenomena. TraceReplay extracts application layer characteristics from a single trace, and replays this information across many users in simulation, by using suitable randomization. Our model is also generic enough to replay any application layer protocol. Validation of our simulation model shows that simulation results obtained using TraceReplay are significantly different from those using other models, and are closer to experimental observations. The property of locality in program behavior has been studied and modelled extensively because of its application to memory design, code optimization, multiprogramming etc. We propose a k order Markov chain based scheme to model the sequence of time intervals between successive references to the same address in memory during program execution. Each unique address in a program is modelled separately. To validate our model, which we call the Inter-Reference Gap (IRG) model, we show substantial improvements in three different areas where it is applied. (1) We improve upon the miss ratio for the Least Recently Used (LRU) memory replacement algorithm by up to 37 . (2) We achieve up to 22 space-time product improvement over the Working Set (WS) algorithm for dynamic memory management. (3) A new trace compression technique is proposed which compresses up to 2.5 with zero error in WS simulations and up to 3.7 error in the LRU simulations. All these results are obtained experimentally, via trace driven simulations over a wide range of cache traces, page reference traces, object traces and database traces. We consider in this chapter a class of two-player nonzero-sum stochastic games with incomplete information, which is inspired by recent applications of game theory in network security. We develop fully distributed reinforcement learning algorithms, which require for each player a minimal amount of information regarding the other player. At each time, each player can be in an active mode or in a sleep mode. If †This material is based upon work supported in part by the U.S. Air Force Office of Scientific Research (AFOSR) under grant number AFOSR MURI FA9550-09-1-0249. D R A F T October 2, 2011, 1:59am D R A F T 2 HYBRID LEARNING IN STOCHASTIC GAMES AND ITS APPLICATION IN NETWORK SECURITY a player is in an active mode, she updates her strategy and estimates of unknown quantities using a specific pure or hybrid learning pattern. The players’ intelligence and rationality are captured by the weighted linear combination of different learning patterns. We use stochastic approximation techniques to show that, under appropriate conditions, the pure or hybrid learning schemes with random updates can be studied using their deterministic ordinary differential equation (ODE) counterparts. Convergence to state-independent equilibria is analyzed for special classes of games, namely, games with two actions, and potential games. Results are applied to network security games between an intruder and an administrator, where the noncooperative behaviors are well characterized by the features of distributed hybrid learning. Abstract : In this paper, we analyze seven MapReduce workload traces from production clusters at Facebook and at Cloudera customers in e-commerce, telecommunications media, and retail. Cumulatively, these traces comprise over a year's worth of data logged from over 5000 machines, and contain over two million jobs that perform 1.6 exabytes of I O. Key observations include input data forms up to 77 of all bytes, 90 of jobs access KB to GB sized files that make up less than 16 of stored bytes, up to 60 of jobs re-access data that has been touched within the past 6 hours, peak-to-median job submission rates are 9:1 or greater, an average of 68 of all compute time is spent in map, task-seconds-per-byte is a key metric for balancing compute and data bandwidth task durations range from seconds to hours, and five out of seven workloads contain map-only jobs. We have also deployed a public workload repository with workload replay tools so that the researchers can systematically assess design priorities and compare performance across diverse MapReduce workloads. This paper presents an efficient approach for copies detection in a large videos archive consisting of several hundred of hours. The video content indexing method consists of extracting the dynamic behavior on the local description of interest points and further on the estimation of their trajectories along the video sequence. Analyzing the low-level description obtained allows to highlight trends of behaviors and then to assign a label of behavior to each local descriptor. Such an indexing approach has several interesting properties: it provides a rich, compact and generic description, while labels of behavior provide a high-level description of the video content. Here, we focus on video Content Based Copy Detection (CBCD). Copy detection is problematic as similarity search problem but with prominent differences. To be efficient, it requires a dedicated on-line retrieval method based on a specific voting function. This voting function must be robust to signal transformations and discriminating versus high similarities which are not copies. The method we propose in this paper is a dedicated on-line retrieval method based on a combination of the different dynamic contexts computed during the off-line indexing. A spatio-temporal registration based on the relevant combination of detected labels is then applied. This approach is evaluated using a huge video database of 300 hours with different video tests. The method is compared to a state-of-the art technique in the same conditions. We illustrate that taking labels into account in the specific voting process reduces false alarms significantly and drastically improves the precision. This paper proposes a new approach for making simulations realistic. This approach is based on the principle of "trace driven simulation", i.e. using the results of the actual traffic traces analysis in order to reproduce the same experimental conditions in simulation. The main principle of the approach proposed in this paper deals with making simulation traffic sources replay under certain conditions - the actual traffic traces grabbed on actual networks. This paper describes the implementation of this approach in the NS simulator, and evaluates it by comparing the characteristics of the traces obtained with our replay approach with original data traces. The parameters that are considered for making the comparison are the usual traffic parameters as throughput, packet rate, etc., but also everything that is related to traffic dynamics, i.e. the second order statistical moments as autocorrelation of traffic or long range dependence. In this work we present a measurement study of user mobility in Second Life. We first discuss different techniques to collect user traces and then focus on results obtained using a crawler that we built. Tempted by the question whether our methodology could provide similar results to those obtained in real-world experiments, we study the statistical distribution of user contacts and show that from a qualitative point of view user mobility in Second Life presents similar traits to those of real humans. We further push our analysis to line of sight networks that emerge from user interaction and show that they are highly clustered. Lastly, we focus on the spatial properties of user movements and observe that users in Second Life revolve around several point of interests traveling in general short distances. Besides our findings, the traces collected in this work can be very useful for trace-driven simulations of communication schemes in delay tolerant networks and their performance evaluation. This paper presents a method for automatically converting raw GPS traces from everyday vehicles into a routable road network. The method begins by smoothing raw GPS traces using a novel aggregation technique. This technique pulls together traces that belong on the same road in response to simulated potential energy wells created around each trace. After the traces are moved in response to the potential fields, they tend to coalesce into smooth paths. To help adjust the parameters of the constituent potential fields, we present a theoretical analysis of the behavior of our algorithm on a few different road configurations. With the resulting smooth traces, we apply a custom clustering algorithm to create a graph of nodes and edges representing the road network. We show how this network can be used to plan reasonable driving routes, much like consumer-oriented mapping Web sites. We demonstrate our algorithms using real GPS data collected on public roads, and we evaluate the effectiveness of our approach on public roads, and we evaluate the effectiveness of our approach by comparing the route planning results suggested by our generated graph to a commercial route planner. A number of text mining and information extraction projects such as Text Runner and NELL seek to automatically build knowledge bases from the rapidly growing amount of information on the web. In order to scale to the size of the web, these projects often employ ad hoc heuristics to reason about uncertain and contradictory information rather than reasoning jointly about all candidate facts. In this paper, we present a Markov logic-based system for cleaning an extracted knowledge base. This allows a scalable system such as NELL to take advantage of joint probabilistic inference, or, conversely, allows Markov logic to be applied to a web scale problem. Our system uses only the ontological constraints and confidence values of the original system, along with human-labeled data if available. The labeled data can be used to calibrate the confidence scores from the original system or learn the effectiveness of individual extraction patterns. To achieve scalability, we introduce a neighborhood grounding method that only instantiates the part of the network most relevant to the given query. This allows us to partition the knowledge cleaning task into tractable pieces that can be solved individually. In experiments on NELL's knowledge base, we evaluate several variants of our approach and find that they improve both F1 and area under the precision-recall curve. Search is the most heavily used web application in the world and is still growing at an extraordinary rate. Understanding the behaviors of web search engines, therefore, is becoming increasingly important to the design and deployment of data center systems hosting search engines. In this paper, we study three search query traces collected from real world web search engines in three different search service providers. The first part of our study is to uncover the patterns hidden in the query traces by analyzing the variations, frequencies, and locality of query requests. Our analysis reveals that, contradicted to some previous studies, real-world query traces do not follow well-defined probability models, such as Poisson distribution and log-normal distribution. The second part of our study is to deploy the real query traces and three synthetic traces generated using probability models proposed by other researchers on a Nutch based search engine. The measured performance data from the deployments further confirm that synthetic traces do not accurately reflect the real traces. We develop an evaluation tool that can collect performance metrics on-line with negligible overhead. The performance metrics include average response time, CPU utilization, Disk accesses, and cycles-per-instructions, etc. The third of our study is to compare the search engine with representative benchmarks, namely Gridmix, SPECweb2005, TPC-C, SPECCPU2006, and HPCC, with respect to basic architecture-level characteristics and performance metrics, such as instruction mix, processor pipeline stall breakdown, memory access latency, and disk accesses. The experimental results show that web search engines have a high percentage of load store instructions, but have good cache memory performance. We hope those results presented in this paper will enable system designers to gain insights on optimizing systems hosting search engines. In this paper we present an efficient algorithm for tracking multiple players during indoor sports matches. A sports match can be considered as a semi-controlled environment for which a set of closed-world assumptions regarding the visual as well as the dynamical properties of the players and the court can be derived. These assumptions are then used in the context of particle filtering to arrive at a computationally fast, closed-world, multi-player tracker. The proposed tracker is based on multiple, single-player trackers, which are combined using a closed-world assumption about the interactions among players. With regard to the visual properties, the robustness of the tracker is achieved by deriving a novel sports-domain-specific likelihood function and employing a novel background-elimination scheme. The restrictions on the player's dynamics are enforced by employing a novel form of local smoothing. This smoothing renders the tracking more robust and reduces the computational complexity of the tracker. We evaluated the proposed closed-world, multi-player tracker on a challenging data set. In comparison with several similar trackers that did not utilize all of the closed-world assumptions, the proposed tracker produced better estimates of position and prediction as well as reducing the number of failures.